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"post_date": "2023-09-05 14:09:47",
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"post_content": "Computer vision is a field of artificial intelligence that enables computers to understand and interpret visual information, just like humans do. By using complex algorithms and techniques, computer vision allows machines to analyze and interpret images or videos -- recognizing objects, detecting and tracking movements, and even estimating depth and dimensions.\r\n\r\nComputer vision has become an essential technology in various applications such as self-driving cars, surveillance systems, medical imaging, and even social media filters.\r\n<h3>What is computer vision<\/h3>\r\nThe origins of computer vision can be traced back to the 1950s when researchers first started exploring ways to mimic human vision using computational techniques.\r\n\r\nAs technology advanced, so did the capabilities of computer vision systems. The introduction of more powerful hardware, such as GPUs, allowed for faster and more efficient processing of visual data. This, coupled with the development of sophisticated algorithms and machine learning techniques, enabled computer vision systems to tackle more complex tasks.\r\n\r\nOne of the most significant advancements in recent years has been deep learning. Deep learning is a specialized branch of machine learning that focuses on using artificial neural networks to automatically learn from vast amounts of data and uncover intricate patterns within it. It is critical in computer vision techniques involving pattern recognition, classification, regression, and other complex data analysis tasks.\r\n\r\nLet\u2019s take a closer look at how computer vision works and these techniques.\r\n<h3>How computer vision works<\/h3>\r\nComputer vision systems rely on a combination of hardware and algorithms to process visual data. By combining these steps, computer vision algorithms can detect objects, extract relevant features, and make sense of the visual information.\r\n<ol>\r\n \t<li style=\"list-style-type: none;\">\r\n<ol>\r\n \t<li><b><span data-contrast=\"auto\">Image acquisition: <\/span><\/b><span data-contrast=\"auto\">The process begins with capturing an image or video using cameras or other imaging devices. The quality and resolution of the images acquired influence the accuracy of subsequent computer vision tasks.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Preprocessing: <\/span><\/b><span data-contrast=\"auto\">Once the data is captured, it undergoes preprocessing to clean up the images and adjusts them to make them easier to work with. This might involve removing noise, adjusting colors, and resizing images.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Feature extraction: <\/span><\/b><span data-contrast=\"auto\">Lastly, the computer vision system identifies and extracts important parts of the images, for example color, texture, shape, edges, corners, or any other characteristic that helps the computer understand what is in the images.<\/span><\/li>\r\n<\/ol>\r\n<\/li>\r\n<\/ol>\r\nLet's break down a few common scenarios that happen after feature extraction.\r\n\r\nThe next steps typically involve using those extracted features to perform specific tasks, such as object recognition, classification, segmentation, or any other analysis you might be interested in.\r\n\r\n<img class=\"wp-image-6587 size-full aligncenter\" src=\"\/wp-content\/uploads\/2023\/08\/living-room-object-detection.jpg\" alt=\"Ice Cream Cone Quality Control\" \/>\r\n<h3>Types of computer vision techniques<\/h3>\r\n<strong>Action recognition<\/strong>: Identifies when a person is performing a given action (e.g., running, sleeping, falling, etc.).\r\n\r\n<strong>Image classification<\/strong>: Categorizes images into predefined classes or categories. The goal is to train a model to recognize and assign a label to an input image based on the features and patterns present in the image.\r\n\r\n<strong>Image recognition<\/strong>: Identifies the most important high-level contents of an image. For example, given an image of a soccer game, a computer vision model trained for image recognition might return simply \u201csoccer game.\u201d\r\n\r\n<strong>Image segmentation:<\/strong> Isolates the areas of interest, for example it can separate the foreground (objects of interest) from the background and assigns a category to each pixel in the image, grouping them together into objects, people, backgrounds, etc.\r\n\r\n<strong>Object tracking<\/strong>: Estimates the motion of objects between consecutive frames.\r\n\r\n<strong>Machine learning and neural networks<\/strong>: Extracted features often serve as input for machine learning models or deep neural networks. These models learn from the features to make predictions or decisions based on the data they've been trained on.\r\n<h3>Business impact of computer vision and challenges<\/h3>\r\nComputer vision technology is driving innovation across many industries and use cases and is creating unprecedented business applications and opportunities. It\u2019s being used across all industries to address a broad and growing range of business applications. These include physical security, <a href=\"https:\/\/www.chooch.com\/blog\/8-examples-of-retail-automation-to-future-proof-your-business\/\">retail<\/a>, automotive, robotics, <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-in-healthcare\/\">healthcare<\/a>, <a href=\"https:\/\/www.chooch.com\/blog\/top-5-ai-uses-cases-in-manufacturing\/\">manufacturing<\/a>, supply chain\/logistics, government, media and entertainment, and Internet of Things (IoT).\r\n\r\n<strong>2 major computer vision concerns<\/strong>\r\n\r\nAs tools and services continue to drive down costs and improve performance and confidence in computer vision systems, there continues to be concerns around ethics and the lack of explainability of sophisticated approaches.\r\n\r\nConcerns surrounding privacy and data security continue to be paramount.\r\n\r\n<strong>Privacy\r\n<\/strong>The ability to capture, analyze, and store substantial amounts of visual data raises questions about who has access to this information and how it is used. Striking the right balance between the benefits of computer vision and protecting individual privacy is a critical consideration in moving forward.\r\n\r\n<strong>Bias\r\n<\/strong>Computer vision algorithms learn from data, and if the training data is biased, it can lead to biased outcomes. For example, facial recognition algorithms trained on predominantly male faces may struggle to correctly identify female faces. Addressing bias in computer vision algorithms is essential to avoid perpetuating existing societal biases and ensuring fair and ethical use of computer vision technology.\r\n<h3>What does the future hold for computer vision<\/h3>\r\nWith the continuous advancements in technology and the increasing availability of large datasets, the future of computer vision looks promising. As computer vision systems become more sophisticated and capable, they have the potential to revolutionize various industries and reshape the way we interact with machines.\r\n\r\nGartner predicts based on current trends and projections; computer vision will grow as a popular application for edge deployments \u2013 Edge Computer Vision.\r\n<p style=\"padding-left: 40px;\"><em>\u201cBy 2025, Gartner expects computer vision implementations leveraging edge architectures to increase to 60%, up from 20% in 2022.\"<\/em>\r\nEmerging Technologies: Computer Vision Is Advancing to Be Smarter, More Actionable and on the Edge, Gartner July 2022<\/p>\r\n\r\n<h3>What is Chooch computer vision<\/h3>\r\n<strong>Radically improved computer vision \u2014 Chooch AI Vision <\/strong>\r\n\r\nChooch has radically improved computer vision with AI. Chooch\u2019s AI Vision combines the power of computer vision with language understanding to deliver more innovative solutions.\r\n\r\nChooch\u2019s AI Vision solutions can process and understand information from multiple types of data sources, such as videos, images, text, and deliver more granular details by recognizing subtle nuances that may not be visible to humans.\r\n\r\nChooch\u2019s AI Vision detects patterns, objects, and actions in video images, gathering insights in seconds and can send real-time alerts to people or business intelligence systems to initiate further action in a fraction of the time a human would even notice there might be an issue.\r\n\r\nBusinesses are using Chooch to build innovative solutions to drive process improvements and improve operations such as <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail analytics<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a> quality assurance, <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety<\/a>, loss prevention, infrastructure management, and more.\r\n\r\nWhether in the cloud, on premise, or at the edge, Chooch is helping businesses deploy computer vision faster to improve investment time to value.\r\n\r\nIf you are interested in learning how Chooch AI Vision can help you, see how it works and <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">request a demo<\/a> today.",
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"post_content": "<span data-contrast=\"none\">Have you ever found yourself needing to create a caption for a picture before sharing it? Or wanted to ensure you had the latest version of your company's logo? Maybe you've wished to quickly grasp the theme of a document or even asked for an image to be described in another language. What if you could use text prompts to converse with both images and text documents?<\/span>\r\n\r\n<strong>Welcome to ImageChat \u2014 the forefront of generative AI technology.<\/strong>\r\n<h3><b><span data-contrast=\"none\">ImageChat \u2014 Next gen AI tool<\/span><\/b><\/h3>\r\n<span data-contrast=\"none\"><a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a> is an innovative multimodal model, merging computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/how-to-integrate-large-language-models-with-computer-vision\/\" target=\"_blank\" rel=\"noopener\">large language models<\/a> (LLMs) to analyze and understand information from various data sources like images and text. <\/span><span data-contrast=\"none\">Multimodal computer vision capitalizes on the strengths of each modality, for example images, video, document files, etc., to enhance the AI model's precision and robustness.<\/span>\r\n<h3><b><span data-contrast=\"none\">How ImageChat works<\/span><\/b><\/h3>\r\n<span data-contrast=\"none\">ImageChat generative AI technology uses <a href=\"https:\/\/www.grabon.in\/indulge\/ai-tools\/#prompt-generators\" target=\"_blank\" rel=\"noopener\">prompt engineering<\/a> for enabling users to engage with image and text files \u2014 pairing visual input with textual output. Custom text prompts allow users to query streams of visual and textual data to learn more about the contents.<\/span>\r\n\r\n<span data-contrast=\"none\">This versatile visual question and answer (VQA) tool handles a broad spectrum of questions, from factual queries about objects in an image like:<\/span>\r\n<p style=\"padding-left: 40px;\"><strong>\"What is the hex code for the red?\"<\/strong><\/p>\r\n<span data-contrast=\"none\"><strong><img class=\"aligncenter wp-image-7963 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/09\/blog-what-color-is-car.png\" alt=\"Detecting Hex Code Colors\" width=\"500\" height=\"473\" \/><\/strong><\/span>\r\n\r\n<span data-contrast=\"none\">Or for example, take a picture of a potential wildfire, users can ask create prompts to ask more complex questions requiring reasoning and contextual understanding like: complex inquiries demanding reasoning and contextual comprehension like:<\/span>\r\n<p style=\"padding-left: 40px;\"><strong>\"How do you know it isn't clouds?\"<\/strong><\/p>\r\n<img class=\"aligncenter wp-image-7964 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/09\/blog-imagechat-wildfire-detection-clouds.png\" alt=\"Detecting Clouds From Fire Smoke\" width=\"500\" height=\"473\" \/>\r\n\r\n<span data-contrast=\"none\">Fine-tuned text prompts enable users to refine queries and extract precise information from images. This feature streamlines the search for relevant content in only the areas of interest in the image. <\/span><span data-contrast=\"none\">ImageChat\u2019s advanced technology delivers more granular image details by recognizing subtle nuances where you need a set of human eyes.<\/span>\r\n<h3><b><span data-contrast=\"none\">ImageChat features<\/span><\/b><span data-ccp-props=\"{"335551550":0,"335551620":0}\">\u00a0<\/span><\/h3>\r\n<span data-contrast=\"none\">ImageChat-3, the latest release, introduces cutting-edge capabilities that redefine the boundaries of AI potential. These features mark a significant advancement in integrating vision and language capabilities.<\/span>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"none\">Diverse file support for 14 file types\r\n<\/span><\/b><span data-contrast=\"none\">ImageChat supports more than 14 file formats, including txt, .pdf, .doc, .ppt, .csv, .xls, .jpg, .png, and .webm. This wide-ranging support enables users to interact seamlessly with various content types, expanding ImageChat's versatility beyond images.<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"none\">Multilingual interaction in 50 languages\r\n<\/span><\/b><span data-contrast=\"none\">ImageChat bridges language gaps by supporting text prompts and responses in <strong>over 50 languages<\/strong>. This facilitates meaningful interactions with global audiences and empowers localized use cases, such as multilingual image captions.<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"none\">Tailored responses in desired tone\r\n<\/span><\/b><span data-contrast=\"none\">ImageChat transcends mere information delivery. It engages in conversations using <strong>prompted tones, styles, and directions<\/strong>, ensuring responses align with the desired tone and language style.<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"none\">YouTube video integration<\/span><\/b><span data-ccp-props=\"{"335551550":0,"335551620":0}\">\r\n<\/span><span data-contrast=\"none\">ImageChat introduces a new dimension of interaction with YouTube videos. Users can <strong>upload YouTube video links<\/strong> to explore deeper the video content, facilitating insights, discussions, and enhanced collaboration with multimedia.<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"none\">Unprecedented response accuracy\r\n<\/span><\/b><span data-contrast=\"none\">Trained on a massive dataset of over <strong>11 billion parameters<\/strong> and <strong>400 million images<\/strong>, ImageChat can <\/span><span data-contrast=\"none\">recognize more than <strong>40 million visual details<\/strong><\/span><span data-contrast=\"none\"> and excels in generating textual descriptions of diverse content types. Its unmatched scale ensures unparalleled accuracy and depth in understanding.<\/span><\/p>\r\n\r\n<h3><b><span data-contrast=\"none\">ImageChat business applications<\/span><\/b><\/h3>\r\n<span data-contrast=\"none\">Businesses harness ImageChat to automate scalable tasks. Industries across the spectrum integrating ImageChat into their existing technology platforms such as digital asset management, product information management, or leveraging ImageChat technology for customer service, <a href=\"https:\/\/www.chooch.com\/blog\/loss-prevention-retail-ai-can-make-dramatic-improvements-with-edge-ai\/\">loss prevention<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">EHS management<\/a>, and more.<\/span>\r\n\r\n<span data-contrast=\"none\">Using custom text prompts enables businesses to train ImageChat models to automate frequent questions, generate metadata, and detect actions resulting in efficient alerts and responses to detected people behavior. This rapid and accurate analysis minimizes human oversight and enhances decision-making.<\/span>\r\n<h3><b><span data-contrast=\"none\">ImageChat business benefits<\/span><\/b><\/h3>\r\n<span data-contrast=\"none\">ImageChat empowers businesses to proactively monitor video streams and detect incidents that occur in real time and initiate faster responses, enhancing efficiency. This rapid and accurate analysis minimizes human oversight and enhances decision-making. By automating repetitive tasks, businesses optimize data intelligence, improve operational efficiency, and scale data review without accruing additional costs. <\/span>ImageChat provides businesses the ability to proactively monitor video streams and detect incidents that occur in real time and initiate faster responses.\r\n<h3><b><span data-contrast=\"none\">The future of ImageChat generative AI<\/span><\/b><\/h3>\r\n<span data-contrast=\"auto\">Advanced capabilities that ImageChat offers provide organizations with the tools needed to apply advanced computer vision and language understanding to the broadest variety of use cases to solve a range of business challenges.\u202f <\/span><span data-contrast=\"none\">As ImageChat evolves, it will incorporate larger datasets and new features to further enhance its functionality.<\/span>\r\n\r\n<span data-contrast=\"none\">Discover the future of AI with ImageChat. <\/span><a href=\"https:\/\/www.chooch.com\/imagechat\/\"><span data-contrast=\"none\">Learn more<\/span><\/a> or <a href=\"https:\/\/app.chooch.ai\/app\/imagechat\/\" target=\"_blank\" rel=\"noopener\">try it yourself<\/a>. <span data-contrast=\"auto\">Explore its potential and get the free app in the <\/span><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.chooch.ic2&pli=1\"><span data-contrast=\"none\">Google Play<\/span><\/a><span data-contrast=\"auto\"> and <\/span><a href=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/08\/apple-app-store-badge-mobile.png\"><span data-contrast=\"none\">App Store<\/span><\/a><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{"335551550":0,"335551620":0}\">\u00a0<\/span>",
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"post_content": "<span data-contrast=\"auto\">Object detection, also known as object recognition, is a computer vision technique to identify and classify specific objects or patterns within an image or video. <\/span><span data-contrast=\"auto\">Object detection detects the presence of objects, recognizes what they are, and identifies their location in the image. <\/span><span data-contrast=\"auto\">Often times, object detection and image recognition\/classification are used synonymously. But while they are similar, they are very distinct computer vision tasks.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>What is an object detection model?<\/h3>\r\n<span data-contrast=\"auto\">An object detection model, also known as an object recognition algorithm, is a computational model designed to recognize and classify objects within images or videos. These models are trained on large datasets with labeled examples, where each example is associated with a specific object class or category.<\/span>\r\n\r\n<span data-contrast=\"auto\">Object detection models use a variety of techniques from computer vision, machine learning, and deep learning to learn the patterns and features that distinguish each specific object class or category.<\/span>\r\n\r\n<span data-contrast=\"auto\">For example, given a photograph of a city street, an object detection model would return a list of annotations or labels for all the different objects in the image: traffic lights, vehicles, road signs, buildings, etc. These labels would contain both the appropriate category for each object, such as \u201cperson\u201d and a \u201cbounding box,\u201d or rectangle in which the object is completely contained.<\/span>\r\n\r\n<img class=\"aligncenter wp-image-6588 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/object-detection-new-york-street.jpg\" alt=\"Object Detection on New York Street\" width=\"800\" height=\"450\" \/>\r\n\r\n<span data-contrast=\"auto\">Another example is given a photograph of a dog, an object recognition model returns a label and bounding box for the dog, as well as other prominent objects.<\/span>\r\n\r\n<img class=\"aligncenter wp-image-6589 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/object-recognition-dog.jpg\" alt=\"Object Recognition Dog on Street\" width=\"800\" height=\"450\" \/>\r\n<h3>The difference between object detection and image recognition models<\/h3>\r\n<span data-contrast=\"auto\">As mentioned earlier, while both <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">object detection and image recognition<\/a> are similar, an image recognition model categorizes the entire image with a single label. An object detection model identifies and classifies individual objects or patterns within an image or video.<\/span>\r\n\r\nFor example, an <strong>image recognition model<\/strong> would simply return \"cottage.\"\r\n\r\n<img class=\"aligncenter wp-image-6586 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/image-recognition-cottage.jpg\" alt=\"Image Recognition\" width=\"1308\" height=\"587\" \/>\r\n\r\nUsing the same example, an <strong>object detection model<\/strong> would return other prominent objects in the image, for example thatch, house, cottage.\r\n\r\n<img class=\"aligncenter wp-image-6585 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/object-detection-cottage.png\" alt=\"Object Detection of Cottage Scene\" width=\"1550\" height=\"588\" \/>\r\n<h3>6 Common object recognition techniques<\/h3>\r\n<span data-contrast=\"auto\">Let\u2019s explore the most common object recognition techniques used in computer vision. Often, they work together to provide a comprehensive understanding of objects within visual data. Once trained, the computer vision model can accurately recognize and classify objects based on their visual appearance and characteristics.<\/span>\r\n\r\n<span data-contrast=\"auto\">Depending upon the application, different combinations or variations of techniques are used based on training requirements.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<ol>\r\n \t<li><b><span data-contrast=\"auto\">Object detection: <\/span><\/b><span data-contrast=\"auto\">This technique locates and identifies multiple instances of objects within an image or video by drawing a bounding box around the objects of interest and providing a label or class for each bounding box.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Object segmentation: <\/span><\/b><span data-contrast=\"auto\">Object segmentation separates individual objects within an image or video by assigning a pixel-level mask to each object. Each pixel is assigned a value or color code based on the object or class it corresponds to. This segmentation provides a more detailed understanding of the object's boundaries and more precise object recognition.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Object tracking: <\/span><\/b><span data-contrast=\"auto\">Object tracking involves following the movement of a specific object across frames in a video sequence. The goal of object tracking is to maintain a consistent association between the object being tracked and its representation in subsequent frames, even as the object changes, such as appearance, scale, orientation, and occlusion. It is useful in applications like video surveillance and autonomous vehicles.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Object recognition and classification: <\/span><\/b><span data-contrast=\"auto\">This technique not only detects objects and understands their spatial location but also identifies and categorizes objects into predefined classes or categories. It involves training models to recognize specific objects based on their visual features and assigning them classes, such as cars, people, animals, or specific objects like chairs or cups.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Pose estimation:<\/span><\/b><span data-contrast=\"auto\"> Pose estimation infers the body joint positions and skeletal structure from images or videos. It estimates the pose of a person, including the positions and orientations of body parts, such as the head, shoulders, elbows, wrists, hips, knees, and ankles. Because it understands the pose or pose changes, it is useful in augmented reality, robotics, and human-computer interaction applications.<\/span><\/li>\r\n \t<li><b><span data-contrast=\"auto\">Instance segmentation: <\/span><\/b><span data-contrast=\"auto\">This combines object detection and semantic segmentation techniques. It detects the presence and location of an object and then segments each object separately by providing both bounding box coordinates and pixel-level masks for each individual object instance in an image or video.<\/span><\/li>\r\n<\/ol>\r\n<h3>Industry applications use cases for object detection<\/h3>\r\n<span data-contrast=\"auto\">Object detection is a key task for humans: when entering a new room or scene, our first instinct is to visually assess the objects and people it contains and then make sense of them.<\/span>\r\n\r\n<span data-contrast=\"auto\">Similar to humans, object detection plays a crucial role in enabling computers to understand and interact with the visual world. Object recognition is used in many use cases across industries including:<\/span>\r\n\r\n<img class=\"aligncenter wp-image-5498 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/ezgif.com-video-to-gif.gif\" alt=\"AI Vision for Workplace Safety\" width=\"600\" height=\"338\" \/>\r\n\r\n<b><span data-contrast=\"auto\">Workplace safety and security AI: <\/span><\/b><span data-contrast=\"auto\">Object detection models can help improve <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-ai-safety-technology-to-detect-workplace-hazards\/\" target=\"_blank\" rel=\"noopener\">workplace safety<\/a> and security. For example, they can detect the presence of suspicious individuals or vehicles in a sensitive area. More creatively, it can help ensure that workers are using personal protective equipment (PPE) such as gloves, helmets, or masks.\u00a0<\/span>\r\n\r\n<b><span data-contrast=\"auto\">Media: <\/span><\/b><span data-contrast=\"auto\">Object detection models can help recognize the presence of certain brands, products, logos, or people in digital media. Advertisers can then use this information to collect metadata and show more relevant ads to users. It also helps automate the process of detecting and flagging inappropriate or prohibited content, such as explicit images, violence, hate speech, or other forms of harmful or offensive material. Social media sites rely on this type of content moderation to protect their community members and the integrity of their site.<\/span>\r\n\r\n<img class=\"aligncenter wp-image-6587 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/ice-cream-cone-mfg-qa.png\" alt=\"Ice Cream Cone Quality Control\" width=\"600\" height=\"333\" \/>\r\n\r\n<b><span data-contrast=\"auto\">Manufacturing quality control:<\/span><\/b><span data-contrast=\"auto\"> Object detection models enable automation of visual data review. Computers and cameras can analyze data real-time and automatically detect and process visual information and understand its significance which reduces the need for manual intervention in tasks where constant visual reviews are required. This is particularly beneficial for manufacturing <a href=\"https:\/\/www.chooch.com\/blog\/how-to-use-ai-for-production-line-quality-assurance\/\">production quality control<\/a>. It not only enhances efficiency but also detects production anomalies that may go unnoticed by the human eye which prevent potential production disruption or product recalls.<\/span>\r\n<h3>The importance of object detection in computer vision<\/h3>\r\n<span data-contrast=\"auto\">Object detection techniques are crucial in computer vision. These algorithms enable machines to understand, interpret, and make decisions based on visual data.<\/span>\r\n\r\n<span data-contrast=\"auto\">If you\u2019re new to the field, our <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-definitions\/\">computer vision glossary<\/a> has dozens of definitions of computer vision terminology. See how Chooch's object detection works by creating a free <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\" target=\"_blank\" rel=\"noopener\">AI Vision Studio account<\/a>.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>",
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"post_date": "2023-07-17 22:33:45",
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"post_content": "Computer vision engineering is where things get interesting in the world of artificial intelligence and computers. It's all about making computers see and understand what's happening around them. Imagine teaching a computer to look at things just like we do!\r\n\r\nThat's exactly what computer vision software engineers, or CV engineers, do.\r\n\r\nThe job of a computer vision engineer is to make sure that computers can understand and analyze visuals better than a human could. They use deep\/machine learning techniques to develop software to handle huge amounts of data, which train computers to make smart decisions based on what they \"see\" in pictures and videos.\r\n\r\nLet's meet one of Chooch's outstanding CV software engineers.\r\n<h3>Tell us about yourself.<\/h3>\r\n<span data-contrast=\"auto\">My name is Shijin Mathiyeri, and I'm a Sr. software engineer at Chooch. I am an electronics and communication engineering graduate. Before joining Chooch, I worked at a few startups as a full-stack developer, primarily focusing on React and Django development.\u00a0\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">I live in Kerala, a southern state of India. Kerala is known for its breathtaking backwaters and serene houseboat experiences. It is also renowned for its rich cultural heritage; including vibrant performing art forms and being the birthplace of Ayurveda.\u00a0<\/span>\r\n<h3>What programming languages and technologies do you specialize in, and why did you choose to focus on those specific areas?<\/h3>\r\n<span data-contrast=\"auto\">I specialize in full-stack development, encompassing both back-end and front-end technologies. My expertise includes Python, Django, AWS, Node.js, and React.js. When I started in software development, <a href=\"https:\/\/developer.mozilla.org\/en-US\/docs\/Learn\/Server-side\/Django\/Introduction\" target=\"_blank\" rel=\"noopener\">Django<\/a> was the technology that I focused on. For those who may not be familiar, Django\/Python is an easy-to-use framework that enables the rapid development of small applications, covering various aspects of web development such as HTML, CSS, databases, backend logic, and APIs, among others.<\/span>\r\n<h3>How do you stay updated with the latest trends and advancements in computer vision and artificial intelligence?<\/h3>\r\n<span data-contrast=\"auto\">I like to read the sites below, and I follow companies and profiles on social media related to AI to get the latest news on advancements in the field.<\/span>\r\n\r\n<a href=\"https:\/\/www.technologyreview.com\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"auto\">https:\/\/www.technologyreview.com\/<\/span>\u00a0<\/a>\r\n<a href=\"https:\/\/venturebeat.com\/category\/ai\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"auto\">https:\/\/venturebeat.com\/category\/ai\/<\/span>\u00a0<\/a>\r\n<a href=\"https:\/\/www.artificialintelligence-news.com\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"auto\">https:\/\/www.artificialintelligence-news.com\/<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/a>\r\n<h3>Are there any specific methodologies or frameworks that you use in your work? Why do you find them effective?<\/h3>\r\n<span data-contrast=\"auto\">Working on a POC (proof of concept) helps us gain a better understanding of what we are building and provides us the flexibility to iterate and continuously improve and add new features as we go along. During the POC phase, we can validate our ideas, gather feedback, and make better informed decisions.<\/span>\r\n\r\n<span data-contrast=\"auto\">Kanban workflows are really important for keeping track of fast-changing requirements and short delivery times. The <a href=\"https:\/\/kanban.university\/kanban-guide\/\" target=\"_blank\" rel=\"noopener\">Kanban methodology<\/a> offers excellent flexibility in adapting to changing priorities and is not bound by fixed time frames. As a team, we are able to adjust our workflow based on real-time needs and ensure efficient task management and delivery.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>What advice would you give to someone starting their career as a software engineer who wants to focus on working at an AI company right now?<\/h3>\r\n<span data-contrast=\"auto\">When building any software solution, it is crucial to prioritize accuracy, reliability, and usability for users. Following industry best practices, rigorous testing, and quality assurance processes, you can be confident in the software solution you build.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">It\u2019s all about testing. To create an accurate solution, it is essential to leverage reliable data sources, employ robust algorithms, and continuously evaluate and improve the model's performance. Rigorous testing, including unit testing, integration testing, and end-to-end testing, helps identify and rectify any issues or inconsistencies in the system.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Lastly, I think it is very important to continually check in to make sure the solution you are building puts usability first and meets the needs and expectations of the users. This involves designing intuitive user interfaces, providing clear and understandable output, and offering user-friendly features and functionalities.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">By combining these principles with a comprehensive development and deployment process, you can build AI solutions that deliver value to users and meet their expectations. It\u2019s really exciting when this all comes together.<\/span>\r\n<h3>Are there any particular software engineering principles or best practices that you consider essential to your work?<\/h3>\r\n<span data-contrast=\"auto\">Test-driven development (TDD) is a software development approach where developers write tests before writing the actual code. It follows a cyclical process: write a failing test, write the minimum code to pass the test, and then refactor. TDD promotes code quality, early bug detection, and provides a safety net for future modifications, leading to more robust and maintainable software.<\/span>\r\n<h3>Can you talk about the importance of code quality and testing for a computer vision product?<\/h3>\r\n<span data-contrast=\"auto\">At Chooch, we strive to build the most powerful, cutting edge computer vision applications to deliver to the market. Code quality and testing play a vital role in helping us do this by:\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\"><strong>Reducing testing time:<\/strong> Investing in code quality and comprehensive testing practices can help identify and address issues early in the development cycle. This reduces the overall testing time required and enables us to iterate faster, accelerating our time-to-market.<\/span>\r\n\r\n<span data-contrast=\"auto\"><strong>Reducing after-release bugs:<\/strong> Thorough testing and quality-focused development help minimize the occurrence of bugs and issues after the product is released. This reduces the need for post-release patches and hot fixes, enhancing the product's reliability and customer satisfaction.<\/span>\r\n\r\n<span data-contrast=\"auto\"><strong>Enabling easy future code changes:<\/strong> Well-structured and well-tested code is easier to understand and modify in the future. This agility allows us to seamlessly incorporate of new features, bug fixes, and improvements, ultimately saving time and effort during future development cycles.<\/span>\r\n<h3>Are there any specific projects or initiatives that you're particularly proud of as a software engineer? What makes them stand out?<\/h3>\r\n<span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/imagechat\/\" target=\"_blank\" rel=\"noopener\">ImageChat<\/a> is an industry-first feature built by Chooch that allows users to engage in image-based conversations and effortlessly create custom chat models. At its core, it is a generative AI foundational model for image-to-text.<\/span>\r\n\r\n<span data-contrast=\"auto\">It combines computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/how-to-integrate-large-language-models-with-computer-vision\/\" target=\"_blank\" rel=\"noopener\">LLMs<\/a> for creating text prompts which allow you to narrow in on exactly what you want to know about the image. <\/span><span data-contrast=\"none\">By fine-tuning prompts, you can get very specific with your text queries to extract precise information which you may not have been able to see previously.<\/span>\r\n\r\n<span data-contrast=\"auto\">This groundbreaking functionality can be seamlessly integrated into our production-level application with minimal effort.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\"> I encourage you to try ImageChat. It\u2019s free to download on<a href=\"https:\/\/apps.apple.com\/us\/app\/chooch-ic2\/id1304120928\" target=\"_blank\" rel=\"noopener\">\u202fiOS<\/a>\u202for\u202f<a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.chooch.ic2&pli=1\" target=\"_blank\" rel=\"noopener\">Android<\/a>, and you can explore exactly what it is capable of.<\/span>\r\n<h3>Looking ahead, what do you see as the most exciting opportunities or challenges in the field of software engineering within the industry of AI and computer vision platforms?<\/h3>\r\n<span data-contrast=\"auto\">I believe that advancements in deep learning are going to expand the potential applications of AI and computer vision, especially those that will benefit society. As deep learning evolves, it will only continue to improve the accuracy and efficiency of the models being built, and that expands the endless possibilities for applications.<\/span>\r\n\r\n<span data-contrast=\"auto\">I think collaboration with fields like robotics and IoT provides interdisciplinary possibilities. Edge computing and AI with IoT devices is really exciting. It provides real-time data processing opportunities. Industries like <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a> are enabling edge devices with computer vision technology to gain real-time insights from video data to monitor production QA, detect <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety hazards<\/a>, and predict equipment maintenance.<\/span>\r\n\r\n<span data-contrast=\"none\">But as more data is gathered, it requires more management, and privacy and compliance with regulations continue to be concerns. However, it is an exciting time to be a software engineer. Technologies are continuing to evolve and with that brings real challenges, but this creates a great opportunity for software engineers to shape the future of AI and computer vision and really b<\/span><span data-contrast=\"auto\">ridge the gap between research and practical applications.<\/span>",
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"post_date": "2023-06-29 11:31:26",
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"post_content": "Unless you\u2019ve been living in a cave for the last year, you\u2019ve probably heard of generative AI tools such as <a href=\"https:\/\/www.gartner.com\/en\/articles\/your-7-biggest-chatgpt-questions-answered\" target=\"_blank\" rel=\"noopener\">ChatGPT<\/a> and Bard. Chances are, you\u2019ve tested some out. Generative AI is already fusing with our daily processes, after all; Microsoft is <a href=\"https:\/\/www.theverge.com\/2023\/5\/23\/23732454\/microsoft-ai-windows-11-copilot-build\" target=\"_blank\" rel=\"noopener\">embedding ChatGPT into its applications<\/a>, just as <a href=\"https:\/\/blog.google\/technology\/ai\/google-bard-updates-io-2023\/\" target=\"_blank\" rel=\"noopener\">Google is integrating Bard into G-suite tools<\/a>.\r\n\r\nSo far, generative AI tools have gotten the most attention for the ways they help people in everyday life, such as using text-based outputs to write software code, college application essays, or a clinical treatment plan. Some use visual output to design websites or produce 3D models for video games, while a <a href=\"https:\/\/www.theguardian.com\/music\/2023\/jun\/13\/ai-used-to-create-new-and-final-beatles-song-says-paul-mccartney\" target=\"_blank\" rel=\"noopener\">new Beatles song using an old clip of John Lennon\u2019s voice<\/a> uses audio-based output.\r\n\r\nBut generative AI is also improving existing technology \u2013 including computer vision. From generating fresh content to creating <a href=\"https:\/\/www.chooch.com\/blog\/training-computer-vision-ai-models-with-synthetic-data\/\">synthetic data<\/a>, generative AI is bringing a new sophistication to computer vision technology.\r\n\r\nToday we\u2019re looking at four improvements to computer vision that generative AI is making, and how Chooch\u2019s <a href=\"https:\/\/www.chooch.com\/platform\/\">AI Vision Platform<\/a> and new <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a> model is unleashing these benefits.\r\n<h2>From insight to innovation<\/h2>\r\nIf you\u2019re not familiar with how generative AI works, it draws on several techniques. Most of us are familiar with large language models (LLM), a branch of machine learning. These models are trained on massive data sets, including text, images, and sounds. Using prediction algorithms, they respond to human prompts; our feedback and reinforcement learning helps them refine their output.\r\n\r\nBut generative AI does much more than answer questions. Here are four ways it works with <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> to bring deeper insights and greater innovation to organizations.\r\n<h3>#1. Improved quality and accuracy<\/h3>\r\nOne of the primary computer vision use cases is to recognize and classify objects \u2013 from weapon detection to facial recognition to <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE checks<\/a>. Generative AI enhances this ability by removing noise and artifacts from imagery and video, increasing image resolution, and canceling background noise. The result: sharper imagery, faster object identification, and fewer false positives.\r\n\r\nThis can be critical for a security team using computer vision to detect weapons at a school or a factory <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">monitoring PPE compliance<\/a> to prevent shop floor accidents. It can also help clinicians use highly detailed medical imaging scans in ultrasound, x-ray, computed tomography (CT), or magnetic resonance imaging (MRI) to diagnose conditions, understand where to place a biopsy needle, or form a treatment plan.\r\n<h3>#2. The creation of realistic images<\/h3>\r\nBecause generative AI can create extremely realistic new images and videos, it can assist <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> by generating original 3D models of objects, machine components, buildings, medications, people, landscapes, and more. Users don\u2019t have to search for an actual image or footage that already exists; they can simply develop their own and extract more useful insights.\r\n\r\nFor an engineering company, this could take the form of designing innovative new products via simulation; federal organizations could develop smarter prevention and mitigation strategies for wildfires and other natural disasters by analyzing realistic footage and photos of simulated events to understand how they would unfold.\r\n<h3>#3. Synthetic data<\/h3>\r\nData is the lifeblood of computer vision, but data annotation has been a barrier to AI adoption. Generative AI overcomes this barrier by <a href=\"https:\/\/www.chooch.com\/blog\/training-computer-vision-ai-models-with-synthetic-data\/\">creating synthetic, automatically labelled, new data elements<\/a> that help train computer vision models how to see, learn, and predict.\r\n\r\nAlthough organizations have been reluctant to share sensitive data with third parties because of the security risk, privacy is no longer a concern as synthetic data can\u2019t be linked to a real person. This also addresses the ethics issue of bias in models; while teams have worried about bias filtering into models through the data they\u2019re trained on, synthetic data can eliminate any possibility of bias.\r\n<h3>#4. More comprehensive data resources<\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> models are trained on vast quantities of data \u2013 but some generative AI models tap into even bigger data stores, including data that\u2019s never been leveraged before. Combining computer vision AI and large language models for image-to-text provides the ability to gain more detailed insights into visuals.\r\n\r\nAnalysts can craft targeted prompts to obtain specific information based on what\u2019s most important to their business. For example, they can ask questions like \"what objects are present in this image?\" or \"where is the person located in the video?\"\u00a0 By fine-tuning text prompts, they can narrow down their text queries to extract precise information which they may not have been able to do before.\r\n\r\nMore actionable, higher quality data enhances the accuracy of computer vision tools and accelerates the benefits that they bring to organizations.\r\n<h3>Chooch is taking generative AI capabilities to the next level with ImageChat<sup>TM<\/sup><\/h3>\r\nChooch is one of the few companies globally that currently offers generative AI technology for image-to-text. Chooch recently released, <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>, a generative AI foundational model that combines computer vision and large language models (LLMs) for creating text prompts to gain more detailed insights into video stream visuals.\r\n\r\nImageChat is pre-trained on vast amounts of visual and language data combined with object detectors to generate localized, highly accurate detection of even the most subtle nuances in images with staggering accuracy. It can recognize over 40 million visual elements \u2013 offering a revolutionary way to build computer vision models using text prompts with image recognition.\r\n<h3>Computer Vision AI + Large Language Models = Chooch AI Vision<\/h3>\r\nWith image-to-text technology, Chooch\u2019s AI Vision platform goes beyond traditional computer vision algorithms to incorporate generative AI that can automate the process of extracting information from visual content to significantly reduce analyst review times and manual efforts, while creating actionable, higher quality data in real-time.\r\n\r\nEquipped with these dynamic and context-aware data insights, Chooch\u2019s <a href=\"https:\/\/www.chooch.com\/platform\/\">AI Vision platform<\/a> can solve a broader range of problems and challenges. Simply put, computer vision AI can now go to an unprecedented level of accuracy and intelligence.\r\n\r\nComputer vision has always been about the precise analysis of visual information. Generative AI helps translate imagery into more actionable, higher quality data in unprecedented new ways. How will you take advantage of this new era in computer vision?\r\n\r\nWe urge you to try <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a> for yourself. It\u2019s free to download on<a href=\"https:\/\/apps.apple.com\/us\/app\/chooch-ic2\/id1304120928\" target=\"_blank\" rel=\"noopener\">\u202fiOS<\/a>\u202for\u202f<a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.chooch.ic2&pli=1\" target=\"_blank\" rel=\"noopener\">Android<\/a> or <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">schedule a demo<\/a> for a guided tour that explores just exactly what Chooch\u2019s AI Vision platform and ImageChat are capable of.",
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"post_date": "2023-06-27 17:07:40",
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"post_content": "Automotive parts manufacturing is a complex and dynamic industry that requires strict adherence to safety procedures and government regulations. One of the key safety measures is the use of Personal Protective Equipment (PPE) such as safety glasses, helmets, and gloves to protect workers from injury and ensure their well-being. Unfortunately, enforcing PPE compliance among workers can be a daunting task, especially when human intervention alone is not enough. However, with the advent of computer vision AI, detecting PPE compliance has become easier and more efficient.\r\n\r\nLet\u2019s explore how <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision AI<\/a> can help improve safety in automotive parts <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a> by enforcing PPE compliance.\r\n<h3>Understanding PPE compliance in automotive parts manufacturing<\/h3>\r\nBefore we delve into the role of computer vision AI, let\u2019s first understand the importance of <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE compliance<\/a> in the industry.\r\n\r\n<strong>Importance of PPE in workplace safety\r\n<\/strong>Automotive parts manufacturing is a potentially hazardous industry due to the use of heavy machinery and power tools. Therefore, it is important to ensure that workers are always protected from injury. This is where PPE comes in. PPE, or Personal Protective Equipment, refers to the specialized clothing and equipment that workers wear to protect themselves from potential <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards in the workplace<\/a>. PPE is not just a regulatory requirement, but it is also essential for the well-being of workers.\r\n\r\nWearing PPE can significantly reduce the risk of injury and illness in the workplace. It can protect workers from a range of hazards, such as chemical splashes, electrical shocks, and physical injuries. By providing workers with the appropriate PPE, employers can create a safe and healthy work environment, which can lead to increased productivity and job satisfaction.\r\n\r\n<strong>Common PPE requirements and standards\r\n<\/strong>There are different types of PPE that are required in <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">automotive parts manufacturing<\/a>, depending on the job being performed and the hazard that is present. For example, workers may need to wear safety glasses when working with machines that could produce flying debris, or gloves when working with sharp objects. It is important to follow the required PPE standards to ensure the safety of workers.\r\n\r\nThe Occupational Safety and Health Administration (OSHA) has set guidelines for PPE use in the workplace. These guidelines outline the types of PPE that are required for specific jobs and hazards, as well as the proper use and maintenance of PPE. Employers are responsible for providing workers with the appropriate PPE and ensuring that it is used correctly.\r\n\r\nIn addition to OSHA standards, there are also industry-specific PPE requirements. For example, the National Institute for Occupational Safety and Health (NIOSH) has developed PPE guidelines for workers in the automotive industry. These guidelines address the specific hazards that are present in automotive parts manufacturing, such as exposure to chemicals and noise.\r\n\r\nIt is important for workers to be trained in the proper use and maintenance of PPE. This includes knowing when to wear PPE, how to properly put it on and take it off, and how to inspect and maintain it. By following these guidelines, workers can effectively protect themselves from <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace hazards<\/a>.\r\n<h3>What is computer vision?<\/h3>\r\nNow that we have a better understanding of the importance of PPE compliance, let\u2019s explore how computer vision AI can help improve safety in the industry.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision AI<\/a> technology that enables machines to interpret, analyze, and understand visual data from the real world. This technology is used to train machines to recognize patterns and objects from images or videos, and then make decisions based on that data.\r\n\r\nIt has become increasingly popular in recent years due to its ability to automate tasks that were previously performed by humans. It has numerous applications across industries, including <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a>, and manufacturing.\r\n\r\nIn the context of <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">automotive parts manufacturing<\/a>, computer vision AI can be used to detect <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-for-ppe-compliance-at-industrial-facilities\/\">PPE compliance among workers<\/a>. This technology can analyze video footage from the factory floor and identify workers who are not wearing the required safety equipment, such as hard hats and safety glasses.\r\n<h3>Implementing computer vision for PPE compliance<\/h3>\r\nNow that we understand what computer vision is and why it is important, let\u2019s explore how it can be implemented for PPE compliance in automotive parts manufacturing.\r\n\r\n<strong>Setting up the computer vision system\r\n<\/strong>The first step in implementing a computer vision system for <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE compliance<\/a> is to set up the necessary hardware and software. This may involve installing cameras at different locations in the facility, and then connecting those cameras to a computer or cloud-based platform for processing the data. Also known as edge computing, this enables AI inferencing to occur closer to the data source, resulting in real-time responsiveness and reliable connectivity.\r\n\r\nChooch makes it easy for its customers to use their existing cameras to run AI models, reducing added infrastructure costs, while increasing the speed of deploying AI.\r\n\r\n<strong>Training the AI model for PPE detection\r\n<\/strong>Once the system is set up, the next step is to train the AI model to detect PPE compliance. This involves feeding the system with hundreds or thousands of images or videos of workers wearing PPE and not wearing PPE, and then letting the AI learn from those images. Often overlooked, the inference engine making predictions on this data plays a critical role in the success of computer vision. Edge deployment ensures that the AI algorithms can function with minimal delay and uninterrupted connectivity, enhancing overall performance and accuracy of the model.\r\n\r\nAt Chooch, we make it easy for customers to deploy AI with ReadyNowTM models specifically for common PPE use cases. Whether customers use <a href=\"https:\/\/app.chooch.ai\/app\/ready-now-models\/\" target=\"_blank\" rel=\"noopener\">ReadyNow models<\/a> or their own, we have invested significant efforts in optimizing and enhancing our inference engine to ensure real-time processing and analysis of visual data at the edge.\r\n\r\n<strong>Integrating the computer vision systems into manufacturing operations\r\n<\/strong>After the computer vision system has been trained, it can be integrated into overall manufacturing operations. After detecting specific images and business-critical anomalies, the inference engine is capable of immediately comprehending their significance and instantly putting in motion pre-programed responses to them. This may involve setting up alerts that notify managers when workers are not wearing the required PPE or alerting employees that they are not in compliance.\r\n\r\nFor Chooch, we have optimized our <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision platform<\/a> to do these things in a fraction of the time a human being could even notice there might be an issue.\r\n<h3><img class=\"wp-image-5872 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/ppe-detection-gloves.jpg\" alt=\"PPE Detection of Gloves\" width=\"473\" height=\"295\" \/>Benefits of using computer vision AI for PPE compliance<\/h3>\r\nThere are various benefits of using <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision AI<\/a> for PPE compliance in <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">automotive parts manufacturing<\/a>.\r\n\r\n<strong>Improved safety and reduced accidents\r\n<\/strong>By ensuring that workers are always wearing the required PPE, computer vision AI can significantly improve <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safety in the workplace<\/a> and reduce the number of accidents.\r\n\r\n<strong>Real-time monitoring and reporting\r\n<\/strong>Computer vision AI can provide real-time monitoring of <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE compliance<\/a>, allowing managers to quickly respond to any non-compliance issues. It can also generate reports that provide insights into the compliance rates and areas that need improvement.\r\n\r\n<strong>Increased efficiency and cost savings\r\n<\/strong>By automating PPE compliance monitoring, computer vision AI can help increase efficiency and reduce costs associated with manual monitoring and intervention.\r\n<h3>Challenges and limitations of computer vision in detecting PPE compliance<\/h3>\r\nWhile computer vision AI offers various benefits for enforcing PPE compliance, there are also some challenges and limitations to consider.\r\n\r\n<strong>Ensuring accuracy and reliability\r\n<\/strong>The accuracy and reliability of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision AI systems<\/a> are dependent on the quality and quantity of the training data. Therefore, it is important to ensure that the data used for training is comprehensive and unbiased.\r\n\r\n<strong>Addressing privacy concerns\r\n<\/strong>Computer vision AI involves capturing and processing images and videos of people, which raises privacy concerns. It is important to ensure that all privacy regulations are followed, and that workers are aware of the monitoring that is taking place.\r\n\r\n<strong>Overcoming technical and logistical hurdles\r\n<\/strong>Implementing a computer vision AI system for PPE compliance can involve technical and logistical hurdles, such as connectivity issues and hardware\/software compatibility. It is important to ensure that these hurdles are overcome before implementing the system.\r\n<h3>Computer vision AI offers an innovative solution for monitoring and enforcing PPE compliance<\/h3>\r\nEnforcing PPE compliance in automotive parts manufacturing is crucial for the safety and well-being of workers. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision AI<\/a> offers an innovative solution for monitoring and enforcing PPE compliance, resulting in improved <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety<\/a>, increased efficiency, and reduced costs.\r\n\r\nWhile there are challenges and limitations to consider, the benefits of using computer vision AI for PPE compliance are undeniable. By implementing such a system, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">automotive parts manufacturing<\/a> companies can improve safety and compliance rates, while also demonstrating a commitment to their workers\u2019 well-being. Learn more about the Chooch <a href=\"https:\/\/www.chooch.com\/platform\/\">AI Vision platform<\/a>, and how it can benefit your safety processes.",
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"post_date": "2023-06-27 12:25:58",
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"post_content": "<span data-contrast=\"auto\">Imagine that you\u2019re going camping \u2013 and before your trip, you visit a <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">sporting goods retailer<\/a>. The sales associate doesn\u2019t seem that informed about the products or camping. After you locate the empty shelf where you hoped your ideal tent would be, you walk out empty handed. But at the next store, everything you need is in stock, you receive a discount as part of an in-store promotion, and the sales associate suggests useful products you actually need.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">As a consumer, you might chalk up the difference to better management. But as a retail leader, you\u2019ll recognize automation as the differentiator.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Retail automation unlocks smarter workflows across every step of the retail journey, from headquarters to warehouses to ecommerce sites and brick-and-mortar stores. By automating manual processes in inventory management, security, order fulfillment, and other areas, retailers enjoy lower shrinkage, stronger customer loyalty, higher profits, and other benefits.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>The future of retail<\/h3>\r\n<span data-contrast=\"auto\">While the pandemic gets a lot of credit for changing the way we shop, consumer expectations have been evolving for a while. The Harvard Business Review found <\/span><a href=\"https:\/\/hbr.org\/2017\/01\/a-study-of-46000-shoppers-shows-that-omnichannel-retailing-works\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">73% of consumers prefer shopping through multiple channels.<\/span><\/a><span data-contrast=\"auto\"> Retailers have responded offering more personalization and omnichannel services, such as curbside pickups, special delivery options, and in-store only offers. Some luxury retailers have added refreshments and private shopping time and fashion shows for in-store experiences; many big box and budget retailers have expanded online inventory and expedited shipping times to rival Amazon Prime offers. Some forward-thinking retailers are using virtual and augmented reality to provide tailored browsing and buying options.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">The key here is automation. A recent study showed <\/span><a href=\"https:\/\/www.businesswire.com\/news\/home\/20230321005330\/en\/Retailers-Plan-to-Automate-Up-to-70-of-Routine-Store-Tasks-By-2025\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">retail AI will increase ninefold by 2025,<\/span><\/a><span data-contrast=\"auto\"> with up to 70% of routine tasks at least partially automated by 2025. Those retailers who understand how to use technology, particularly automation and computer vision tools, are the most likely to dominate their market in the years to come.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>Future proofing through automation<\/h3>\r\n<span data-contrast=\"auto\">Here are a few ways retailers use automation to cater to your preferences while beating their competitors.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<ol>\r\n \t<li><b><span data-contrast=\"auto\">Predicting shopper needs.<\/span><\/b><span data-contrast=\"auto\"> Data from sales channels helps them recommend the right products to you, offer discount codes, and create personalized emails that provide an easier and more helpful experience \u2013 so you buy more without perceiving any upselling.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Warehouse automation<\/span><\/b><span data-contrast=\"auto\">. Retailers like <\/span><a href=\"https:\/\/www.retaildive.com\/news\/walmart-automated-stores-robots-e-commerce\/646885\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Walmart already use robots<\/span><\/a><span data-contrast=\"auto\"> to clean, select and pack inventory, scan barcodes, and optimize warehouse layouts.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Inventory management software<\/span><\/b><span data-contrast=\"auto\">. Retailers can accurately forecast demand, track stock, and track inventory across a mix of environments such as pop-up shops, e-commerce, brick and mortar stores, and partner channels.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Smarter marketing.<\/span><\/b><span data-contrast=\"auto\"> Data-driven insights unlock personalized campaigns, such as tailored messages on emailed receipts. If you abandon your online shopping cart after seeing the shipping costs, you may receive a special offer for free shipping.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Automated billing and payments.<\/span><\/b><span data-contrast=\"auto\"> Making customers wait for human-driven refunds or store credit card decisions can slow down transactions and lose shopper interest. Automating these processes can mean immediate payments, refunds, and credit decisions.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Fraud and theft control<\/span><\/b><span data-contrast=\"auto\">. Instead of relying on a security guard\u2019s eyes, monitoring tools can alert teams to suspicious and criminal behavior on the front end, while back-end tools can accurately spot signs of fraud and improve <a href=\"https:\/\/www.chooch.com\/blog\/loss-prevention-retail-ai-can-make-dramatic-improvements-with-edge-ai\/\">loss prevention<\/a> efforts.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Convenient customer experiences.<\/span><\/b> <a href=\"https:\/\/aibusiness.com\/computer-vision\/waicf-2022-how-computer-vision-deep-learning-power-amazon-go\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Amazon Go stores use computer vision<\/span><\/a><span data-contrast=\"auto\"> and machine learning technology to eliminate checkout lines. Shoppers grab the products they want and walk out \u2013 with their Amazon account charged automatically.<\/span><\/li>\r\n \t<li data-leveltext=\"%1.\" data-font=\"Arial\" data-listid=\"6\" data-list-defn-props=\"{"335552541":0,"335559684":-1,"335559685":1440,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Workforce optimization.<\/span><\/b><span data-contrast=\"auto\"> Because there\u2019s no need to focus on tedious manual work, staff can provide a human touch in resolving issues and helping consumers understand products.<\/span><\/li>\r\n<\/ol>\r\n<span data-contrast=\"auto\">All of this adds up to more informed decision-making, happier customers, improved efficiency, and lower operational costs. Human error is reduced; processes are streamlined. Profits are higher too. In fact, Walmart recently predicted that automation in stores and warehouses would <\/span><a href=\"https:\/\/finance.yahoo.com\/news\/walmart-says-automation-in-stores-warehouses-will-boost-sales-by-130-billion-over-5-years-210221278.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAABEb9C58gINeX5GMAIhRtDEf2-hSpCMAg-g4-DOY5EmMn5erBi6l4Mi_uQ0dJDdUL03i5pcgE-niiq8Tw6D3H8m2RU0094XarsHotd8X0ZEfVmbuIc5UfBsEHkIRQ7kdnWSzmJheKNMkWyQ_o4T2B_pUG5PhkXdAEe32DZH2oJQI\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">boost sales by $130 billion in just five years.<\/span><\/a><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>Computer vision in retail<\/h3>\r\n<span data-contrast=\"auto\">One reason <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a> automation platforms are so sophisticated these days is their <\/span><span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/blog\/artificial-intelligence-is-transforming-retail-shelf-management\/\">AI-powered visual monitoring<\/a> and analysis tools, such as image and pattern recognition and predictive analytics.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Returning to the sporting goods retailer in our earlier example, here are a few ways they might use Chooch\u2019s AI-powered computer vision:<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<ul>\r\n \t<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"\uf0b7","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">By monitoring everything from an employee\u2019s fall in the distribution center to a spill from a customer\u2019s latte in the store, teams can quickly respond to incidents and keep both staff and shoppers safe.<\/span><\/li>\r\n \t<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"\uf0b7","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">The retail staff no longer need to physically search for <a href=\"https:\/\/www.chooch.com\/blog\/artificial-intelligence-is-transforming-retail-shelf-management\/\">stock-outs<\/a> and re-order inventory; computer vision tools automatically take care of it, along with identifying the most advantageous product placements and store layouts.<\/span><\/li>\r\n \t<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"\uf0b7","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">The ecommerce team can improve image quality and placement on the website, resulting in better product presentation, faster searchability, and higher sales.<\/span><\/li>\r\n \t<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"\uf0b7","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Stock can be counted and inspected at every touchpoint. Theft is reduced while loading and unloading merchandise; defective products are collected before they make it to the shop floor.<\/span><\/li>\r\n \t<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"5\" data-list-defn-props=\"{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"\uf0b7","469777815":"hybridMultilevel"}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Shoppers can scan products with their phones to view other customer reviews and even video tutorials on using equipment. After viewing a lacrosse stick or cooler or golf balls, they receive content on their chosen sports and vacations \u2013 increasing the likelihood they\u2019ll complete their purchase.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\r\n<\/ul>\r\n<h3>Building the foundation for a stronger future for retail<\/h3>\r\n<span data-contrast=\"auto\">Retailers have worked hard for years now to improve their back-end operations while enhancing the front-end customer journey.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Chooch\u2019s AI-powered <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">computer vision solutions are helping retailers<\/a> gain the data insights they need to improve the shopper experience and drive revenue. Chooch makes it easy for retailers to integrate AI models onto their existing video streams to witness real-time shopper insights in seconds. From loss prevention to monitoring stock out to planogram design and safety and security detection, Chooch helps retailers deploy AI quickly and easily scale as their use cases grow.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Automation is key to bringing <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a> into a higher level of efficiency while reducing costs and attracting new customers \u2013 it\u2019s clear \u2013 computer vision is becoming the driving force behind it.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>",
"post_title": "8 Examples of Retail Automation to Future-Proof Your Business\u00a0",
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"post_date": "2023-06-26 23:19:22",
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"post_content": "<span data-contrast=\"auto\">Imagine effortlessly discovering the perfect pair of jeans using an image or making a purchase through a simple voice command. Or what about virtually trying on an entire wardrobe without ever stepping foot in a store? Better yet, imagine a <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail industry<\/a> that knows what you need before you do and a supply chain that runs with clockwork precision, efficiency, and sustainability. If this sounds like a glimpse into a far-off future, you might be surprised that this reality is closer than you think.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Welcome to the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">future of retail<\/a>, powered by Artificial Intelligence (AI). AI has grown from a futuristic concept to a business-critical technology, and the retail industry is at the forefront of this transformation. In 2022, AI in the retail market accounted for a whopping <\/span><a href=\"https:\/\/spd.group\/artificial-intelligence\/ai-for-retail\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">USD 8.41 billion, and by 2030<\/span><\/a><span data-contrast=\"auto\">, it is set to skyrocket to an astounding USD 45.74 billion, growing at a compound annual growth rate (CAGR) of 18.45%.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">AI is redefining the retail industry in unimaginable ways. From enhancing customer experiences to optimizing supply chain management, AI is making an indelible impact on every facet of retail. In this blog, we will embark on an exciting journey into the heart of this transformation, unveiling <\/span><b><span data-contrast=\"auto\">five major use cases<\/span><\/b><span data-contrast=\"auto\"> where AI is reshaping and revolutionizing the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail industry<\/a>. Buckle up and prepare to witness a retail revolution in action.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">1. <\/span><b><span data-contrast=\"none\">Visual search<\/span><\/b><\/h3>\r\n<span data-contrast=\"auto\">Visual search technology, powered by AI, allows customers to upload images and find similar products, is increasingly being adopted by retailers like Farfetch and tech giants like Google. Visual Search is growing rapidly, with over <\/span><a href=\"https:\/\/www.pymnts.com\/news\/retail\/2021\/visual-search-drives-new-online-sales-for-merchants\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">eight billion visual searches conducted monthly<\/span><\/a><span data-contrast=\"auto\">, particularly among younger consumers who shop with mobile devices.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">AI and machine vision has transformed the search engine experience, making it more natural and visual. They help extract meaningful information from digital images and videos, allowing for more <\/span><a href=\"https:\/\/aimagazine.com\/articles\/visual-search-engines-the-role-of-ai-and-machine-vision\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">precise search results and personalized recommendations<\/span><\/a><span data-contrast=\"auto\">. An example of this technology is Google's \u201cmultisearch\u201d functionality combines text and visual search through Google Lens<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<b><span data-contrast=\"auto\">Computer vision is powering visual search<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">The combination of <a href=\"https:\/\/www.chooch.com\/\">computer vision (CV)<\/a> and natural language processing (NLP) enhances visual search by overcoming the limitations of traditional keyword searches. This pairing can extract properties of an image or video and adds more descriptive metadata to them for improved search results. <\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">This combination of images and text enables users to find what they're looking for by describing it in natural language as well as using the image on its own as a method of search.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">2. Voice search<\/span><\/h3>\r\n<span data-contrast=\"auto\">Voice artificial intelligence (AI) is playing a pivotal role in e-commerce. Major brands offer voice search capabilities to allow customers to <\/span><a href=\"https:\/\/www.forbes.com\/sites\/forbestechcouncil\/2021\/08\/24\/harnessing-conversational-voice-ai-in-the-e-commerce-industry\/?sh=7c35e20e60ec\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">inquire about products and delivery statuses without typing<\/span><\/a><span data-contrast=\"auto\">. This technology is not only driven by tech giants but also by new players in the market, and its use is increasingly preferred by both younger demographics, and those who may have difficulty typing, such as older adults<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Voice AI offers numerous benefits. It is convenient and accessible, providing 24\/7 customer support. It streamlines various processes, like order tracking and payment procedures, enhancing the shopping experience. Moreover, conversational AI can boost sales significantly by engaging in personalized, human-like conversations and making data-informed product recommendations<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">However, there are challenges. The unpredictability of voice interactions, potential misinterpretation by voicebots due to speech recognition flaws, and the need to ensure customer data security and privacy are some of the obstacles that need to be addressed for the full potential of this technology to be realized<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Despite these challenges, conversational voice AI holds promise for retailers, offering potential cost savings, improved customer experience, and increased sales revenue. However, continually monitoring the latest developments in this rapidly evolving field is crucial<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">3. <\/span><b><span data-contrast=\"none\">Virtual fitting rooms<\/span><\/b><\/h3>\r\n<span data-contrast=\"auto\">Virtual fitting rooms have become more prominent in recent years, particularly during the COVID-19 pandemic. This technology utilizes augmented reality (AR) and virtual reality (VR) and has been adopted by major retailers. Companies like <\/span><a href=\"https:\/\/www.businessinsider.com\/retailers-like-macys-adidas-are-turning-to-virtual-fitting-rooms-2020-8\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Macy's, Adidas, and ASOS<\/span><\/a><span data-contrast=\"auto\"> have joined forces with technology firms to offer these capabilities to their customers.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Despite the growth, there's a challenge in educating consumers and maintaining their interest in the technology. Trying clothing via an avatar on a mobile phone has not been a common practice. Zeekit is among the companies aiming to mainstream virtual fitting rooms. They allow users to upload a full-body photo and try on clothing from various brands. They also report that their service has helped reduce return rates by 36%.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">The future of virtual try-ons is still being determined, with differing opinions on whether the trend will continue post-pandemic. Some experts believe younger generations will continue using virtual fitting rooms, while others emphasize the need for retailers to optimize the technology to retain customers after the pandemic.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">4. <\/span><b><span data-contrast=\"none\">Customer behavior prediction<\/span><\/b><\/h3>\r\n<span data-contrast=\"auto\">Artificial intelligence (AI) platforms significantly influence the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail industry<\/a> by curating tailored <\/span><a href=\"https:\/\/www.forbes.com\/sites\/garydrenik\/2023\/06\/14\/ai-and-retail-consumer-adoption-on-the-rise-yet-uncertainty-looms\/?sh=1876551972ee\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">promotions, deals, and product purchase reminders<\/span><\/a><span data-contrast=\"auto\"> based on customer behavior. Most consumers trust AI to enhance their shopping experiences, and many are comfortable using it despite a general lack of deep understanding of the technology<\/span><span data-contrast=\"auto\">.<\/span>\r\n\r\n<span data-contrast=\"auto\">However, concerns about data privacy and trust persist. Many consumers hesitate to share personal information and need more trust in online retailers. This necessitates a shift in strategy for retailers, who must provide value beyond just product availability and price<\/span><span data-contrast=\"auto\">.<\/span>\r\n\r\n<span data-contrast=\"auto\">Retailers who prioritize AI investments are better positioned to redefine customer loyalty. They're exploring areas like Generative AI to drive productivity in stores, thereby freeing up resources for more innovative applications of AI<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Moreover, retailers are learning from how <\/span><a href=\"https:\/\/www.forbes.com\/sites\/forbestechcouncil\/2023\/06\/16\/building-genuine-connections-the-role-of-technology-in-cultivating-fan-engagement\/?sh=154683801612\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">celebrities and creators engage with their fans<\/span><\/a><span data-contrast=\"auto\">. They're leveraging AI to personalize experiences based on fan preferences, behaviors, and interests and using gamification and interactive campaigns to foster a sense of community. However, challenges related to fake profiles, spam accounts, and balancing personal and professional boundaries need careful management. Transparent privacy policies and responsible data-handling practices are essential for cultivating trust and ensuring safe environments for customer engagement<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">5. <\/span><b><span data-contrast=\"none\">Retail supply chain<\/span><\/b><\/h3>\r\n<span data-contrast=\"auto\">Artificial Intelligence (AI) is significantly transforming the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail supply chain<\/a> and is projected to push the market to over <\/span><a href=\"https:\/\/www.businessdit.com\/ai-in-supply-chain\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">$20 billion by 2028<\/span><\/a><span data-contrast=\"auto\">. AI applications include autonomous transport, improving delivery route efficiency, enhancing loading processes, and managing warehouse supply and demand. This allows businesses to boost operations and make data-driven decisions.<\/span>\r\n\r\n<span data-contrast=\"auto\">AI helps plan capacity, forecast demand, and identify trends that can reduce costs and generate revenue, giving businesses a competitive edge. Future applications include predicting unexpected events that could disrupt supplies and improving decision-making and automation technologies\u200b.<\/span>\r\n\r\n<span data-contrast=\"auto\">AI also aids supply chain sustainability by reducing carbon footprints through efficient production and delivery processes. For example, it can suggest optimal product storage locations to reduce delivery distances and fuel usage. It can also facilitate coordination between organizations, enhancing supply chain efficiency\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><b><span data-contrast=\"auto\">Retail AI is a game-changer<\/span><\/b><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":0,"335559740":259}\">\u00a0<\/span><\/h3>\r\n<span data-contrast=\"auto\">From improving e-commerce product search and discovery with visual and voice searches to reducing the hassle of physical fitting rooms, <a href=\"https:\/\/www.chooch.com\/\">AI and computer vision<\/a> are redefining the customer's journey. They not only are improving predicting consumer behavior with uncanny precision but also driving operational efficiency throughout the supply chain.<\/span>\r\n\r\n<span data-contrast=\"auto\">However, the journey has its challenges. As we've seen, data privacy concerns, user adaptation, and technological nuances must be meticulously navigated. It is crucial for retailers to not only stay updated with the latest developments but also adopt responsible, transparent data privacy practices to gain consumer trust.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">As retailers adopt and implement these intelligent solutions, the shopping experience continues to become increasingly seamless, personalized, and efficient. The future of the retail industry is undeniably intertwined with AI, promising to transform it in ways we are only beginning to comprehend.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">So, the next time you're scrolling through your favorite online store, using voice commands to order groceries, or admiring an outfit on a virtual avatar, remember \u2013 you are part of the AI revolution in retail. Welcome to the future of shopping. Learn more about <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Chooch\u2019s AI Vision solutions for retailers<\/a>. <\/span>",
"post_title": "5 AI Use Cases Revolutionizing the Retail Industry\u00a0",
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"post_content": "<h2>Chooch is a leading provider of computer vision AI solutions that make cameras intelligent.<\/h2>\r\nChooch was founded by Turkish-American brothers, business focused, Emrah, and technology driven, Hakan G\u00fcltekin. Emrah, a serial entrepreneur, had spent 20 years building startups and businesses from an engineering consultancy, to real estate development, to commercial and social investment consulting. He saw the world was rapidly changing and not exactly in the right direction. Despite technological advancements, there continued to be a lack of efficiency, foresight, and transparency to help companies make the right decisions, and he had a strong desire to contribute to the next wave of technological innovation to solve this.\r\n\r\nChooch was founded at the intersection of the evolution of society and the advancement of artificial intelligence.\r\n<h3><img class=\"alignleft wp-image-5806 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/hakan-emrah-gultekin-chooch-cofounders.jpg\" alt=\"Hakan and Emrah Gultekin, Chooch Founders\" width=\"198\" height=\"531\" \/>Who is Chooch?<\/h3>\r\nFrom an early age, Hakan G\u00fcltekin immersed himself in computer programming. A decade prior to starting Chooch, Hakan developed an image analysis system for medical imagery that could be utilized on smartphones and tablets. This marked the first instance where developers could employ deep learning frameworks to train models and implement these early prototypes in real-world scenarios.\r\n\r\nIn 2012 when Hakan began working on visual perception and imaging systems utilizing artificial intelligence, it sparked an idea. Was it possible to replicate human visual comprehension and cognition in machines, enabling them to see, understand, and learn like humans?\r\n\r\nThis challenge became the catalyst and eventually turned into an obsessive goal for the G\u00fcltekin brothers.\r\n<h3><strong>While humans see with their eyes, they think in language.<\/strong><\/h3>\r\nDeep learning frameworks and networks, like Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN), enabling multimodal capabilities allowed vision to extend beyond the mere concept of sight. It now encompassed the peripheral and adjacent aspects such as language, audio, and tabular data.\r\n\r\nThe G\u00fcltekin brothers discovered that each artificial intelligence framework possessed unique characteristics in terms of data collection, annotation, model training, and deployment. Working together, these AI algorithms could solve the business challenges of detecting visuals, objects, and actions in video images.\r\n\r\nAs a result, they shifted their focus exclusively on developing artificial intelligence for <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>. The significance of vision lies in the fact that most things in the world are observed through sight. For the G\u00fcltekin brothers, understanding vision not only posed significant complexity but also offered the biggest societal impact.\r\n<h3>What is Chooch\u2019s AI Vision technology?<\/h3>\r\nThink of Chooch\u2019s <a href=\"https:\/\/www.chooch.com\/\">AI Vision<\/a> technology as highly evolved eyes and brains with limitless capacity to perform hypercritical tasks.\r\n\r\nChooch's AI Vision solutions helps enterprises derive valuable insights from visual data to drive better informed business decisions. Chooch\u2019s AI Vision instantly detects specific visuals, objects, and actions in videos and images, including critical anomalies; immediately comprehending their significance; and instantly putting into motion pre-programed responses to them. It does this in a fraction of the time a human being could.\r\n\r\nThese highly evolved artificial eyes and brains produce vast amounts of data and can analyze it quickly and accurately at scale to provide insights upon which to formulate predictive models and prevention.\r\n<h3>How is AI Vision transforming industries?<\/h3>\r\nIndustries like <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a>, transportation, security, and entertainment, among others, are already experiencing profound transformations with <a href=\"https:\/\/www.chooch.com\/\">AI Vision<\/a>.\r\n\r\n<strong>Healthcare<\/strong>: AI Vision is enhancing medical imaging analysis, assisting in disease diagnosis, and enabling more precise surgical interventions.\r\n\r\n<strong>Manufacturing<\/strong>: It is optimizing quality control processes, detecting anomalies on production lines, and enhancing production automation.\r\n\r\n<strong>Retail<\/strong>: AI Vision is enabling personalized shopping experiences, inventory management, and loss prevention.\r\n\r\n<strong>Transportation<\/strong>: AI Vision is driving better autonomous driving, traffic management, and infrastructure safety monitoring.\r\n\r\n<strong>Security and safety:<\/strong> Security systems are being enhanced with <a href=\"https:\/\/www.chooch.com\/\">AI Vision<\/a> for real-time surveillance for <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety hazards<\/a> as well as threat detection.\r\n\r\n<strong>Entertainment<\/strong>: AI Vision is creating more immersive experiences and better personalized content recommendations for viewers.\r\n\r\nFrom counting cells faster and more accurately than medical researchers to identifying objects on the ground from aircraft synthetic aperture radar more easily than geospatial analysts to detecting poor quality product photos posted online faster then digital merchandising editors\u2014Chooch is making a transformative impact across multiple industries.\r\n<h3>The future of AI Vision from Chooch<\/h3>\r\nThe widespread adoption of AI Vision has historically been hindered by economic viability and technical feasibility challenges. But these obstacles are steadily diminishing as advancements in cloud computing, availability of more diverse datasets, more powerful and affordable hardware, and continued investment AI and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> continue to accelerate.\r\n\r\nEach day brings advancements that lower the friction to deploying and distributing <a href=\"https:\/\/www.chooch.com\/\">AI Vision<\/a> solutions on a massive scale.\r\n\r\nWe are already witnessing this transformation in the field of language, where barriers have been significantly reduced and <a href=\"https:\/\/www.forbes.com\/sites\/robtoews\/2022\/02\/13\/language-is-the-next-great-frontier-in-ai\/?sh=5290eaa25c50\" target=\"_blank\" rel=\"noopener\">language-based AI technologies<\/a> that utilize Natural Language Processing (NLP) techniques, like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformers), have become pervasive.\r\n\r\nChooch's vision for the future of AI Vision technology is to unlock its full potential across various industries, revolutionizing the way we perceive and interact with the world. By making AI Vision economically viable, technically feasible, and easily accessible, Chooch aims to empower businesses and individuals to leverage the transformative capabilities of this technology.\r\n\r\nWith <a href=\"https:\/\/www.chooch.com\/\">AI Vision<\/a> at the forefront, the G\u00fcltekin brothers are dedicated to shaping a future where visual intelligence drives innovation, efficiency, and a positive impact across all sectors of society.",
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"post_date": "2023-06-26 08:15:15",
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"post_content": "Picture a world where machines can not only see but also describe what they see in a way that is insightful and relatable to humans. This is the world we are stepping into, thanks to the confluence of two of the most groundbreaking technologies in artificial intelligence: Large Language Models (LLMs) and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer Vision<\/a>.\r\n\r\nOver the years, computer vision has empowered machines to comprehend images and videos, facilitating capabilities like <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">object detection, image classification<\/a>, pattern recognition, and situational analysis. At the same time, large language models have allowed machines to understand and generate human-like language. These two areas are beginning to intersect, holding immense potential for enterprises across industries.\r\n\r\nLet\u2019s delve into the developments in computer vision technology, the role large language models play, and how their integration accelerates next-gen AI use cases across various industries.\r\n<h3>The evolution of computer vision and its role in enterprises<\/h3>\r\nAt its core, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> equips machines to perceive and interpret visual data like the human eye. By processing images and data using trained models of neural networks and utilizing cameras as sensors, computer vision models can identify objects, actions, and discern patterns to help provide insights for making more informed data-driven decisions. This ability to provide actionable insights has enabled advancements like <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a>, autonomous vehicles, and image-based diagnostic systems.\r\n\r\nDifferent types of Convolutional Neural Networks (CNNs) and Deep Learning frameworks have played a crucial role in their evolution.\r\n<ul>\r\n \t<li><strong>Convolutional Neural Networks<\/strong>\r\nCNNs simplify images into a matrix of pixels, assigning mathematical values to each pixel. When multiplied with different filters, these values help identify various concepts within an image. While CNNs have been pivotal in computer vision, newer techniques like Vision Transformers are emerging, promising to elevate the field further.<\/li>\r\n \t<li><strong>Deep Learning<\/strong>\r\nDeep Learning, a subset of machine learning, utilizes neural networks with several layers (hence, 'deep') to process data and make predictions. This technology has transformed <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>, enabling more sophisticated image processing and recognition tasks.<\/li>\r\n<\/ul>\r\nWith the rise in high compute devices, like GPUs and next-gen CPUs, businesses are pushing AI closer to where data is acquired. This approach, known as edge computing, is empowering businesses to deploy intelligent systems that can monitor and gather critical information in real time. These computer vision models simplify decision-making, boost productivity, and reduce losses by eliminating the complexities associated with manual visual data processing.\r\n<h3>The intersection of large language models and computer vision<\/h3>\r\nWhile computer vision is already revolutionizing many industries, integrating it with large language models can take its capabilities several notches higher. The goal is to teach these machines to see and generate human-like language and respond to textual prompts. As a result, providing more detailed insights about the visuals and video streams.\r\n\r\nIntegrating large language models with <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> allows operators to query, using text prompts, an infinite number of video streams at the same time with natural language, enhancing computer-to-computer (C2C) interactions.\r\n\r\n<strong>This transformative combination can:<\/strong>\r\n<ul>\r\n \t<li>Allow computers to comprehend visual information similarly to how the human brain processes it.<\/li>\r\n \t<li>Facilitate quick human responses to information based on previously impossible insights.<\/li>\r\n<\/ul>\r\n<h3>Impact of large language models and computer vision on different industries<\/h3>\r\nThe combination of large language models and computer vision is poised to impact various industries significantly.\r\n\r\n<strong>Let's examine a couple of them:<\/strong>\r\n<ul>\r\n \t<li><strong>Context-aware security: <\/strong>\r\nThe combined capabilities of large language models and computer vision can revolutionize surveillance systems. They can detect an intruder and generate a comprehensive report detailing the incident, accelerating threat response times, and significantly enhancing security.<\/li>\r\n \t<li><strong>AI-powered precision in healthcare: <\/strong>\r\nThe synergy between large language models and <a href=\"https:\/\/www.chooch.com\/\">computer vision<\/a> can bring about radical changes to diagnostic procedures. While advanced computer vision can analyze medical images, large language models can correlate these findings with patient history and medical research, delivering comprehensive diagnostics, and potential treatment options. This powerful combination can accelerate diagnostics, improve accuracy, and minimize human error and bias.<\/li>\r\n \t<li><strong>Automated inventory management: <\/strong>\r\nRetailers can use the combination of LLMs and computer vision for automating their inventory management systems. Cameras equipped with computer vision can scan shelves and identify items, noting their placement and quantity. The data captured by these cameras is then processed by an large language model, which generates detailed inventory reports, provides restocking alerts, and even assists in forecasting future inventory needs.<\/li>\r\n \t<li><strong>Manufacturing quality control:<\/strong>\r\nManufacturers are utilizing computer vision to identify product defects on assembly lines. Coupled with a large language model, these systems can provide detailed reports on the defects' nature, frequency, and potential causes. Better insights into the QA enables the manufacturer to take targeted action to improve product quality and efficiency.<\/li>\r\n<\/ul>\r\n<h3><strong>Looking forward: LLMs and computer vision as AI's next milestone<\/strong><\/h3>\r\nUntil now, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI solutions<\/a> have largely been segregated based on their computational power, use case needs, algorithm designs, and data type requirements for model training. However, the demand for multi-modal solutions that deliver targeted business value and address as many adjacent needs as possible is rising. Integrating large language models and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is a step in this direction, bringing us closer to realizing the dream of a highly competent digital assistant.\r\n\r\nThe integration of large language models and computer vision is heralding the advent of next-gen <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI technology<\/a>, where machines are trained to see and tell us what they see. For organizations, the convergence of these technologies facilitates the classification of enterprise data, generates prompts for specific visual content, and provides customized insights for actionable decision-making.\r\n\r\nThe time is ripe for businesses to leverage computer vision solutions incorporating large language models for generative AI capabilities. The benefits are manifold \u2013 decreased operational costs, reduced manual operations, and the elimination of the need for expensive and manual data and machine learning processes.\r\n\r\nThe possibilities are endless as we stand on the cusp of this exciting intersection of technologies. The fusion of large language models and computer vision is not just a novel development in the AI landscape; it's a leap toward a future where machines can understand our world in ways, we've only dreamed of until now. To learn more about Chooch's generative AI image-to-text technology, please <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">contact us<\/a>.",
"post_title": "How do Large Language Models (LLMs) Integrate with Computer Vision?",
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"post_date": "2023-06-14 08:03:10",
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"post_content": "As we enter the age of advanced technology known as Industry 4.0, smart devices, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> AI, and data analysis enable manufacturers to build a comprehensive strategy that combines various data-driven approaches to optimize manufacturing processes known as predictive manufacturing.\r\n\r\nManufacturers are applying data and predictive modeling to anticipate and prevent equipment failures or breakdowns, known as predictive maintenance. Predictive maintenance is helping manufacturers to improve the efficiency, reliability, and cost-effectiveness of their operations.\r\n<h2>Benefits of predictive maintenance beyond predicting maintenance<\/h2>\r\nThe adoption of predictive maintenance has grown in recent years due to advancements in technology, the availability of data, and the benefits both offer. However, predictive maintenance isn't just about preventing machine breakdowns, it has several other benefits that positively affect various aspects of manufacturing.\r\n\r\n<strong>Here are a few reasons why manufacturing companies are embracing predictive maintenance:<\/strong>\r\n<p style=\"padding-left: 40px;\"><strong>Reduces costs:<\/strong> Identifying potential equipment issues before they disrupt operations saves time and money. Early detection, coupled with the ability to forecast maintenance needs, reduces emergency repairs and extends machine life.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Increases safety:<\/strong> Proactively addressing equipment issues reduces the risk of employee accidents or injuries due to equipment failure and improves the overall <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safety<\/a> of their work environment. This EHS commitment provides employees with a greater sense of security.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Reduces workloads:<\/strong> By minimizing unexpected equipment breakdowns and failures, employees are less likely to be called upon for emergency repairs or troubleshooting tasks. This reduces the burden on their workload and improves productivity and job satisfaction.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Improves planning and scheduling:<\/strong> Based on data-driven insights, maintenance activities can be planned and scheduled. A clear plan and schedule for maintenance tasks enables employees to better allocate their time and resources and reduces the likelihood of last-minute disruptions or overtime work.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Competitive advantage:<\/strong> By reducing operational costs, improving product quality, and enhancing customer satisfaction, companies can gain a competitive edge. By minimizing equipment downtime and maximizing production efficiency, they can deliver products more reliably and meet customer demands effectively.<\/p>\r\nIt\u2019s clear that by optimizing maintenance, manufacturers not only improve operations and reduce costs but also create a more efficient and productive environment for their employees leading to an improved overall sense of well-being and superior job performance.\r\n<h2>The role of computer vision in predictive maintenance<\/h2>\r\n<strong>So where does computer vision fit in.<\/strong> Manufacturing is a world of precision and consistency; the tiniest defect can make a big impact. Traditional equipment inspection methods, either by human scrutiny or machine systems, have limitations, such as fatigue, lack of adaptability, and inability to spot certain defects.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> is beginning to play a crucial role in helping to augment traditional inspection processes. It can collect massive amounts of data from equipment, machinery, or production environments which can be analyzed faster and with greater accuracy, saving time and effort compared to traditional methods.\r\n\r\nBy analyzing real-time sensor data and historical performance data, computer vision can easily identify patterns, detect anomalies, and provide early warnings of potential equipment issues. This allows maintenance teams to schedule maintenance activities proactively, minimizing downtime, reducing costs, and optimizing the lifespan of equipment.\r\n\r\n<strong>So, let\u2019s explore the 6 ways computer vision is helping in predictive maintenance:<\/strong>\r\n<p style=\"padding-left: 40px;\"><strong>1. Anomaly detection:<\/strong> Computer vision algorithms can analyze images or videos captured by cameras to detect anomalies or deviations from normal operating conditions. By comparing real-time visual data to baseline or reference images, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> systems can quickly identify signs of potential equipment failure, such as abnormal vibrations, leaks, cracks, or irregularities in the appearance of components. These anomalies can often be very subtle and easily missed by the human eye, making computer vision far more accurate and reliable for detecting defects faster.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>2. Wear and tear assessment:<\/strong> Computer vision can monitor the wear and tear of equipment components over time. Advanced image segmentation techniques, like semantic segmentation, identify the exact location and size of the potential equipment degradation. By collecting this data at these levels of specificity, it makes it easier to track over time the condition of machine parts and signs of degradation or corrosion to estimate their remaining useful life. This information helps maintenance teams plan timely replacements or repairs, minimizing downtime, and optimizing maintenance schedules.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>3. Real-time monitoring:<\/strong> Computer vision enables real-time monitoring of production processes and equipment performance. By analyzing visual data, computer vision systems detect deviations from normal operating parameters, such as temperature, pressure, or speed. When abnormal conditions are detected, pre-programmed actions can be initiated, and real-time notifications can alert maintenance teams to intervene promptly to prevent potential failures that could disrupt operations.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>4. Object recognition and tracking:<\/strong> <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> can recognize and track objects, such as tools, parts, or products, within a manufacturing environment. Computer vision models using algorithms like Single Shot MultiBox Detectors (SSD), can detect and classify multiple objects within video images in real time. This helps in monitoring the movement and usage of equipment and assets within the manufacturing environment. For example, camera video streams can be analyzed in real time to track the location of tools and equipment to prevent loss or identify instances of improper handling.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>5. Inspection automation:<\/strong> By combining computer vision, robotics, and data analysis, equipment inspection processes and condition reports can be automated. For example, video data captured by cameras can be analyzed using deep learning algorithms to learn hierarchical representations of this data to deliver more accurate object recognition, image classification, and location segmentation. This level of analysis can detect defects, measure dimensions, or identify irregularities in components faster and more accurately; reducing the reliance on human inspectors and speeding up maintenance procedures.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>6. Predictive analytics:<\/strong> Computer vision data, when combined with other sensor data, can be leveraged for predictive analytics. By analyzing historical visual data and equipment performance, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> systems can identify patterns, correlations, or early warning signs of potential failures. This enables maintenance teams to predict maintenance actions before they are needed and plan proactive interventions.<\/p>\r\n\r\n<h2>The future of predictive maintenance with computer vision<\/h2>\r\nComputer vision provides manufacturers the ability to continuously monitor the health of equipment without fatigue. Just like a human, when a computer vision system detects a potential failure pattern, it can alert the maintenance team, allowing for timely replacement and avoid unexpected downtime. In a nutshell, computer vision is making machine maintenance smarter, safer, and more efficient.\r\n\r\nNot without implementation challenges, including data privacy concerns, the need for high-quality data for training AI models, and the technological infrastructure required, predictive maintenance driven by computer vision technology will become the norm in manufacturing. And as it continues to advance and merge with other technologies like artificial intelligence and the Internet of Things, it's set to revolutionize machine maintenance even further.",
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"post_date": "2023-06-12 23:29:39",
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"post_content": "Over the past few years, the drone industry has witnessed a significant evolution with the integration of advanced technologies, particularly computer vision. This shift has brought many opportunities for drone service providers, enhancing their capabilities, and creating great value in agriculture, construction, and military operations. We're now on the brink of an intriguing era where autonomous drones can execute complex tasks such as geospatial analysis and aerial surveillance with remarkable efficiency and precision.\r\n\r\nConsider a future where drones, armed with advanced <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>, capture detailed thermal images and autonomously interpret them through high-performance thermal image analysis software. It's a future where visual data management and analysis become smooth, automated processes, clearing the path for more effective operations across various sectors.\r\n\r\nIn this blog, we'll explore the realm of drone computer vision and image processing and the opportunities they present to drone service providers. From drone inspection to automated visual inspections, we will highlight the benefits and potential of these forward-looking technologies in reshaping the drone industry.\r\n\r\nSo, get your radio controller ready, and let's set off on this insightful expedition into the future of drones, where the sky's not the limit. Stay with us as we uncover the untapped potential of drone technology, and together we'll see how computer vision is on its way to redefining how we perceive and interact with the world around us.\r\n<h2>Deriving intelligence from drone visual data with computer vision<\/h2>\r\nAt the core of modern drone technology lies computer vision algorithms, empowering drones to navigate, interpret, and interact with their surroundings autonomously. By <a href=\"https:\/\/www.programmingempire.com\/the-advancements-in-drone-computer-vision-opportunities-and-challenges\/\">enhancing the precision and reliability of drone navigation systems<\/a>, computer vision facilitates autonomous drone flights and real-time decision-making.\r\n\r\nAnother advantage of computer vision algorithms is enhanced sensing. These algorithms amplify visual data processing from cameras, enabling drones to detect and respond to obstacles, identify and track targets, and make more informed decisions. in the result is improved object recognition, a particularly handy feature for delivery, inspection, and search and rescue operations applications.\r\n\r\nFurthermore, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> algorithms are pivotal in optimizing drone operations and improving efficiency, leading to lower energy consumption, longer flight times, and more advanced drone performance.\r\n<h2>Image processing: The drone\u2019s window on the world<\/h2>\r\nImage processing techniques powered by computer vision technology are instrumental in enabling a drone's understanding of its environment. By leveraging propulsion and navigation systems, sensors, cameras, and GPS technology, drones can avoid obstacles and zero in on their destinations.\r\n\r\nSeveral image annotation and segmentation techniques are used to train drones for aerial imaging. These techniques enable drones to <a href=\"https:\/\/www.keymakr.com\/blog\/computer-vision-in-drone-technology\/\">recognize, track, and avoid objects during flight<\/a>, thus improving drone accuracy and providing a richer visual representation of the environment.\r\n<h2>Enabling drones with computer vision AI: Common use cases<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision and AI<\/a> together have unlocked an array of possibilities for drone use across a variety of sectors. This technology transforms traditional operational methods, offering better efficiency, accuracy, and safer alternatives to manual procedures.\r\n\r\n<strong>Here are some expanded examples:<\/strong>\r\n\r\n<strong>Monitoring telecom infrastructure\r\n<\/strong>Drones equipped with AI and computer vision are revolutionizing the telecom industry. They can autonomously inspect vast telecom infrastructures, identifying issues such as defective Radio Access Network (RAN) units. This ensures consistent network performance and reduces downtime. Additionally, these drones can conduct pipeline security inspections, identifying potential leaks or damage, especially in areas that are difficult to reach or inspect manually.\r\n\r\n<strong>Wildfire detection\r\n<\/strong>One of the more novel applications is <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">fire detection<\/a>. Using thermal cameras and computer vision algorithms, drones can swiftly detect and report heat signatures indicative of fire, helping prevent minor incidents from escalating into more significant disasters.\r\n\r\n<strong>Detecting railway maintenance and hazards\r\n<\/strong>AI-powered drones provide an innovative solution for maintaining and monitoring railways. They can capture high-resolution images of tracks, identify potential <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards<\/a>, and assess track integrity. By leveraging computer vision algorithms, these drones can detect structural abnormalities or debris and send accurate location data to facilitate quick repair and maintenance.\r\n\r\n<strong>Monitoring solar infrastructure\r\n<\/strong>Drones, equipped with thermal cameras and AI, play an increasingly important role in renewable energy. They can scan vast solar farms and use real-time image processing to detect anomalies in solar panels. <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI algorithms<\/a> allow drones to autonomously interpret the captured thermal data, identifying underperforming panels for quick maintenance and repair.\r\n\r\n<strong>Perimeter detection\r\n<\/strong>Drones powered by computer vision are becoming a cornerstone of perimeter detection and surveillance. They can patrol boundaries and detect potential intrusion attempts, serving as an invaluable security asset. These drones can differentiate between bystanders and potential threats, reducing false alarms and ensuring more accurate security responses.\r\n<h2>End-to-end drone solutions: The aim<\/h2>\r\nMany companies in the drone industry strive to provide comprehensive, end-to-end drone solutions. They integrate everything from flight planning and data capture to data analysis and reporting. They also incorporate advanced technologies such as <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">computer vision and machine learning<\/a> to enhance drone performance and efficiency, making drones even more versatile across various industries.\r\n\r\nIntegrating computer vision into drone technology is transforming what drones can do and extending their potential applications. Drones have become invaluable tools in various industries, from image processing and geospatial analysis to visual data management and automated visual inspections. As drone technology continues to evolve, the sky is truly the limit for what these versatile machines can achieve.",
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"post_date": "2023-06-11 16:39:27",
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"post_content": "<span data-contrast=\"none\">Computer vision (CV) is reshaping industries with diverse applications, from self-driving cars to augmented reality, facial recognition systems, and medical diagnostics. <\/span><span data-contrast=\"none\">\u201cNo industry has been or will be untouched by <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> innovation, and the next generation of emerging technologies will generate new market opportunities and innovations, including: Scene understanding and fine-grained object and behavior recognition for security, worker health and safety, and critical patient care.\u201d <\/span><span data-contrast=\"none\">Despite the robust growth and increasing market value, CV still faces challenges\u200b.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">Innovations like the shift from model-centric to data-centric artificial intelligence and the rise of Generative AI appear promising for tackling common <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> challenges. As we delve into five common problems, we'll explore the solutions, and how they pave the way for a more advanced and efficient use of computer vision.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h3><strong> <img class=\"wp-image-5407 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/fire-detection.jpg\" alt=\"Fire Detection\" width=\"800\" height=\"396\" \/><\/strong><\/h3>\r\n<h3><strong>1. Variable lighting conditions<\/strong><\/h3>\r\n<h4>Problem:<\/h4>\r\n<span data-contrast=\"none\">One of the significant challenges for computer vision systems is dealing with <\/span><a href=\"https:\/\/www.exposit.com\/blog\/computer-vision-object-detection-challenges-faced\/\"><span data-contrast=\"none\">varied lighting conditions<\/span><\/a><span data-contrast=\"none\">. Changes in lighting can considerably alter the appearance of an object in an image, making it difficult for the system to recognize. The lighting challenges in computer vision is complex due to the difference between human visual perception and camera image processing. While humans can easily adjust to different lighting conditions, <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-definitions\/\">computer vision systems<\/a> can struggle with it. Varying amounts of light in other parts of the combined with shadows and highlights distort the appearance of objects. Moreover, different types of light (e.g., natural, artificial, direct, diffused) can create other visual effects, further complicating the object recognition task for these systems.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h4>Solutions:<\/h4>\r\n<span data-contrast=\"none\">Techniques such as histogram equalization and gamma correction help counteract the effects of variable lighting conditions. Histogram equalization is a method that <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Histogram_equalization\"><span data-contrast=\"none\">improves the contrast of an image<\/span><\/a><span data-contrast=\"none\"> by redistributing the most frequent intensity values across the image. At the same time, gamma correction adjusts the brightness of an image by applying a nonlinear operation to the pixel values. These methods adjust the brightness across an image, improving the system's ability to identify objects irrespective of lighting conditions.\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">Another approach to the problem of variable lighting conditions involves using hardware solutions, such as infrared sensors or depth cameras. These devices can capture information that isn't affected by lighting conditions, making object recognition more manageable. For instance, depth cameras can provide data about the distance of different parts of an object from the camera, which help identify the object even when lighting conditions make it difficult to discern its shape or color in a traditional 2D image. Similarly, infrared sensors can detect heat signatures, providing additional clues about an object's identity.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<img class=\"wp-image-5406 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/animal-livestock-detection.jpg\" alt=\"Livestock and Animal Detection\" width=\"800\" height=\"409\" \/>\r\n<h3><b><span data-contrast=\"none\">2. Perspective and scale variability<\/span><\/b><\/h3>\r\n<h4>Problem:<\/h4>\r\n<span data-contrast=\"none\">Objects can appear differently depending on their distance, angle, or size in relation to the camera. This <\/span><a href=\"https:\/\/ar5iv.labs.arxiv.org\/html\/2202.02489v1\"><span data-contrast=\"none\">variability in perspective and scale presents a significant challenge for computer vision systems<\/span><\/a><span data-contrast=\"none\">. In remote sensing applications, accurate object detection from aerial images is more difficult due to the variety of objects that can be present, in addition to significant variations in scale and orientation\u200b.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h4>Solutions:\u00a0<span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span><\/h4>\r\n<span data-contrast=\"none\">Techniques such as <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Scale-invariant_feature_transform\"><span data-contrast=\"none\">Scale-Invariant Feature Transform (SIFT)<\/span><\/a><span data-contrast=\"none\">, Speeded Up Robust Features (SURF), and similar methods can identify and compare objects in images regardless of scale or orientation.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">SIFT is a method that can more reliably identify objects even among clutter and under partial occlusion, as it is an invariant to uniform scaling, orientation, and illumination changes. It also offers partial invariance to affine distortion. The SIFT descriptor is based on image measurements over local scale-invariant reference frames established by local scale selection. The SIFT features are local and based on the object's appearance at particular interest points, making them invariant to image scale and rotation.\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h3><b><span data-contrast=\"none\">3. Occlusion<\/span><\/b><\/h3>\r\n<h4>Problem: <span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span><\/h4>\r\n<span data-contrast=\"none\">Occlusion refers to scenarios where another object <\/span><a href=\"https:\/\/stackoverflow.com\/questions\/2764238\/image-processing-what-are-occlusions\"><span data-contrast=\"none\">hides or blocks part of an object<\/span><\/a><span data-contrast=\"none\">. This challenge varies depending on the context and sensor setup used in <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>. For instance, in object tracking, occlusion occurs when an object being tracked is hidden by another object, like two people walking past each other or a car driving under a bridge. In range cameras, occlusion represents areas where no information is present because the camera and laser are not aligned, or in stereo imaging, parts of the scene that are only visible to one of the two cameras. This issue poses a significant challenge to computer vision systems as they may struggle to identify and track partially obscured objects correctly over time.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h4>Solutions:<span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span><\/h4>\r\n<span data-contrast=\"none\">Techniques like <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Robust_principal_component_analysis\"><span data-contrast=\"none\">Robust Principal Component Analysis (RPCA)<\/span><\/a><span data-contrast=\"none\"> can help separate an image's background and foreground, potentially making occluded objects more distinguishable. RPCA is a modification of the principal component analysis (PCA) statistical procedure, which aims to recover a low-rank matrix from highly corrupted observations. In video surveillance, if we stack the video frames as matrix columns, the low-rank component naturally corresponds to the stationary background, and the sparse component captures the moving objects in the foreground\u200b.<\/span>\r\n\r\n<span data-contrast=\"none\">Training models on datasets that include occluded objects can improve their ability to handle such scenarios. However, creating these datasets poses a challenge due to the requirement of a large number and variety of occluded video objects with modal mask annotations. A possible solution is to use a self-supervised approach to create realistic data in large quantities. For instance, the YouTube-VOI dataset contains 5,305 videos, a 65-category label set including common objects such as people, animals, and vehicles, with over 2 million occluded and visible masks for moving video objects. A unified multi-task framework, such as the Video Object Inpainting Network (VOIN), can <\/span><a href=\"https:\/\/ar5iv.labs.arxiv.org\/html\/2108.06765\"><span data-contrast=\"none\">infer invisible occluded object regions and recover object appearances<\/span><\/a><span data-contrast=\"none\">. The evaluation of the VOIN model on the YouTube-VOI benchmark demonstrates its advantages in handling occlusions\u200b\u200b.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h3><b><span data-contrast=\"none\"><img class=\"wp-image-5408 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/06\/people-face-detection-street.jpg\" alt=\"People Detection\" width=\"807\" height=\"430\" \/><\/span><\/b><\/h3>\r\n<h3><b><span data-contrast=\"none\">4. Contextual understanding<\/span><\/b><\/h3>\r\n<h4>Problem:<\/h4>\r\n<span data-contrast=\"none\"><a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-definitions\/\">Computer vision systems<\/a> often need help with understanding context. They can identify individual objects in an image, but understanding the relationship between them and interpreting the scene can be problematic.<\/span>\r\n<h4>Solutions:<\/h4>\r\n<span data-contrast=\"none\">Scene understanding techniques are being developed to tackle this problem. One particularly challenging field within scene understanding is Concealed Scene Understanding (CSU), which involves recognizing objects with camouflaged properties in natural or artificial scenarios. The CSU field has advanced in recent years with <\/span><a href=\"https:\/\/ar5iv.labs.arxiv.org\/html\/2304.11234\"><span data-contrast=\"none\">deep learning techniques and the creation of large-scale public datasets such as COD10K<\/span><\/a><span data-contrast=\"none\">, which has advanced the development of visual perception tasks, especially in concealed scenarios. A benchmark for Concealed Object Segmentation (COS), a crucial area within CSU, has been created for quantitative evaluation of the current state-of-the-art. Moreover, the applicability of deep CSU in real-world scenarios has been assessed by restructuring the CDS2K dataset to include challenging cases from various industrial settings\u200b.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">Furthermore, incorporating Natural Language Processing (NLP) techniques such as Graph Neural Networks (GNNs) can help models <\/span><a href=\"https:\/\/ar5iv.labs.arxiv.org\/html\/2303.03761\"><span data-contrast=\"none\">understand relations between objects in an image<\/span><\/a><span data-contrast=\"none\">. GNNs have become a standard component of many 2D image understanding pipelines, as they can provide a natural way to represent the relational arrangement between objects in an image. They have been especially used in tasks such as image captioning, Visual Question Answering (VQA), and image retrieval. These tasks require the model to reason about the image to describe it, explain aspects of it, or find similar images, which are all tasks that humans can do with relative ease but are difficult for deep learning models.<\/span>\r\n<h3><b><span data-contrast=\"none\">5. Lack of annotated data<\/span><\/b><\/h3>\r\n<h4>Problem:<span data-contrast=\"none\">\u202f<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span><\/h4>\r\n<span data-contrast=\"none\"><a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-engineer-skills-and-jobs\/\">Training computer vision<\/a> models necessitates a substantial amount of annotated data. Image annotation, a critical component of training AI-based computer vision models, involves human annotators structuring data. For example, images are <\/span><a href=\"https:\/\/encord.com\/blog\/guide-image-annotation-computer-vision\/\"><span data-contrast=\"none\">annotated to create training data for computer vision models identifying specific objects across a dataset<\/span><\/a><span data-contrast=\"none\">\u200b. However, manual annotation is a labor-intensive process that often necessitates domain expertise, and this process can consume a significant amount of time, particularly when dealing with large datasets\u200b.<\/span>\r\n<h4>Solution:<span data-contrast=\"none\">\u202f<\/span><\/h4>\r\n<span data-contrast=\"none\">Semi-supervised and unsupervised learning techniques offer promising solutions to this issue. These methods <\/span><a href=\"https:\/\/ar5iv.labs.arxiv.org\/html\/2208.11296\"><span data-contrast=\"none\">leverage unlabeled data, making the learning process more efficient<\/span><\/a><span data-contrast=\"none\">. <\/span>\r\n\r\n<i><span data-contrast=\"none\">Semi-supervised learning (SSL)<\/span><\/i><span data-contrast=\"none\">\u202faims to jointly learn from sparsely labeled data and a large amount of unlabeled auxiliary data. The underlying assumption is that the unlabeled data is often drawn from the same distribution as the labeled data. SSL has been used in various application domains such as image search, medical data analysis, web page classification, document retrieval, genetics, and genomics.<\/span>\r\n\r\n<i><span data-contrast=\"none\">Unsupervised learning (UL)<\/span><\/i><span data-contrast=\"none\">\u202faims to learn from only unlabeled data without utilizing any task-relevant label supervision. Once trained, the model can be fine-tuned using labeled data to achieve better model generalization in a downstream task\u200b.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">Also, techniques like data augmentation can artificially increase the size of the dataset by creating altered versions of existing images.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"335559738":0,"335559739":0}\">\u00a0<\/span>\r\n<h2>Computer vision\u2019s next frontier and AI\u2019s role as its primary catalyst<\/h2>\r\n<span data-contrast=\"none\">Computer vision is an immensely beneficial technology with widespread applications, spanning industries from <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> to transportation, e-commerce, and beyond. However, it has its challenges. Factors such as varied lighting conditions, perspective and scale variability, occlusion, lack of contextual understanding, and the need for more annotated data have created obstacles in the journey toward fully efficient and reliable <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-definitions\/\">computer vision systems<\/a>.<\/span>\r\n\r\n<span data-contrast=\"none\">Researchers and engineers continually push the field's boundaries in addressing these issues. Techniques such as histogram equalization, gamma correction, SIFT, SURF, RPCA, and the use of CNNs, GNNs, and semi-supervised and unsupervised learning techniques, along with data augmentation strategies, have all been instrumental in overcoming these challenges.<\/span>\r\n\r\n<span data-contrast=\"none\">Continued investment in research, development, and training of the next generation of computer vision scientists is vital for the field's evolution. As computer vision advances, it will play an increasingly important role in driving efficiency and innovation in many sectors of the economy and society. Despite the challenges faced, the future of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> remains promising, with immense potential to reshape our world.<\/span>\r\n<h2>Computer vision platforms of tomorrow<\/h2>\r\n<span data-contrast=\"none\">The most recent wave of generative <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-security-robotics-and-drone-ai-for-the-security-industry\/\">AI technologies<\/a> will prove instrumental in shaping the next iterations of computer vision solutions. Today\u2019s CV platforms use AI to detect events, objects, and actions that neural networks have been trained to identify, but tomorrow\u2019s platforms may use AI to <\/span><i><span data-contrast=\"none\">speculate<\/span><\/i><span data-contrast=\"none\"> the outcome of events, objects\u2019 state or positions, and the results of actions before they occur.<\/span>\r\n\r\n<span data-contrast=\"none\">The true challenge of today\u2019s AI-powered vision-based systems is their narrow understanding. For a model to \u201cknow\u201d how to spot more objects, it must be familiar with those things. More knowledge means more training and heavier models.<\/span>\r\n\r\n<span data-contrast=\"none\">Our society is on the precipice of general AI, which will provide always-on, hyper-intelligent, digital assistants to tomorrow\u2019s enterprises. Such assistants will not just know how to detect things it knows, but they will know how to learn and how to communicate what they see. Replicating human visual understanding has never been closer to a reality as it is today.<\/span>\r\n\r\n<span data-contrast=\"none\">Have you ever tried chatting it an image? <\/span><a href=\"https:\/\/www.chooch.com\/imagechat\/\"><span data-contrast=\"none\">Chooch\u2019s ImageChat\u2122<\/span><\/a><span data-contrast=\"none\"> is pioneering this concept by combining the latest <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> architecture with LLMs to give us a glimpse of what this future will be like.\u00a0<\/span>",
"post_title": "5 Common Problems with Computer Vision and their Solutions\u00a0",
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"post_content": "<p aria-level=\"1\"><span data-contrast=\"auto\"><strong>Machine Learning<\/strong> (ML) and <strong>Deep Learning<\/strong> (DL) are subsets of artificial intelligence, playing pivotal roles in advanced technology like self-driving cars, voice assistants, and recommendation systems.<\/span><\/p>\r\n<p aria-level=\"1\"><span data-contrast=\"auto\">Machine learning is a data analysis method that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It uses algorithms to \"learn\" information directly from data without a predetermined equation as a model.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n<span data-contrast=\"auto\">Deep learning, a subfield of <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a>, uses artificial neural networks inspired by the human brain to carry out machine learning. These networks can transform inputs in increasingly abstract ways, enabling them to solve complex problems previously thought to be the exclusive domain of human cognition.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Generally, deep learning is machine learning, but not all machine learning is deep learning. Deep learning can tackle tasks that are too complex for traditional machine learning processes but requires more data and computational horsepower.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h2><strong>An introduction to machine learning<\/strong><\/h2>\r\n<span data-contrast=\"auto\">Machine learning, a foundational part of artificial intelligence (AI), is fundamentally a method of data analysis that empowers computers to uncover hidden insights without explicit programming. Its central principle is rooted in systems <\/span><a href=\"https:\/\/mitsloan.mit.edu\/ideas-made-to-matter\/machine-learning-explained\"><span data-contrast=\"none\">learning from data, identifying patterns, and making decisions with minimal human intervention<\/span><\/a><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3>Three types of machine learning:<\/h3>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">1. Supervised Learning\r\n<\/span><\/b>In this type of learning, the model is given labeled training data along with the desired output. The goal is to learn a general rule that maps inputs to outputs. It's akin to learning with a teacher who provides guidance. Common algorithms used in supervised learning include Linear Regression, Decision Trees, and Support Vector Machines. According to a survey conducted in 2020, about 89% of data scientists use supervised learning methods in their work.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">2. Unsupervised Learning\r\n<\/span><\/b>In this scenario, the model is given unlabeled data and must discover the underlying structure and relationships within that data on its own. This is analogous to learning without a teacher. Common algorithms in unsupervised learning include K-means Clustering, Hierarchical Clustering, and Principal Component Analysis. While less commonly used than supervised learning, unsupervised learning is vital for anomaly detection and understanding complex datasets.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">3. Reinforcement Learning\r\n<\/span><\/b>Here, the model learns to make decisions based on rewards and penalties. It's akin to learning by trial and error taking suitable action to maximize reward in a particular situation. Reinforcement learning has been instrumental in teaching computers to perform tasks once thought to require human intuition, such as playing complex games like Go and Chess. AlphaGo, a computer program developed by Google DeepMind, used reinforcement learning to defeat the world champion in the board game Go in 2016, marking a significant milestone in AI research.<span data-ccp-props=\"{"335559685":720}\">\u00a0<\/span><\/p>\r\n\r\n<h2>An introduction to deep learning<\/h2>\r\n<span data-contrast=\"auto\">Deep learning, a subset of <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a>, employs <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Artificial_neural_network\"><span data-contrast=\"none\">artificial neural networks<\/span><\/a><span data-contrast=\"auto\"> with several layers (\"deep\" structures) to model and understand complex patterns in datasets. These neural networks attempt to simulate the behavior of the human brain\u2014albeit far from matching its ability\u2014to learn from substantial amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help optimize the predictions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3 aria-level=\"3\">The types of deep learning include:<\/h3>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Artificial Neural Networks (ANNs)\r\n<\/span><\/b><span data-contrast=\"auto\">Inspired by the human brain, ANNs are the foundation of deep learning. They are designed to simulate the behavior of the human brain to solve complex pattern recognition tasks. ANN's capabilities are highlighted by Google's DeepMind using them to secure 32 wins out of 40 games against the world champion of the Ancient Game of <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Go_(game)\"><span data-contrast=\"none\">Go<\/span><\/a><span data-contrast=\"auto\">.<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Convolutional Neural Networks (CNNs)\r\n<\/span><\/b><span data-contrast=\"auto\">CNNs are primarily used in pattern recognition within images and are mostly applied in image recognition tasks. They have been instrumental in the medical field, with deep learning techniques achieving 95% accuracy in detecting Parkinson's disease through voice samples.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Recurrent Neural Networks (RNNs)\r\n<\/span><\/b><span data-contrast=\"auto\">RNNs excel in learning from sequential data, making them especially effective for natural language processing and time-series analysis.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n\r\n<h2>Key differences between machine learning and deep learning<\/h2>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Data dependencies<\/span><\/b>\r\n<span data-contrast=\"auto\">Machine learning algorithms can perform well in scenarios with massive and large datasets, while deep learning algorithms are better suited for massive datasets. This is because DL models learn complex patterns from the data, and the accuracy of these models generally improves with more data.\r\n<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Computational requirements\r\n<\/span><\/b>Deep learning models require significantly more computational power than <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a> models due to their complexity and the large datasets they use. This is especially true for DL models with many layers, often requiring high-performance clusters and other substantial infrastructure. While ML models can run on a single instance or server cluster, DL models typically require powerful hardware accelerators, often GPUs.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Feature engineering\r\n<\/span><\/b>Data scientists often manually handle data extraction in machine learning, which can be time- and labor-intensive. In contrast, deep learning handles feature extraction automatically during the learning process, which can significantly reduce the workload of data scientists. However, this automatic feature extraction in DL is balanced by the requirement of network topology design, which can put a heavy load on the execution time and efficiency.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Interpretability<\/span><\/b>\r\nMachine learning models offer precise rules that can be used to explain the decisions behind specific choices, making them easier to interpret. In contrast, the decisions made by deep learning models can seem \"arbitrary,\" providing the user with little interpretive capability to rationalize choices. This lack of interpretability in DL models is often called the \"black box\" problem.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Training time\r\n<\/span><\/b>The training time for DL models is typically longer and more complex due to their intricate neural layers. In contrast, ML algorithms can often be trained in a much shorter time.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Problem-solving approach\r\n<\/span><\/b>In <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a>, large problems are often broken down into smaller chunks, each solved separately, and then all solutions are put back together. However, deep learning solves problems end-to-end, meaning it takes in raw input (like image pixels or text) and processes it through multiple layers of its neural network to output a result without any need for manual feature extraction or rule-based programming.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Hardware dependencies<\/span><\/b>\r\nBoth machine learning and deep learning require significant computational resources, but the extent and nature of these requirements differ. ML can often run efficiently on modest hardware, while DL generally demands more powerful hardware due to its need for processing large neural networks. For instance, graphics processing units (GPUs) are often employed for DL because they can perform many operations simultaneously \u2013 an ideal feature for training large neural networks.<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Algorithm types\r\n<\/span><\/b>ML includes a variety of algorithm types, including linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means, random forest, and dimensionality reduction algorithms. On the other hand, DL is more focused and includes algorithms such as convolutional neural networks, recurrent neural networks, long short-term memory networks, generative adversarial networks, and deep belief networks.\u00a0<span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n\r\n<h2 aria-level=\"2\">Applications of machine learning and deep learning<\/h2>\r\n<h3 aria-level=\"2\">Machine learning in action<\/h3>\r\n<p aria-level=\"2\"><span data-contrast=\"auto\">Traditional ML algorithms, like decision trees, are rule-based and are excellent for problems where the reasoning process is well-understood and can be defined in terms of rules or conditions. <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">Machine learning techniques<\/a> are effective with smaller datasets, especially if the dataset is well-curated, like structured data, where features have clear definitions and relationships.<\/span><\/p>\r\n<p aria-level=\"2\">Machine learning has wide-ranging applications in various fields:<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Predicting house prices\r\n<\/span><\/b><span data-contrast=\"auto\">With Linear Regression, we can estimate the price of a house based on simple features such as the number of bedrooms, location, and size of the house. This is commonly used in the real estate industry.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Diagnosing patient illnesses\r\n<\/span><\/b><span data-contrast=\"auto\">Decision Trees and Random Forests can help doctors diagnose diseases by looking at past patient records. This tool can learn from these past cases to make accurate predictions.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Recognizing faces\r\n<\/span><\/b><span data-contrast=\"auto\">Support Vector Machines (SVMs) help in recognizing faces in images. This can be used in various systems, such as security, to identify or verify a person from a digital image or a video frame.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Suggesting products or movies\r\n<\/span><\/b><span data-contrast=\"auto\">K-Nearest Neighbors (KNN) is used in services like Amazon or Netflix to suggest items you might like. This is done based on your past behavior and the behavior of other users like you.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Filtering spam and understanding customer opinions\r\n<\/span><\/b><span data-contrast=\"auto\">Naive Bayes is used to sort your emails, identifying which ones are likely to be spam. It's also used to understand whether customer reviews are positive, negative, or neutral.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n\r\n<h3 aria-level=\"2\">Deep learning in action<\/h3>\r\n<p aria-level=\"2\"><span data-contrast=\"auto\">Deep learning, a subset of machine learning, is particularly adept at managing problems where the reasoning process is intricate and not readily expressible in explicit rules. Unstructured data, such as images, audio, and text, often contain relationships and complex patterns that are challenging to capture using traditional, rule-based methods. It's in these domains where deep learning truly shines.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n<p aria-level=\"2\"><span data-contrast=\"auto\">Deep learning relies heavily on artificial neural networks, particularly those with numerous layers, hence the term \"deep.\" These multilayered networks mimic the human brain's function, allowing the model to learn intricate patterns and perform abstract reasoning. Because of this, deep learning can outperform traditional <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a> in tasks involving high-dimensional, unstructured data.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\r\n<p aria-level=\"2\">Let's consider some common applications of deep learning to illustrate its capabilities:<\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Computer vision\r\n<\/span><\/b><span data-contrast=\"auto\">Deep learning algorithms are proficient at identifying, classifying, and labeling objects in images and videos. They form the backbone of numerous applications, including facial recognition software, object detection in surveillance systems, and even disease diagnosis in medical imaging.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Language translation\r\n<\/span><\/b><span data-contrast=\"auto\">Deep learning has revolutionized the field of natural language processing, including language translation. For example, Google\u2019s neural machine translation system uses deep learning to translate between languages with remarkable accuracy, often matching or surpassing human translators.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Sentiment analysis\r\n<\/span><\/b><span data-contrast=\"auto\">Deep learning is extensively used in sentiment analysis, which involves understanding human emotions from text. This application is particularly beneficial in marketing and customer service, where understanding customer sentiment can inform strategy and improve service delivery.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Autonomous driving\r\n<\/span><\/b><span data-contrast=\"auto\">Autonomous vehicles utilize deep learning to perceive their environment and make driving decisions. They use it to recognize objects, predict their movements, and determine optimal paths.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Virtual assistants\r\n<\/span><\/b><span data-contrast=\"auto\">Deep learning is crucial in virtual assistants like Amazon's Alexa, Google's Assistant, and Apple's Siri. These systems use it to understand spoken language, recognize the user's voice, and generate natural-sounding responses.<\/span><span data-ccp-props=\"{"335559685":720,"469777462":[720],"469777927":[0],"469777928":[1]}\">\u00a0<\/span><\/p>\r\n\r\n<h2>Playing favorites\u2014Deep learning vs. machine learning<\/h2>\r\n<span data-contrast=\"auto\">Machine learning and deep learning are two significant subsets of <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Artificial_intelligence\"><span data-contrast=\"none\">artificial intelligence<\/span><\/a><span data-contrast=\"auto\"> with unique strengths.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">Machine learning<\/a> excels in structured data environments with clear rules, making it ideal for applications ranging from real estate to healthcare.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">On the other hand, deep learning is particularly adept with unstructured data like images, audio, and text, making strides in computer vision, language processing, and autonomous driving. Choosing between them is not superiority but problem applicability, data nature, and resource availability. Both continue to drive advancements across numerous fields, revolutionizing the era of artificial intelligence applications.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>",
"post_title": "A Comparison Guide to Deep Learning vs. Machine Learning \u00a0",
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"post_date": "2023-06-05 23:29:44",
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"post_content": "<span class=\"TextRun SCXW202602414 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun CommentStart SCXW202602414 BCX0\">As <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">the <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">product lead for <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">Chooch<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">\u2019s<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> AI Vision platform<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">, Kasim <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">Acikbas<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> drives the <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">strategy for creating <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">solutions that both fit market needs and drive <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">enterprise-level <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">growth.<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">He works hand-and-hand with <\/span><\/span><a class=\"Hyperlink SCXW202602414 BCX0\" href=\"https:\/\/www.chooch.com\/blog\/meet-chooch-ux-designer-zeynep-inal-caculi\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW202602414 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW202602414 BCX0\" data-ccp-charstyle=\"Hyperlink\">Zeynep <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\" data-ccp-charstyle=\"Hyperlink\">Caculi<\/span><\/span><\/a><span class=\"TextRun SCXW202602414 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW202602414 BCX0\">, <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">product design lead, to<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">make sure that <\/span><a href=\"https:\/\/www.chooch.com\/platform\/\"><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">Chooch\u2019s<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">AI Vision<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">platform <\/span><\/a><span class=\"NormalTextRun SCXW202602414 BCX0\">features and functionality <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">surpass<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> customer expectations<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">. <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">Kasim\u2019s<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> diverse background as a front-end developer and UI designer<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">gives<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> him a <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">unique<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> perspective when developing <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">Chooch<\/span> <span class=\"NormalTextRun SCXW202602414 BCX0\">solutions<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">. His mission is to create user-centric solutions that not only address <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">customer <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">need<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">s<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">\u00a0but also <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">to <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">pro<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">pel <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW202602414 BCX0\">Chooch<\/span><span class=\"NormalTextRun SCXW202602414 BCX0\"> as a <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">category <\/span><span class=\"NormalTextRun SCXW202602414 BCX0\">leader in computer vision. <\/span><\/span><span class=\"EOP SCXW202602414 BCX0\" data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3><strong>Tell me about yourself. <\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">I\u2019m proud to say that I joined Chooch in 2017 as its first employee. I started as a front-end developer and worked on UI design. I eventually transitioned into product management as the product lead for Chooch. The last 6 years have been exciting. My journey has put me on a path of continuous growth as I focus on creating user-centric solutions that align with industry needs. I've not only grown professionally, but it has been gratifying to see how Chooch has grown as it has engineered this exciting technology.<\/p>\r\n\r\n<h3><strong>What\u2019s your favorite tech and non-tech products?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">My favorite tech product is <a href=\"https:\/\/www.adobe.com\/products\/photoshop.html?promoid=RBS7NL7F&mv=other\" target=\"_blank\" rel=\"noopener\">Adobe Photoshop<\/a>. I've been using Photoshop since the early 2000s when I got my first computer. Using Photoshop sparked my interest in software design. Photoshop's versatility, powerful tools, and constant innovation have kept me loyal to it over the years, despite the emergence of alternatives. It's been an indispensable tool throughout my professional journey.<\/p>\r\n<p style=\"padding-left: 40px;\">On the non-tech front, my favorite product is my sling bag. It's a practical and stylish solution that perfectly meets my needs. In case you don\u2019t know, a sling bag is a small, compact one-strap bag that is worn across your body above the waist. Because I tend to carry a lot of items with me daily, I used to carry a backpack. The design and convenience of my sling bag\u2014 a thoughtful gift from my wife\u2014 have made it my go-to choice. It serves as a great reminder that the best products are those that effectively combine form and function to address real-world needs.<\/p>\r\n\r\n<h3><strong>Who is the ideal user for the Chooch AI Computer Vision platform?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">Chooch is designed for any organization interested in a no-code, end-to-end AI solution. We have a broad range of customers from individual freelancers, startups, and Fortune 50 enterprise companies. Chooch is designed to simplify the process of AI deployment with a strong focus on an intuitive interface. Our platform helps users generate datasets, build models, and manage deployments without needing to write a single line of code. It is a perfect fit for anyone seeking a user-friendly, comprehensive <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI computer vision solution<\/a> to streamline their operations or services.<\/p>\r\n\r\n<h3><strong>Describe the process you follow and how you prioritize new features for the Chooch platform?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">Our process for prioritizing new features revolves around a deep commitment to listening to customer feedback. This is the foundation of innovative ideas from our team and staying current with the latest technology trends.<\/p>\r\n<p style=\"padding-left: 40px;\">Given the unique nature of our work developing no-code AI solutions, customer insights offer invaluable perspectives for product enhancements. We take customer feedback seriously. From functionality requests to usage challenges to overall user experience, it helps shape our understanding of where our product stands today, and where it needs to go.<\/p>\r\n<p style=\"padding-left: 40px;\">We value the diverse ideas and creativity within our team. From the developers to the executives, we encourage everyone to share their thoughts without restrictions. This free exchange of ideas has proven to be a meaningful source of inspiration.<\/p>\r\n<p style=\"padding-left: 40px;\">Staying informed about the latest technology and AI trends is crucial for our product's evolution. It helps us anticipate emerging needs and opportunities, ensuring we remain at the cutting edge of technology.<\/p>\r\n<p style=\"padding-left: 40px;\">Our product development process is iterative and continuous, ensuring that Chooch remains relevant, user-friendly, and impactful for our diverse users.<\/p>\r\n\r\n<h3><strong>How has generative AI affected the direction of Chooch\u2019s product roadmap?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\"><a href=\"https:\/\/www.chooch.com\/blog\/4-ways-generative-ai-is-improving-computer-vision\/\">Generative AI<\/a> has had a significant impact on Chooch's product roadmap. As this technology evolves, it opens a new range of possibilities and applications, allowing us to envision and create more sophisticated, automated, and user-friendly solutions.<\/p>\r\n<p style=\"padding-left: 40px;\">Increasing competition in our industry pushes us to explore the boundaries of innovation and continuously improve our platform to stay ahead. We're actively developing new features that leverage the power of generative AI, with the goal of simplifying processes and making our users\u2019 lives easier.<\/p>\r\n<p style=\"padding-left: 40px;\">Generative AI also pushes us to continually reassess our strategy and roadmap. It's important for us to stay adaptable, agile, and ready to pivot as new AI advancements emerge and user needs evolve. <a href=\"https:\/\/www.chooch.com\/blog\/4-ways-generative-ai-is-improving-computer-vision\/\">Generative AI<\/a> isn\u2019t just influencing immediate product features though. It\u2019s also shaping our plans of a more intelligent, dynamic, and accessible AI solution.<\/p>\r\n\r\n<h3><strong>How do you anticipate Chooch AI Vision solutions developing over the next year?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">Given the dynamic nature of the computer vision industry, it is challenging to make definitive predictions about how Chooch's AI <a href=\"https:\/\/www.chooch.com\/platform\/\" target=\"_blank\" rel=\"noopener\">Computer Vision platform<\/a> will develop over the next year. However, I can say with certainty that we'll continue to adapt, evolve, and innovate to ensure our product remains relevant, user-friendly, and technologically advanced.<\/p>\r\n<p style=\"padding-left: 40px;\">Our highly skilled team is adept at rapidly integrating new technologies into our platform. Regardless of whatever new advancements or trends might emerge in the AI and no-code sectors, we are committed to incorporating these in a manner that enhances our user experience and simplifies AI lifecycle management.<\/p>\r\n<p style=\"padding-left: 40px;\">While we will always strive for innovation, I recognize the importance of constantly improving our existing products. Our focus will remain on refining and perfecting our current offerings, based on customer feedback and our own insights, to provide the best solutions for our users.<\/p>\r\n<p style=\"padding-left: 40px;\">Our goal over the next year, and beyond, is to solidify Chooch's standing as a key player in the <a href=\"https:\/\/www.mckinsey.com\/featured-insights\/mckinsey-explainers\/what-is-generative-ai\" target=\"_blank\" rel=\"noopener\">generative AI<\/a> field. We want to offer top-tier, easy-to-use solutions that address real-world challenges.<\/p>\r\n\r\n<h3><strong>What do you do to empower product managers at Chooch?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">Empowering my product managers to speak up and share ideas is very important to me. I believe in fostering a sense of ownership and responsibility in each of them. When a product manager feels truly responsible for their product, it not only inspires a higher level of commitment but also enables them to better manage relationships with engineers, salespeople, and customers.<\/p>\r\n<p style=\"padding-left: 40px;\">I strive to instill an \"evangelist\" mindset in our product managers. Instead of just working for a paycheck (the \"mercenary\" mindset), I want them to believe in our product and its potential to make a difference. I want them to see themselves as champions for their products, advocating for the best outcomes for our users and our company.<\/p>\r\n<p style=\"padding-left: 40px;\">I hold weekly meetings to discuss product improvements, insights from the market, and developments in the product area. These sessions provide a platform for our product managers to share insights, collaborate on solutions, and learn from each other.<\/p>\r\n\r\n<h3><strong>How do you help keep engineers engaged and motivated?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">This involves a deep understanding of an engineer\u2019s mindset and working style. At Chooch, we recognize that engineers thrive in environments where they have the autonomy to apply their creativity and problem-solving skills.<\/p>\r\n<p style=\"padding-left: 40px;\">Instead of micromanaging or dictating how tasks should be done, I prefer to define what needs to be achieved. I outline the objectives, requirements, and expectations, then leave the 'how' part to the engineers. This approach respects their expertise and gives them the freedom to devise their own strategies and solutions.<\/p>\r\n\r\n<h3><strong>What has been your biggest lesson learned as product lead?<\/strong><\/h3>\r\n<p style=\"padding-left: 40px;\">At <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a>, I've experienced first-hand that creating a great product isn't simply about building something we think is good or innovative; it's about crafting a solution that meets the specific needs, preferences, and experiences of our users. This realization has emphasized the necessity of including our customers and partners in the product development process, seeking their insights, feedback, and validation before finalizing any product.<\/p>\r\n<p style=\"padding-left: 40px;\">This approach has made us more attuned to our users and has led to products that truly solve their problems and enhance their operations. It has taught us the value of empathy in product development, ensuring that our focus is always on delivering value to our users, and not just on creating something we think is cool or impressive.<\/p>\r\n\r\n<h3 aria-level=\"2\">Want to meet more of the Chooch team?<\/h3>\r\n<p aria-level=\"3\">Check out the blogs below.<\/p>",
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"post_date": "2023-05-31 22:23:09",
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"post_content": "<span data-contrast=\"auto\">As the world becomes even more digitized, facial recognition is reshaping the business landscape in unprecedented ways, far beyond mere device unlocking or social media interactions. This sophisticated technology, powered by advanced <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">AI and machine learning algorithms<\/a>, enables a wave of innovative applications that businesses across a wide spectrum of industries are rapidly adopting. Whether bolstering security protocols, streamlining operational processes, or enhancing customer experiences, the potential of <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">facial recognition<\/a> technology in business is vast and largely untapped.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">In this blog post, we delve into five remarkable applications of facial recognition technology and how these applications are revolutionizing the retail, hospitality, and security sectors; from personalized marketing campaigns to automated access control systems, all driven by facial recognition. No longer is this technology a futuristic concept, it is a powerful tool transforming businesses today.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3>1. Streamlined security solutions<\/h3>\r\n<span data-contrast=\"auto\">Facial recognition technology is progressively being adopted by businesses, enhancing security protocols, and optimizing operations. This advanced system verifies individuals' identities by comparing facial features from digital images with a stored facial database, offering real-time identification of unauthorized individuals and preventing security breaches. This seamless <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security solution<\/a> eliminates the need for traditional physical tokens like keys or access cards, reducing the chances of lost or stolen credentials\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<a href=\"https:\/\/vpnalert.com\/resources\/facial-recognition-statistics\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">The adoption of facial recognition technology\u00a0 in businesses has seen substantial growth<\/span><\/a><span data-contrast=\"auto\">, with 68% of startups in 2021-2022 focusing on identity verification, and the market is projected to grow at a 15.4% compound annual growth rate through 2028\u200b. <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">Facial recognitions<\/a> use extends beyond security, with many businesses leveraging it to provide personalized services to customers and employees. Unique profiles can be created for everyone based on past behavior, demographics, and other data, allowing businesses to offer better products and services to their target audiences. This technology can also help companies stay ahead of the competition by <\/span><a href=\"https:\/\/www.mindxmaster.com\/how-businesses-are-using-facial-recognition-in-2022\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">pinpointing emerging consumer trends<\/span><\/a><span data-contrast=\"auto\">\u200b\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition also prevents shoplifting in <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail stores<\/a>, supermarkets, and other shopping centers. Facial analysis scans people as they enter the premises, identifying any known criminals based on their previous criminal records. Some companies might even use real-time alerts during thefts so that law enforcement agencies can respond more quickly\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">While larger corporations have primarily been the ones to invest in this technology, as the cost of facial recognition software continues to decline, smaller businesses will start using it to improve their operations\u200b.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><strong>2. Personalized marketing strategies\u00a0<\/strong><\/h3>\r\n<span data-contrast=\"auto\">Marketing campaign success relies heavily on delivering personalized experiences to consumers. <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">Facial recognition technology<\/a> is being increasingly utilized to identify certain customer demographics, such as age, gender, or ethnicity. By analyzing this data, businesses can enhance their marketing strategies to better align with their target audience's needs and preferences.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">In the retail industry, facial recognition technology can provide significant benefits. For instance, it can help <\/span><a href=\"https:\/\/www.entrepreneur.com\/science-technology\/facial-recognition-the-future-of-targeted-marketing\/437103\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">identify loyal customers and offer personalized deals or recommendations<\/span><\/a><span data-contrast=\"auto\"> based on shopping behavior\u200b. It can also be valuable for brick-and-mortar businesses with multiple locations by counting the number of store visitors at each location and analyzing sluggish sales at locations with less foot traffic\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Personalization has become one of the most indispensable marketing strategies among B2B and B2C marketers worldwide. By 2023, the global revenue of customer experience personalization and optimization software will surpass $9 billion (about $28 per person in the US) dollars. Many companies are already spending more than half of their budgets on personalization initiatives. If fact, <\/span><a href=\"https:\/\/www.statista.com\/topics\/4481\/personalized-marketing\/#topicOverview\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">over 60% of online shoppers have stated that brands underdelivering personalized content would impact their brand loyalty<\/span><\/a><span data-contrast=\"auto\">\u200b\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3>3. Enhanced customer experience<\/h3>\r\n<span data-contrast=\"auto\">Facial recognition technology has rapidly evolved in processing passport and mugshot photos, reaching an <\/span><a href=\"https:\/\/webtribunal.net\/blog\/facial-recognition-statistics\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">impressive accuracy level of up to 99.97% in ideal conditions<\/span><\/a><span data-contrast=\"auto\">, s. This accuracy rate can average around 90% in some situations due to factors like aging, makeup, lighting, and the subject's position relative to the camera\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">In the <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">hospitality<\/a> and service industries, facial recognition has the potential to revolutionize the customer experience. Many hotels are investing in this technology, with statistics showing that <\/span><a href=\"https:\/\/webtribunal.net\/blog\/facial-recognition-statistics\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">72% of hotels are likely to adopt facial recognition by 2025<\/span><\/a><span data-contrast=\"auto\">\u200b. This technology can identify returning guests, enabling a swift check-in process and personalized greetings. It can even eliminate the need for conventional room keys, enabling guests to enter their rooms without a physical key and facilitating contactless payment methods at checkout. In China, guests can check into the Marriott by simply allowing a machine to scan their faces\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">In the aviation industry, airlines are utilizing facial recognition to streamline the boarding process, making it faster, more secure, and more efficient.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><span data-ccp-props=\"{}\">4. <\/span><b><span data-contrast=\"auto\">Workforce management<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h3>\r\n<span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">Facial recognition technology<\/a> has been gaining traction as an effective tool for Human Resources (HR) teams to manage their workforces. Its adoption in HR\u00a0 <\/span><a href=\"https:\/\/www.sine.co\/blog\/facial-recognition-workplace-management\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\"> is expected to grow to $9.6 billion by 2027<\/span><\/a><span data-contrast=\"auto\">\u200b\u200b. This technology presents significant advantages for companies, including accurately tracking employee attendance, productivity, and behavior.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition helps eliminate time theft and buddy punching, where employees clock in for colleagues who are not present. This is particularly important, considering <\/span><a href=\"https:\/\/www.shrm.org\/ResourcesAndTools\/hr-topics\/technology\/Pages\/Employers-Using-Biometric-Authentication.aspx\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">since 2018, 17% of companies used biometrics on time clock systems to verify employee identities <\/span><\/a><span data-contrast=\"auto\">\u200b\u200b. The digital database used for check-in can also validate attendance if required, adding a layer of accountability, and reducing unauthorized absences.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition can contribute to a more efficient work environment by automating access to workplaces even during off-hours, thus eliminating the need for exclusive access requests. This feature also drives enhanced security, preventing unauthorized access, and is of paramount importance in increasing <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace security<\/a>\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition can provide critical insights into employee engagement and job satisfaction levels. This data-driven approach allows HR teams to develop targeted talent retention and performance enhancement strategies. For example, by reducing manual tasks like monitoring guest logs, <a href=\"https:\/\/www.chooch.com\/blog\/presentation-attack-prevention-why-facial-authentication-requires-liveness-detection\/\">facial recognition<\/a> can free up reception staff to focus on higher-value activities, boosting their productivity\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3><strong>5. Financial transaction fraud prevention \u00a0<\/strong><\/h3>\r\n<span data-contrast=\"auto\">With the <\/span><a href=\"https:\/\/www.statista.com\/outlook\/dmo\/fintech\/digital-payments\/worldwide\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"auto\">exponential growth of digital transactions<\/span><\/a><span data-contrast=\"auto\">, managing the risk of fraud has become a paramount focus for businesses. The total transaction value in the Digital Payments market is projected to reach $9.46 trillion in 2023. It is expected to grow at 11.80% annually, reaching $14.78 trillion by 2027\u200b. Concurrently, the <\/span><a href=\"https:\/\/www.statista.com\/statistics\/1273177\/ecommerce-payment-fraud-losses-globally\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">losses due to online payment fraud were estimated to be $41 billion globally in 2022, rising to $48 billion by 2023<\/span><\/a><span data-contrast=\"auto\">\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition technology has emerged as a potential solution to tackle this issue. In the United States, 15% to 20% of approximately 11,000 financial institutions use selfie photo imaging in combination with document verification for user authentication. It is estimated that <\/span><a href=\"https:\/\/www.americanbanker.com\/news\/facial-recognition-tech-is-catching-on-with-banks\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">600 to 700 more financial institutions adopted facial recognition technology in the past year<\/span><\/a><span data-contrast=\"auto\">\u200b\u200b. As financial institutions continue to invest more in their digital platforms, the adoption of this technology is expected to increase, especially for online\/mobile banking and online applications.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">The market for services that pair biometrics with document authentication is growing, with the number of providers offering this technology more than doubling from less than 20 in 2018 to over 50 in 2021\u200b. Many digital ID providers have added some form of facial authentication as they seek to provide more powerful technology than fingerprint scans to <\/span><span data-contrast=\"none\">scans to combat spoofing, when a person impersonates a a contract or brand, and minimize friction for people when accessing sites securely. <\/span><span data-contrast=\"auto\">Major tech firms like Apple have been paving the way with new customer experiences in biometrics, making <a href=\"https:\/\/www.chooch.com\/blog\/presentation-attack-prevention-why-facial-authentication-requires-liveness-detection\/\">facial recognition<\/a> a very intuitive experience for many consumers\u200b. The market for this technology, in combination with document ID verification, is projected to surpass $1 billion by 2024\u200b.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h2><b><span data-contrast=\"auto\">Where does facial recognition go from here<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h2>\r\n<span data-contrast=\"auto\">Facial recognition technology's potential in the business landscape is immense. By incorporating it into their operations, businesses can leverage the benefits of enhanced <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security<\/a>, personalized marketing, improved customer experience, effective workforce management, and robust fraud prevention.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Facial recognition is no longer a futuristic concept. It is a reality today that's redefining business operations across the globe. With its wide-ranging applications and the potential to revolutionize business, this technology is undoubtedly an investment that will yield significant returns now and in the future.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>",
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"post_content": "What is the fundamental process for developing <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer Vision (CV) systems?<\/a> <strong>The answer: Image Annotation<\/strong>.\r\n\r\nThis blog walks you through the ABCs of image annotation, starting from the fundamentals and progressing to more advanced concepts. Step-by-step you\u2019ll discover how this process, foundational to Chooch, is teaching<strong> computers what to see.<\/strong> By the end, you will see how Chooch\u2019s commitment to continuous learning and innovation in <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI Vision<\/a> is reshaping the landscape of computer vision.\r\n<h3>A is for Annotation: The basics<\/h3>\r\n<strong>Image annotation<\/strong> is the bedrock of computer vision. It is the meticulous process of identifying, labeling, and classifying various components within an image. This could entail drawing bounding boxes around specific objects, highlighting areas of interest, or even tagging individual pixels. The outcome is a comprehensive visual map from which a machine can learn.\r\n\r\nIn most cases, the annotation task is entrusted to human experts, who bring context, understanding and interpretation to what cameras see. They meticulously label data, creating a rich learning environment for <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning algorithms<\/a>. This labeled data is like the textbook for machine learning models, helping them navigate the complex tasks of object detection, image segmentation, and semantic segmentation.\r\n\r\nWhile annotation might sound simple, it is a labor-intensive process that requires a keen eye for detail and a deep understanding of the subject matter. Why is so much effort poured into this task? The answer lies in the quality of the training data.\r\n\r\nThink of training data as the fuel for your machine learning engine. The cleaner and more refined the fuel, the smoother and more efficiently the engine runs. Similarly, the accuracy and quality of your image annotations directly influence the effectiveness of your trained image models. In other words, the better the annotations, the better your model will interpret new images. Poorly annotated images might lead to a model that misunderstands or misinterprets visual data, which can have significant implications, particularly in critical applications like medical imaging or autonomous vehicles.\r\n<h3>B is for Bounding Boxes: A core technique<\/h3>\r\n<span data-contrast=\"auto\">In image annotation, <strong>bounding boxes<\/strong> are much like the frames we put around our favorite pictures. They provide a way of focusing on specific parts of images. This technique involves drawing a rectangular box around the object we want a machine-learning model to recognize and learn from. Each bounding box comes with a label that denotes what it captures \u2013 anything from a \"cat\" to a \"car\" or a \"person.\"\u00a0 Bounding boxes are a staple in object detection tasks, playing a vital role in various applications.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-ccp-props=\"{}\">\u00a0<\/span><span data-contrast=\"auto\">Take, for instance, self-driving cars. These autonomous vehicles are equipped with object detection models that have been trained on images annotated with bounding boxes. These boxes serve as guides, helping the model identify key environmental elements like pedestrians, other vehicles, and road signs. This understanding is crucial for the safe and efficient operation of the vehicle.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-ccp-props=\"{}\">\u00a0<\/span><span data-contrast=\"auto\">However, like any other tool, bounding boxes have strengths and weaknesses. One of its major strengths is simplicity: they are straightforward to understand and implement. This makes them an ideal choice for many object detection tasks. They are also computationally efficient, a valuable attribute in real-time applications where speed is critical.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n\r\n<span data-ccp-props=\"{}\">\u00a0<\/span><span data-contrast=\"auto\">On the other hand, bounding boxes do have certain limitations. They are less effective when dealing with objects that do not conform to a rectangular shape, as the box may include irrelevant background \"noise.\u201d They struggle to differentiate between overlapping objects, as the boxes may encompass more than one object causing ambiguity.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span>\r\n<h3>C is for Classes and Categories: Organizing annotations<\/h3>\r\n<strong>Organizing annotations<\/strong> into classes and categories plays a vital role in training <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning models<\/a> for image annotation tasks. Each labeled item in image annotation belongs to a specific class or category, which can encompass a diverse range of objects or concepts. From concrete objects like \"dogs\" and \"cars\" to abstract ideas like \"happy\" or \"dangerous,\" the choice of classes depends on the specific computer vision task.\r\n\r\nBy organizing annotations into classes, we enable the machine learning model to recognize patterns associated with each class. This allows the model to learn and understand the characteristics and features of specific objects distinguishable in one class. As a result, when faced with new, unseen images, the model can accurately predict its appropriate class.\r\n\r\nSelecting the right classes is crucial for successfully training machine learning models. The granularity and level of detail in defining classes can significantly impact the model's performance. Fine-grained classes provide a more specific representation, enabling the model to capture intricate patterns and nuances within the data. Coarse-grained classes offer a more generalized object perspective, which can be advantageous when dealing with large-scale datasets or diverse image collections.\r\n\r\nIn addition to classes, organizing annotations into meaningful categories further enhances training. Categories provide a hierarchical structure that groups similar classes, facilitating an understanding of relationships and dependencies between annotations. This hierarchical organization helps create a cohesive framework that aids in training models for complex image annotation tasks.\r\n<h3>D is for Deep Learning: The power behind computer vision<\/h3>\r\n<strong>Deep learning<\/strong>, a subset of <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a>, has emerged as a powerful technology that drives most modern computer vision applications. At the heart of deep learning for computer vision lies Convolutional Neural Networks (CNNs), a specialized neural network designed for image-processing tasks. With their ability to automatically learn and extract features from raw pixel data, CNNs have revolutionized the field of computer vision.\r\n\r\nOne of the key requirements for deep learning models, including CNNs, is a large amount of annotated data. Annotated data refers to images labeled with precise and accurate annotations, such as bounding boxes, segmentation masks, or keypoint coordinates. These annotations provide ground truth information to the model, allowing it to learn and generalize from the labeled examples.\r\n\r\nThe quality and thoroughness of the annotations play a crucial role in the deep learning model's performance. When images are meticulously annotated, capturing detailed information about the objects or concepts of interest, the model gains a more comprehensive understanding of the data. This enables the model to learn intricate patterns and make more accurate predictions when presented with new, unseen images.\r\n<h3>E is for Evaluation: Assessing model performance<\/h3>\r\nOnce a machine learning model is trained, evaluating its <strong>performance<\/strong> is a critical step in understanding its effectiveness and identifying areas for improvement. Evaluation allows us to assess how well the model generalizes new, unseen data and provides insights into its strengths and limitations. One common approach to evaluating model performance in computer vision tasks is using a separate set of annotated images known as the validation set.\r\n\r\nThe validation set is distinct from the training set and serves as an unbiased sample of data that the model has not yet seen during training. By evaluating the model on this independent set of images, we can obtain a realistic estimation of the model\u2019s performance with unseen data.\r\n\r\nDuring evaluation, the model's predictions on the validation set are compared to the actual annotations or ground truth labels. This comparison enables the calculation of various evaluation metrics that quantify various aspects of the model's performance. Some commonly used metrics in computer vision evaluation include precision, recall, and the F1 score.\r\n\r\nEvaluation is an iterative process, and it is common to fine-tune models based on the insights gained from the evaluation results. This may involve adjusting model parameters, exploring different training strategies, or collecting additional annotated data to address specific challenges identified during the evaluation.\r\n\r\nBy continuously evaluating and refining the model's performance, work can progress in developing robust and reliable computer vision systems. Effective evaluation enables informed decisions, optimized model performance, and ultimately AI systems that meet the highest accuracy, reliability, and usability standards.\r\n<h3>F is for Future: AI-assisted image annotation<\/h3>\r\nLooking toward the future, <strong>AI-assisted image annotation<\/strong> emerges as a promising development area that can revolutionize the annotation process. By leveraging the power of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a>, we can reduce the workload for human annotators, enhance annotation consistency, and accelerate the overall annotation process.\r\n\r\nAI-assisted image annotation involves using <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning algorithms<\/a> and computer vision techniques to pre-annotate images automatically. These AI models can be trained on large, annotated datasets, learning to recognize and label objects, regions, or concepts within images. Automating the initial annotation step significantly reduces the burden on human annotators, enabling them to focus on more complex or ambiguous cases that require human expertise.\r\n\r\nThe advantages of AI-assisted annotation go beyond time savings. With the assistance of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a>, annotation consistency can be greatly improved. Human annotators may introduce inconsistencies or subjective biases in their annotations, but AI algorithms can provide a more objective and standardized approach to labeling images. This ensures more annotation consistency, crucial for training accurate and reliable machine learning models.\r\n<h3>Where will the ABCs take you?<\/h3>\r\nImage annotation is a vital process for computer vision, and adhering to the ABCs of image annotation is crucial for accurate and reliable results. At Chooch, we understand the significance of meticulous annotation, whether drawing precise bounding boxes, organizing classes, or evaluating model performance. By adhering to these principles, we ensure annotation quality, consistency, and relevance, enabling the development of robust and effective <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning models<\/a>.",
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"post_content": "<p aria-level=\"1\"><span data-contrast=\"auto\">You are probably aware of how more sophisticated data analytics is transforming the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail industry<\/a>. Today across all customer touch points, artificial intelligence is driving the collection of massive amounts of data, and the latest advancements in <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> are defining a modern approach to real-time store management. <\/span><\/p>\r\n<p aria-level=\"1\"><span data-contrast=\"auto\">AI-powered computer vision is enabling retailers to monitor the most subtle images, or changes in images, in any video stream using their existing in-store cameras. Retailers can not only have this modern technology monitor security cameras, but also monitor stockouts, sending instant alerts to store managers when shelves are not properly stocked or could be better stocked based on shopper behavior. <\/span><span data-ccp-props=\"{"134245418":true,"134245529":true,"201341983":0,"335559738":240,"335559739":0,"335559740":259}\">\u00a0<\/span><\/p>\r\n<span data-contrast=\"auto\">Re<\/span><span data-contrast=\"auto\">tailers rely heavily on data to deliver both consumer and supply chain data deriving insights never before available to them. <\/span>\r\n\r\n<strong>They are prioritizing investment in AI for:\u00a0<\/strong>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Real-time data and insights:<\/span><\/b><span data-contrast=\"auto\"> They are leveraging massive amounts of data from edge devices to help make real-time decisions and improve operations.<\/span><span data-ccp-props=\"{"201341983":0,"335559685":720,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Forecasting Changing Demand:<\/span><\/b><span data-contrast=\"auto\"> Retailers are relying heavily on real-time data to anticipate shifts in demand for countless SKU\u2019s as they improve point of purchase displays and cater to always evolving customer behavior. <\/span><span data-ccp-props=\"{"201341983":0,"335559685":720,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Personalization:<\/span><\/b><span data-contrast=\"auto\"> Data at scale provides retailers with deeper customer insights to better improve everything from product placement to marketing personalization.<\/span><span data-ccp-props=\"{"201341983":0,"335559685":720,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><b><span data-contrast=\"auto\">Data sharing:<\/span><\/b><span data-contrast=\"auto\"> Retailers are creating more transparency across their entire value chains, providing valuable insights to suppliers, distributors, and partners. The best part is that they reduce costs and improve service.<\/span><span data-ccp-props=\"{"201341983":0,"335559685":720,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>Using AI for shelf and stockout management<\/h3>\r\n<span data-contrast=\"auto\">Poor shelf management and stockouts can dramatically reduce sales and have a direct impact on operational costs, employee productivity, and customer satisfaction. Most often, this is simply a result of poor visibility into store settings and shopping behavior.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> is delivering meaningful benefits to retailers. It provides<\/span> <span data-contrast=\"auto\">retailers with real-time visibility and insights into store settings, signage, and shelf displays. It delivers advanced monitoring and benchmarking of out-of-stock instances and product merchandising. <a href=\"https:\/\/www.chooch.com\/\">AI-powered computer vision technology<\/a> provides the infrastructure for more automated, real-time stock management, dramatically improving store operations. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>Measurable ROI of retail AI for shelf management<\/h3>\r\n<img class=\"aligncenter wp-image-5152 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/05\/retail-stockout-stats.png\" alt=\"Retail Stockout Stats\" width=\"1000\" height=\"215\" \/>\r\n<h3 aria-level=\"2\">How to address on-shelf availability with computer vision<\/h3>\r\n<p aria-level=\"2\"><span data-contrast=\"auto\">Foundational technologies such as barcode scanning, ID scanning, and optical character recognition (OCR) have paved the way for improving shelf-monitoring. These technologies, combined with data from live-streaming cameras, are used to power computer vision models that can detect anomalies in product placement on shelves in real-time with minimal human intervention. <\/span><span data-ccp-props=\"{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3 aria-level=\"2\"><img class=\"wp-image-5151 size-full aligncenter\" src=\"\/wp-content\/uploads\/2023\/05\/retail-stockout-products-on-shelves.jpg\" alt=\"Retail Stockout\" width=\"900\" height=\"394\" \/><\/h3>\r\n<h3 aria-level=\"2\">Building computer vision models to monitor stock out<\/h3>\r\n<span data-contrast=\"auto\">Creating computer vision models to deliver insights that retailers need requires a large dataset of images of each product SKU. For example, every bag of potato chips and every flavored variety, i.e., sour cream and onion, cheddar cheese, etc., requires unique images. This compilation of images builds the dataset that teaches the <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision model<\/a> what each bag of chips looks like and the subtleties between each so that it can understand what to look for on shelves and make predictions based on what it sees. Think of this as \u201cteaching\u201d computers what to see. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>How synthetic data is helping retailers<\/h3>\r\n<span data-contrast=\"auto\">Grocery stores frequently have 10,000+ items on a given shelf. This product volume makes gathering a large enough dataset of images to train the model incredibly tedious and time consuming. To speed up the process, rather than taking a photo of each product, synthetic data toolsets can be used today to generate images of each item based on their barcodes or UPCs. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Manufactured, or \u201csynthetic,\u201d features like object occlusions, diverse backgrounds, lighting, rotations, noise, and blurring are proactively added to copies of images. This process creates a more robust and more accurate data set. It produces computer vision models that can see a much broader variety of product scenarios and are more effective at detecting specific objects or products. Using both real and synthetic data, the models can create a more robust analytical visualization of every product, on every shelf, in every location within the store for maximum visibility into shelf inventory. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Now when stockouts occur, products are out of place, or misaligned, automated alerts can be sent to staff in real-time for them to investigate and restock quickly.<\/span> <span data-contrast=\"auto\">Automating more of the visual inspection process reduces labor costs and placement errors. Fast responses to stockouts reduce lost sales while improving customer satisfaction and the overall shopping experience. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>Applications of AI for real-time shelf management<\/h3>\r\n<p aria-level=\"3\"><b><span data-contrast=\"auto\">Stock replenishment<\/span><\/b><span data-ccp-props=\"{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}\">\u00a0<\/span><\/p>\r\n<span data-contrast=\"auto\">By collecting real-time data on inventory levels and stock movement, retailers can make faster and smarter decisions about how and when to restock shelves, reduce overstocking, and optimize pricing to maximize profit margin. Minimizing under-stocking and dead stock holding costs not only saves money but also streamlines operations and increases profitability.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<p aria-level=\"3\"><b><span data-contrast=\"auto\">Planogram design<\/span><\/b><span data-ccp-props=\"{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}\">\u00a0<\/span><\/p>\r\n<span data-contrast=\"auto\">AI-powered data and insights assist retailers with optimizing product placement by putting the most popular products in shelf positions based on shopper buying behavior. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> finds patterns in data and aggregates it into heat maps, analyzing customer dwell times and store flow through. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<b><span data-contrast=\"auto\">Contractual compliance<\/span><\/b><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Compliance audits can be time consuming for retailers. Pre-defined <\/span><span data-contrast=\"none\">compliance metrics, such as on-shelf availability (OSA), share of shelf, and shelf positioning<\/span><span data-contrast=\"auto\"> are all part of service level agreements (SLA) between <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retailers and suppliers<\/a>. If retailers are found to be violating SLA\u2019s by displaying too few products or by positioning products in the wrong shelf locations, contractual penalties and even contract terminations are possible. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><span data-ccp-props=\"{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}\">\u00a0<\/span>\r\n<h3 aria-level=\"2\">Delivering a frictionless shopping experience with AI<\/h3>\r\n<span data-contrast=\"none\">Brick and mortar retailers continue to face customer acquisition challenges and increasing brand loyalty. Despite the continued growth of online shopping, nothing replaces the experience of walking into a store, browsing the shelves, and finding the product customers want to buy.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">Delivering a <\/span><span data-contrast=\"none\">frictionless shopping experience is critical to keeping customers coming back, <\/span><span data-contrast=\"none\">but low on-shelf availability (OSA) and a high number of out-of-stock events can impact that experience. <\/span><span data-contrast=\"none\">Chances are if the inventory isn\u2019t available to the customer to buy immediately, they have most likely ordered it from an online retailer before they have left the store.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<p aria-level=\"1\"><span data-contrast=\"none\">Traditional methods of managing OSA just do not compare to modern technology. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> is <\/span><span data-contrast=\"auto\">changing how retailers are monitoring their <\/span><span data-contrast=\"none\">shelf space. It <\/span><span data-contrast=\"none\">provides the data retailers need to <\/span><span data-contrast=\"auto\">better manage product availability, shelf design, pricing, and product placements. Ultimately, It\u2019s better enabling retailers to <\/span><span data-contrast=\"none\">streamline operations, reduce costs, and <\/span><span data-contrast=\"none\">deliver exceptional customer experiences.<\/span><\/p>\r\n<p aria-level=\"1\"><a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/solution-brief-chooch-retail-ai-vision-solutions.pdf\" target=\"_blank\" rel=\"noopener\">Download our solution brief<\/a> to learn more about Chooch's AI-powered computer vision solutions. Or <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">schedule a demo<\/a> to see how our AI Vision models can help you.<\/p>",
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"post_content": "<span data-contrast=\"auto\">No doubt, one of the<\/span><span data-contrast=\"auto\">\u00a0<\/span> <span data-contrast=\"auto\">most dramatic machine learning trends of 2023 i is generative and conversational AI. Think ChatGPT. <\/span><span data-contrast=\"auto\">Chooch recently released <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>\u2122 <\/span><span data-contrast=\"auto\">which is an exciting new type of generative AI that combines image recognition with Large Language Models (LLM) to provide the ability to chat with images to derive more detailed, accurate insights about the image<\/span><span data-contrast=\"auto\">.<\/span><span data-contrast=\"auto\">\u00a0\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Using text prompts, people can ask images questions, and they will answer back with details of what is in the picture. For example, if you are looking at a picture of a fruit bowl, you can ask whether there are bananas in it, how many, what color, and so on. This technology has far reaching applications across many industries including its use for content moderation, image metadata and caption generation \u2013 even for fire detection.\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Yet like other transformative technology, the newer technology is only as good as the talent building it. One of the challenges of developing the LLMs that power generative AI technology is finding expert machine learning engineers to build it.\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"auto\">Meet Ahmet Kumas, lead Machine Learning Engineer, who is heading the team building Chooch\u2019s exciting new image-to-text generative <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI technology<\/a> called <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>.<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h2><span class=\"TextRun MacChromeBold SCXW258349032 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW258349032 BCX0\">Experienced<\/span><span class=\"NormalTextRun SCXW258349032 BCX0\">. <\/span><span class=\"NormalTextRun SCXW258349032 BCX0\">Skillful. <\/span><span class=\"NormalTextRun SCXW258349032 BCX0\">Acutely f<\/span><span class=\"NormalTextRun SCXW258349032 BCX0\">ocused.\u00a0<\/span><\/span><span class=\"EOP SCXW258349032 BCX0\" data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/h2>\r\n<h3><img class=\"wp-image-5045 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/05\/ahmet-kuman-candid.jpg\" alt=\"Ahmet Kuman\" width=\"768\" height=\"571\" \/><\/h3>\r\n<h2><\/h2>\r\n<h3>Tell us a little bit more about yourself.<span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">I currently live in Antalya, a city situated in Southern Turkey<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\"> renowned for its abundance of orange trees and refreshing sea breeze. I grew up in Isparta, which is located not far from Antalya. Before Chooch, I\u2019ve had the opportunity to work at other AI-specific solution providers including Amani, Sampas, and Medvion.\u00a0<\/span><span data-ccp-props=\"{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">At Amani, I was responsible for the development of \"Know Your Customer\u201d models, particularly in biometric and document data processing. While at Sampas, I contributed to solutions used in the European Union by creating AI models for smart cities and predictive water management.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">At Medvion, I led a team of four ML experts in developing early disease diagnosis models using CT and MRI images. I was particularly proud of the work we did with pneumonia research. <\/span><span data-contrast=\"auto\">Our team worked on a vision-based solution that quantified the three-dimensional volume of damage within the lungs.<\/span> <span data-contrast=\"auto\">The goal was to assess pulmonary damage in order to enhance the treatment process and optimize medication prescription. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">In my free time, I enjoy various sporting activities, particularly board sports. I\u2019m a huge snowboarding\u00a0fan. I\u2019m also a black belt Taekwondo athlete and a lover of camping, hiking, and cross-motorcycle riding which allow me to connect with nature.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>How did you first become interested in machine learning?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">My passion for AI began when I realized the positive impact it could have on healthcare research and the vast number of applications for AI. During my college years, I engaged in several AI research projects, specifically around epilepsy and pneumonia, and contributed to early-stage clinical trials alongside renowned doctors in Turkey.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">My involvement in these projects resulted in multiple awards for proof-of-concept (PoC) products. Witnessing firsthand how <a href=\"https:\/\/www.chooch.com\/\">AI and technology<\/a> can improve patient outcomes and simplify their lives was a transformative experience that solidified my commitment to pursue a career in AI. Today, I leverage AI to build solutions that are helping healthcare organizations drive efficiency, generate more insights, and drive earlier and more accurate diagnoses across a range of diseases. <\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>What do you like most about your job?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">As the Lead ML Engineer at Chooch, I take immense pride in developing scalable and iterative AI products that can be effectively deployed in production environments. Our company is a pioneer in the industry, offering end-to-end solutions from data annotation and model training to out-of-the-box vision models and edge deployment.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">I've worked to develop several models including Personal Protective Equipment (PPE), Fall Detection, Dangerous Activity Detection (e.g., fire, uncontrolled weapons, and dangerous zone management<\/span><span data-contrast=\"auto\">)<\/span><span data-contrast=\"auto\">. It's exciting to know these models are being applied across enterprises in many industries and in some, saving lives. I am proud to be part of the team that builds these solutions and makes them accessible to the larger ecosystem.<\/span><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>Tell us about your work with ImageChat<span data-contrast=\"auto\">\u2122 <\/span> at Chooch.<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a> is a state-of-the-art AI model that enables users to communicate with images and extract relevant information from them. One of the key advantages of ImageChat is its ability to quickly and systematically create an AI model through prompts covering a wide range of subjects. For more specific use cases, the object detector in Chooch Vision Studio can be used to localize objects and refine <\/span><span data-contrast=\"auto\">ImageChat<\/span> <span data-contrast=\"auto\">results.<\/span> <span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\" data-contrast=\"auto\">Training a large language model is a computationally intensive task that requires large-scale datasets and significant computing power. Some of the critical challenges faced when training LLMs are the iterative training, collection, and generation of the appropriate datasets. Developing and training LLMs requires a rigorous, systematic approach to ensure optimal performance and accuracy. <\/span><\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Go Chooch ML team!\u00a0\u00a0<\/strong><\/p>\r\n\r\n<h3>\u00a0What challenges do you find with developing generative AI apps or models?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span class=\"TextRun SCXW39832698 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW39832698 BCX0\">The development of computer vision models presents <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">numerous<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> challenges that must be addressed <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">with <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">a systematic approach. This typically involves a series of steps, including data collection, model architecture design, data annotation, model training, and evaluation. The process is dynamic, as models must continually adapt to different environments <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">to<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> achieve <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">optimal<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> performance. To <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">facilitate<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> this adaptation, an <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">additional<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> layer known as active learning is often incorporated, which iteratively refines the model through multiple cycles of the <\/span><span class=\"NormalTextRun SCXW39832698 BCX0\">steps<\/span><span class=\"NormalTextRun SCXW39832698 BCX0\"> until the desired performance is achieved. The sensitivity of this process underscores the importance of clear and thorough design and execution of each step.<\/span><\/span><\/p>\r\n\r\n<h3><span data-ccp-props=\"{"201341983":0,"335559685":0,"335559739":160,"335559740":259}\">\u00a0<\/span>What do you see as the next big thing in AI?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span class=\"TextRun SCXW32236640 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW32236640 BCX0\">The advent of generative models is transforming the landscape of AI production, with these models becoming increasingly larger in scale. However, their use requires significant computing power, leading to a focus on <\/span><span class=\"NormalTextRun SCXW32236640 BCX0\">optimizing<\/span><span class=\"NormalTextRun SCXW32236640 BCX0\"> future models for production deployment. Smaller models with higher accuracy will become a key point of competition in the LLM space. Additionally, there is growing interest in developing AI models that emulate human experience and incorporate collective knowledge. By merging such diverse information sources into one large AI engine, the potential for expanding the capabilities of AI models is significant.<\/span><\/span><\/p>\r\n\r\n<h3 aria-level=\"3\">Want to meet more of the Chooch team?<\/h3>\r\n<span data-contrast=\"auto\">Check out other Chooch leaders below. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>",
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"post_date": "2023-05-15 19:59:05",
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"post_content": "Images of burning wildland areas across the world are now familiar sights in the media especially during the summer and fall. In the United States alone, there is an <a href=\"https:\/\/www.epa.gov\/climate-indicators\/climate-change-indicators-wildfires#:~:text=Since%201983%2C%20the%20National%20Interagency,year%20(see%20Figure%201).\">average of over 70,000 wildfires each year<\/a>, but worse yet, studies have shown that the <a href=\"https:\/\/www.nifc.gov\/fire-information\/statistics\/wildfires\">annual acreage burned is over 60% larger than decades ago<\/a>. Wildfires are becoming more devastating over time, and federal, state, and local agencies are struggling to keep up.\r\n\r\nDue to the land management complexities across government agencies, understanding who will fund this specific kind of disaster response is an enormous challenge for legislators and emergency agencies. Often state reimbursement from federal funds for wildfire recovery.\r\n\r\nA <a href=\"https:\/\/www.brookings.edu\/articles\/inviting-danger-how-federal-disaster-insurance-and-infrastructure-policies-are-magnifying-the-harm-of-climate-change\/\">2021 study by the Brookings Institution<\/a> estimated that $7 from the federal government went to disaster recovery for every dollar in fire preparedness spending. Any way you look at it, early wildfire \u00a0detection and responsiveness is the most prudent way to being minimizing the \u00a0i fiscal, environmental, and human expense toll.\r\n\r\nThe National Institute of Building Sciences estimates that every dollar invested in <a href=\"https:\/\/www.nibs.org\/reports\/natural-hazard-mitigation-saves-2019-report\">wildfire mitigation saved $3 in post-disaster recovery costs<\/a>. Investment in immediate response protocols and tools that leverage the latest technologies are foundational for early detection and of fires. We sit at a unique inflection point where computers are smaller and more powerful than ever. Cameras can see greater distances with higher resolution in less light. AI computer vision is far more advanced and quickly becoming mainstream thanks to tools like Chat-GPT. Use of this technology working alongside the men and women in fire protection is gaining wider adoption.\r\n<h3><strong>The case for computer vision as an essential investment in wildland fire detection<\/strong><\/h3>\r\nThe subset of AI called <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is easily identifying objects, people, and activities in images derived from live video streams. Such intelligence can \u201csee\u201d images and understand their context by leveraging computing frameworks like deep neural networks and transformers. In the past, such technology has been less mobile. The electrical, processing, and networking power was insufficient to derive real value from computer vision algorithms in ultra dynamic, real-world settings.\r\n\r\nToday, these algorithms can be deployed from cameras on cell towers, unmanned aerial vehicles, or drones to provide always-on monitoring of wide expanses of remote geography. Computer vision AI platforms, like <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI Vision<\/a>, are optimized for remote deployments out-of-the-box and maintain highly accurate fire detection algorithms that can distinguish smoke from clouds, fog, and other atmospheric conditions.\r\n\r\nToday, computer vision for <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">wildfire detection<\/a> is no longer an interesting experiment. It\u2019s a robust and reliable technology capable of running on existing cameras and hardware and accurately detecting wildfires across thousands of cameras. It\u2019s being used right now on <a href=\"https:\/\/www.youtube.com\/watch?v=247u2_vpPJI\">over 2,000 cameras in California<\/a>. In less than a second, smoke is accurately identified across a vast plain of imagery with false positives numbering in the single digits out of thousands of images every minute.\r\n\r\nNow is the time to make computer vision a foundational capability in any wildfire mitigation strategy.\r\n<h3 aria-level=\"2\"><b><span data-contrast=\"none\"><img class=\"aligncenter wp-image-4767 size-full\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/05\/smoke-plume-fire-detected.jpg\" alt=\"Smoke Plumes with Wildfires Detected\" width=\"1006\" height=\"319\" \/><\/span><\/b><\/h3>\r\n<h3><strong>Key components of a computer vision system for wildfire detection<\/strong><\/h3>\r\nA <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">computer vision system for wildfire detection<\/a> typically consists of three main components: image acquisition and processing, feature extraction and pattern recognition, and machine learning algorithms. Let's take a closer look at each of these components.\r\n\r\n<strong>Image acquisition and processing<\/strong>\r\n\r\nImage acquisition and processing \u00a0involves capturing images of the environment using cameras and then processing those images to extract relevant information. Cameras can be placed on towers, drones, or other platforms to capture images at regular intervals. Once the images are captured, they can be processed using image processing technology to enhance their quality and extract features that are relevant for wildfire detection.\r\n\r\n<strong>Feature extraction and pattern recognition<\/strong>\r\n\r\nFeature extraction and patter recognition involves using algorithms to extract specific features from images, such as color, texture, and shape. These features are then used to identify patterns that\u00a0 indicate a wildfire. For example, algorithms may be trained to recognize the shape of a smoke plume or the color of flames, which would indicate the presence of a fire.\r\n\r\n<strong>Machine learning algorithms for wildfire detection<\/strong>\r\n\r\nThe third component of a <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">computer vision system for wildfire detection<\/a> involves machine learning algorithms. These algorithms use patterns identified in the image data to predict whether a wildfire is present or not. Machine learning algorithms can be trained on a large dataset of images that include both wildfire and non-wildfire images, enabling them to recognize patterns that are indicative of a wildfire and make more accurate predictions\r\n<h3><strong>Real-world applications of computer vision in wildfire detection<\/strong><\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> has several real-world applications that can help detect wildfires. These include satellite imagery and remote sensing, drone-based monitoring systems, and ground-based camera networks.\r\n\r\n<strong>Satellite imagery and remote sensing<\/strong>\r\n\r\nSatellite imagery and remote sensing technologies can provide high-resolution imagery of vast areas, enabling authorities to monitor wildfires across a wider geography. Some satellite imagery providers can even detect wildfires in near real-time, allowing authorities to respond quickly before the situation escalates.\r\n\r\n<strong>Drone-based monitoring systems<\/strong>\r\n\r\nDrone-based monitoring systems allow for rapid deployment of cameras to areas where fires are likely to occur, reducing response times and improving situational awareness. Drones can also be equipped with thermal cameras that detect heat signatures, allowing them to identify fires that may not be visually visible from the air.\r\n\r\n<strong>Ground-based camera networks<\/strong>\r\n\r\nGround-based camera networks can also be used for <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">wildfire detection<\/a>. These systems typically consist of a network of cameras placed at tactical locations. These cameras can capture high-resolution images at regular intervals and then transmit those images to a central processing system for analysis.<b><\/b>\r\n<h3 aria-level=\"2\"><b><span data-contrast=\"none\">Overcoming challenges of using computer vision for wildfire detection<\/span><\/b><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/h3>\r\n<p aria-level=\"2\"><span data-contrast=\"none\">Using computer vision for wildfire detection is not a new idea, but there were several core challenges that previously plagued its application at scale. Fortunately, forward momentum in hardware and algorithmic design have made computer vision both a trustworthy and robust solution ready for the field. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n<p aria-level=\"3\"><strong>Image quality and resolution\u00a0<\/strong><\/p>\r\n<span data-contrast=\"none\">In the past, image quality and resolution of images used for wildfire detection have sometimes impacted the accuracy of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI systems<\/a>. Because cameras are exposed to the elements, cracked lenses and variable lighting made it difficult to distinguish fire and smoke from clouds, haze, or fog. A key revolution that has solved for these variables is the popularization of \u201ctransformer\u201d models or self-attention models. Such AI algorithms specialize in maintaining positional context in an image, so the placement of fire and smoke can today be reliably deciphered amongst other occlusions or objects.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<p aria-level=\"3\"><strong>False positives and negatives\u00a0<\/strong><\/p>\r\n<span data-contrast=\"none\">False positives and negatives have been challenges in using <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> for wildfire detection. False positives occur when the system detects a fire when no fire exists, while false negatives occur when the system fails to detect a real fire. Modern fire detection algorithms err on the side of caution and likely trigger more false positives than negatives. Because AI is only as good as its training data, it's imperative to train AI with similar vantage points as the real-world applications. The latest AI models, <\/span><span data-contrast=\"none\">such as <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>\u2122 by Chooch<\/span><span data-contrast=\"none\">, use billions of parameters to fine-tune varieties of training data, ensuring that the models account for the maximum number of edge scenarios.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<p aria-level=\"3\"><strong>Scalability and real-time processing\u00a0<\/strong><\/p>\r\n<span data-contrast=\"none\">Finally, scalability and real-time processing can be challenging for computer vision systems used for wildfire detection. The sheer volume of data generated by cameras can be overwhelming, requiring significant processing power and storage capacity. In addition, the system must be able to process the data quickly and in real-time, alerting authorities to the presence of a fire as quickly as possible, b<\/span><span data-contrast=\"none\">ut today\u2019s computers are more powerful than ever in history. Graphics processing units, or GPUs, are equipped with over 50 billion transistors. Simply, hardware is more mobile and can handle more logic than previously imagined. Also, new data management paradigms adopted by telecom providers with the roll-out of 5G communications has optimized the way only critical data is sent over a network making close-proximity processing a reality for standard hardware tools. <\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3><strong>Use case studies and success stories for AI in wildfire detection<\/strong><\/h3>\r\nThere are several examples of successful AI-powered wildfire detection systems which have been deployed today. For example, <a href=\"https:\/\/wifire.ucsd.edu\/firis\">the Fire Integrated Real-Time Intelligence System (FIRIS)<\/a> uses real-time data from sensors and artificial intelligence algorithms to detect wildfires in California. The Rapid <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">Wildfire Detection<\/a> Using Satellite Data (RAPID) system uses satellite imagery and AI algorithms to detect wildfires in real-time.\r\n\r\nOther examples include <a href=\"https:\/\/firms.modaps.eosdis.nasa.gov\/map\/#d:24hrs;@0.0,0.0,3z\">the NASA Fire Information for Resource Management System (FIRMS) web-based tool<\/a> and UC San Diego\u2019s project for real-time fire detection, <a href=\"https:\/\/alertcalifornia.org\/\">ALERTCalifornia<\/a>.\r\n<h3>Collaborative efforts and partnerships<\/h3>\r\nCollaborative efforts between government agencies, research institutions, and private companies are essential for developing effective wildfire detection and prevention strategies. By sharing data and expertise, these groups work together to create comprehensive solutions that are tailored to specific regions and environments. By leveraging the power of AI, computer vision, and fire detection technologies, we can protect our communities and natural resources from the threat of wildfires.\r\n\r\nChooch has been active in this space since 2019 and has state-of-the-art, <a href=\"https:\/\/app.chooch.ai\/app\/ready-now-models\/\">ReadyNow<span data-contrast=\"none\">\u2122<\/span> computer vision models<\/a> trained to accurately identify smoke and fire in any wilderness image. <a href=\"https:\/\/www.chooch.com\/contact-us\/\">Get in touch with us<\/a> to learn more about <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">Chooch wildfire detection solutions<\/a>.",
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"post_date": "2023-05-15 10:47:23",
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"post_content": "For the first time in history, our society sits at the convergence of high-powered compute devices capable of running the world\u2019s most advanced AI algorithms in disconnected or environmentally rugged environments. This paradigm shift in computing is referred to as Edge AI, and it's unlocking a myriad of insights across industries such as <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a>, retail, and telecom because it unlocks streams of data closest to the point of inception. Much like the cloud computing revolution of the last decade that paved the way to \u201cbig data\u201d related problems and innovations, edge computing is becoming the gateway to intelligent processing for side-by-side collaboration with end users.\r\n<h3>Understanding the types of edge AI<\/h3>\r\n<strong>What is edge AI?<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> refers to the use of artificial intelligence algorithms on devices located close to the source of data generation. This is a contrast to traditional AI, performed in a centralized location, such as cloud servers. By processing data locally, edge AI reduces the amount of data needed to be transferred for processing, making it faster and more efficient. In locations where internet bandwidth is limited \u2013 remote areas or mobile networks \u2013 there is a growing need for devices to be able to process data quickly and efficiently without relying on a cloud server.\r\n\r\n<strong>What is drone AI?<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-security-robotics-and-drone-ai-for-the-security-industry\/\">Drone AI<\/a> is the use of artificial intelligence algorithms to control unmanned aerial vehicles (UAVs) or drones. Drones are equipped with cameras and sensors that allow them to capture data about their surrounding environment, which is then processed by AI algorithms to make better informed decisions about the best course of action. The benefit of using drones is that data can be collected from areas that are difficult or dangerous for humans to access, for example, inspecting oil rigs, monitoring wildlife, and even delivering medical supplies to remote areas.\r\n\r\n<strong>What is edge computer vision?<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Edge computer vision<\/a> refers to applying AI to video streams for real-time inference of operational and business intelligence pushed from core compute services to low-resource edge hardware. There are numerous computer vision techniques running at the edge, including image, action, and pattern recognition; object counting and classification; and <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a>. In all cases, these computer vision techniques run embedded within a non-IT asset, such as a device endpoint (like a smart camera), a gateway device, or on a local edge server. As data is fed from devices into these models, they continue to learn and can adapt to changing conditions. By automatically analyzing vast amounts of data, computer vision algorithms can identify patterns and trends that might not be apparent to human analysts. In identifying these anomalies, most analytics are performed on the edge, and real-time alerts are generated to initiate further action.\r\n<h3>Using edge AI for security<\/h3>\r\nWith security risks becoming more sophisticated and, in some cases, catastrophic, security measures today are evolving beyond simple alarms, guards, and surveillance cameras. Technology is advancing common security systems and edge devices to be more sophisticated, utilizing advanced sensors, facial recognition technology, and computer vision. <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-edge-device\/\">Computer vision models run on edge devices<\/a> are trained on the data collected to identify different types of security threats, including crowds, loitering, weapons, and vehicles. For example, video footage can be analyzed for a crime as it happens; however, you may not be able to identify the perpetrator if they\u2019re wearing a mask or if they're not facing the camera directly. Combining <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">computer vision models and facial recognition<\/a>, multiple data points can be analyzed, such as the shape of a person's face, the distance between their eyes, and the contours of their features, to identify individuals with a high degree of accuracy. This can all be done real-time with the advancements with running these models at the edge.\r\n<h3>Technology that\u2019s advancing the adoption of edge AI<\/h3>\r\nEnterprises adopting <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI<\/a> paradigms have introduced a new set of technological needs that third party vendors or in-house operations must develop to stay at the forefront of edge intelligence.\u00a0 IT leaders will find themselves in an uphill battle of uncategorized data, which results in poorly performing <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI models<\/a>, data drift, and power and compute capacity constraints without considering the technologies below:\r\n<ul>\r\n \t<li><strong>Model compression<\/strong>\r\nModel compression enables larger, complex algorithms to be deployed in smaller, resource-constrained devices through <a href=\"https:\/\/xailient.com\/blog\/4-popular-model-compression-techniques-explained\/\" target=\"_blank\" rel=\"noopener\">several different methods<\/a> such as pruning (weights and filters), quantization, knowledge distillation, and low-rank factorization techniques.<\/li>\r\n \t<li><strong>Federated learning as a decentralized environment<\/strong>\r\nDecentralized deep learning methodologies, such as federated learning, <a href=\"https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=9645169\" target=\"_blank\" rel=\"noopener\">enhance privacy and data security<\/a> by leveraging data processing across client's local networks without exposing training data. This means the data stays on the device and can be encrypted and secured more easily.<\/li>\r\n \t<li><strong>Internet of Things (IoT) and blockchain technologies<\/strong>\r\nNetworks of connected devices generate unstructured data in vast quantities. This is driving the demand for smart, contract-enabled protocols to protect privacy and authenticity of source data inputs to <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI algorithms<\/a>.<\/li>\r\n \t<li><strong>AI chips and neuromorphic devices being integrated into edge hardware<\/strong>\r\nSmaller, more focused AI processors are being installed in CCTV cameras to perform a handful of common AI functions such as people counting, path flow analysis, loitering, and PPE detection; the breadth of these detections increases as more <a href=\"https:\/\/app.chooch.ai\/app\/ready-now-models\/\" target=\"_blank\" rel=\"noopener\">pre-trained AI models<\/a> and services are offered by third parties.<\/li>\r\n<\/ul>\r\n<h3>Enabling AI everywhere<\/h3>\r\nOrganizational demand for actionable insights to improve business operations is driving the adoption of using edge AI. Real-time data processing, data security, and reduced latency delivers new value from proximal data. Cameras, drones, phones, and more are becoming compute environments for providing the data and analysis businesses need to grow.\r\n\r\nThankfully, companies like Chooch, excel in <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-edge-device\/\">edge AI deployments<\/a> so customers can bring their own models or create new ones in a single platform and deploy to the latest edge devices out-of-the-box. See why Chooch was recognized as an innovator in edge AI. Download the <a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\" target=\"_blank\" rel=\"noopener\">Gartner HypeCycle\u2122 for Artificial Intelligence<\/a> to discover AI technologies to drive every stage of your AI strategy. Let\u2019s get in touch <a href=\"https:\/\/www.chooch.com\/contact-us\/\">for a demo<\/a>.",
"post_title": "The Value of Edge AI \u2014 Technologies Advancing Edge AI Adoption",
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"post_content": "<span data-contrast=\"none\">So, what exactly is UX design also known as User Experience Design? The goal of UX design is to create a web application that is both effective and easy to use. To achieve this, UX designers must consider the business problems of their users, possess a high degree of empathy, and be willing to rapidly adjust features upon in-field testing and feedback solicitation.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n\r\n<span data-contrast=\"none\">As Chooch\u2019s Product Design Lead, Zeynep drives the overall design and functionality of new features that impact Chooch users. She brings a diverse professional background of management consulting, marketing, and product management across energy and telecom industries into cross-industry computer vision applications. Her mission is to delight Chooch users as they build, host, and deploy their enterprise computer vision solutions.\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":276}\">\u00a0<\/span>\r\n<h3><b><span data-contrast=\"none\"><img class=\"size-medium wp-image-4584 alignleft\" src=\"\/wp-content\/uploads\/2023\/05\/zeynep-1.png\" alt=\"Zeynep Chooch UX Designer\" width=\"248\" height=\"300\" \/><\/span><\/b>How did you first become interested in UX Design as a profession?<\/h3>\r\n<p style=\"padding-left: 80px;\" aria-level=\"4\"><span data-contrast=\"none\">After working in the fields of consulting and marketing for many years, I knew that I needed a change, but I didn't know how to pivot. After some research, I became interested in UX design, but I had a tough time figuring out how to gain practical skills in the field. At the same time, I was also working in the marketing department of a telecommunications company when I found <\/span><a href=\"https:\/\/smartup.network\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Smartup Network<\/span><\/a><span data-contrast=\"none\">, a software company that helps entrepreneurs and innovative enterprises build <\/span><span data-contrast=\"none\">mobile and web products. <\/span><span data-contrast=\"none\">I started working as a Junior Product Manager in the evenings and weekends. I wanted to see if the role was a good fit and realized I really enjoyed developing a tech product, especially the user-facing parts of it. Because I loved the experience so much, I left my current job and started working as a UX designer at <\/span><a href=\"https:\/\/fol.com.tr\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Fol Agency<\/span><\/a><span data-contrast=\"none\"> specializing in UX\/UI.<\/span><span data-ccp-props=\"{"134233117":true,"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":276}\">\u00a0<\/span><\/p>\r\n\r\n<h3>What do you like most about your job?<\/h3>\r\n<span data-contrast=\"none\">What I like most is the part of my job that<\/span> <span data-contrast=\"none\">involves anticipating human behavior and psychology. Additionally, I love creating a product that serves people's needs and seeing it being used is extremely satisfying. The opportunity to be able to constantly improve the product and do better through feedback is also something I like.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span>\r\n<h3>What do you keep in mind as you create an AI product for users across different industries?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">Designing products for the <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch platform<\/a> is very enjoyable for someone who loves challenges, and I\u2019m one of those people. The reason designing any software product is so difficult is that it requires us to address so many different industry needs using one product and maintaining a cohesive user journey while doing that. For Chooch, it is so important we maintain a seamless journey between the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI Vision<\/a> Studio, Inference Engine, Smart Analytics, and <\/span><a href=\"https:\/\/www.chooch.com\/imagechat\/\"><span data-contrast=\"none\">ImageChat<\/span><\/a><span data-contrast=\"none\">. We are trying to bring together different industries into a common language and make the product more customizable and adaptable.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>How do you establish user empathy for a platform like Chooch?<span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">Our approach to establishing user empathy is to first understand business needs, emotions, and perspectives. We do this by using known methods, which include conducting user research, analyzing user data, continuously testing our product, gathering feedback, and making improvements. One of the most important things we focus on when designing products is to constantly iterate. We all believe that we can improve our designs based on user feedback, which is why we continuously strive to improve. Of course, for platforms like ours, our users include both our partners and their users, our direct customers, and developers, so we must cater to a wide range of primary and secondary personas.<\/span><span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><\/p>\r\n\r\n<h3>What is your main hope for the users of Chooch to understand about the product?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">My main hope for Chooch users is that they can easily leverage our platform\u2019s powerful AI technology stack to solve their most complex business problems, thus making their work easier, more efficient, and providing them a significant competitive advantage.<\/span><span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><\/p>\r\n\r\n<h3>How has Chooch\u2019s product design changed since you started? Where do you see it going forward?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">Chooch's product design has changed a lot and has also improved since I started. We have designed new products from scratch, such as Smart Analytics and <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>, and redesigned our existing products based on user experience analysis. Of course, during this time, our logo and colors have also changed, making our updates even more noticeable. As Chooch continues to grow and innovate, I see our product design becoming more user-centric, with an even greater emphasis on simplicity and ease of use despite the perceived complexities of emerging technology.\u00a0\u00a0<\/span><span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><\/p>\r\n\r\n<h3>How do you effectively come to product design decisions in a fast-paced AI startup?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">If you're doing product design in a fast-paced AI startup, you can't follow all of the steps of a traditional design methodology for each feature; we simply don\u2019t have the luxury of time. However, I still believe that it is important to at least touch each step in a structured design methodology, even if it's less detailed. For example, sometimes we can\u2019t complete a detailed conceptual design, so we still meet as a UX and UI team to draw rough sketches in brainstorming sessions. The most important thing is to decide why the desired feature is needed and which user needs it satisfies, and then figure out how all the products will be affected, even if it\u2019s just with pen and paper. When making product design decisions, it's always important to think from the perspective of what the user needs first - what problem they're facing then how we can solve it. As always, over time, what the user wants is proven by how they use our features, and we adjust accordingly.<\/span><span data-ccp-props=\"{"134233117":true,"201341983":0,"335559739":0,"335559740":276}\">\u00a0<\/span><\/p>\r\n\r\n<h3>What feature that you've designed are you most proud of?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"none\">I don\u2019t have a single feature that I am most proud of; I am proud of our teams\u2019 work creating a platform that users find easy to create AI solutions to solve their problems. At the end of the day, my job is to solve the user's problem in the most straightforward, simple, and useful manner possible.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>Where do you see UX design moving towards in the next few years?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\">In the next five years, I believe UX design will continue to evolve and become more focused on creating personalized and immersive experiences for users. Voice-based interfaces, augmented reality, virtual reality, and 3D interfaces will provide more engaging and interactive experiences. There\u2019ll be a greater emphasis on personalization where users will be able to customize their experience based on their preferences and needs. I feel an exciting trend we\u2019ll see much more of is <\/span><b><span data-contrast=\"auto\">ethical design<\/span><\/b><span data-contrast=\"auto\">, which involves designing interfaces that prioritize user privacy and safety.\u202f<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3>What impact will AI have on UX designer's role in product development?<\/h3>\r\n<p style=\"padding-left: 40px;\"><span data-contrast=\"auto\"><a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI technologies<\/a> will help UX designers analyze and interpret user data to better understand user behavior and preferences. It will also help with prototyping, testing, and even generating general design solutions. However, AI will not replace UX designers as there will always be gaps in technology that require human intervention and creativity.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\r\n\r\n<h3 aria-level=\"2\">Want to meet more of the Chooch team?<\/h3>\r\n<p aria-level=\"3\">Check out the blogs below.<\/p>",
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"post_date": "2023-05-01 15:42:28",
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"post_content": "Automated license plate recognition (ALPR) has become an essential technology in today's ultra-fast and congested world. With rapid advancements in AI, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> has evolved to help make ALPR more efficient and accurate. Using a vast dataset, AI and machine learning algorithms can automatically identify and classify license plates, recognizing patterns with a high degree of accuracy.\u00a0 Even in the most challenging conditions, such as poor lighting or oblique angles, AI-driven ALPR systems are highly successful. Through continuous learning, these <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> sustain high performance and accuracy levels even as new license plate formats or environmental conditions emerge.\r\n<h3>Key components of AI-driven license plate recognition software<\/h3>\r\n<h4><img class=\"size-medium wp-image-4538 alignleft\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/05\/license-plate-detection-of-car.jpg\" alt=\"White Car\" width=\"300\" height=\"242\" \/>Image acquisition and pre-processing<\/h4>\r\nImage acquisition is the first step in any AI-driven ALPR system. High-quality images are essential for accuracy and modern cameras, such as IP or CCTV cameras, are often used to capture these images. Once an image is acquired, pre-processing techniques are used to enhance its quality, adjust for issues such as lighting inconsistencies or distortion, and prepare the image for further analysis.\r\n<h4>License plate localization<\/h4>\r\nNext, the system must localize the license plate within the image. This step involves identifying the region of the image that contains the license plate, which can be a challenging task due to variations in background, lighting, and plate design. AI-driven systems utilize advanced image processing techniques and <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> algorithms to efficiently locate and isolate the license plate region, even in challenging conditions.\r\n<h4>Character segmentation and recognition<\/h4>\r\nOnce the license plate region has been localized and isolated, the system proceeds to segment and recognize the individual characters on the plate. This involves separating the characters from one another and identifying them as letters or numbers. AI algorithms such as convolutional neural networks (CNNs) are commonly used for this task, due to their excellent performance in recognizing patterns and objects within images.\r\n<h4>Post-processing and data storage<\/h4>\r\nUpon successful character recognition, the system processes the data, often involving steps such as ensuring data consistency, validating checksums, or parsing specific formats. The recognized license plate information is then stored in a database for further use, such as querying related vehicle records, generating statistical reports, or triggering specific actions based on the detected license plate.\r\n<h3>Real-world applications of AI-driven license plate detection<\/h3>\r\n<h4>Traffic management and law enforcement<\/h4>\r\nAutomatic monitoring of traffic flow detects vehicles that exceed speed limits, run red lights, or violate parking restrictions, enabling law enforcement agencies to issue citations more efficiently maintaining safer roads.\r\n<h4>Parking and access control systems<\/h4>\r\nRecognizing license plates automatically streamlines the entry and exit processes for vehicles, reducing the need for manual ticketing or access cards, identifying unauthorized vehicles, and improving overall parking operational efficiency.\r\n<h4>Toll collection and road pricing<\/h4>\r\nLicense plate recognition and linking them to specific vehicle records can automatically charge users based on their road usage or vehicle type. This eliminates the need for stopping at toll booths or purchasing additional transponders, contributing to streamlined traffic flow and reduced operating costs for toll operators.\r\n<h4>Vehicle tracking and fleet management<\/h4>\r\nAI-driven detection systems track the location and status of vehicles in real-time, enabling more efficient scheduling, maintenance, and route planning. It can identify and locate stolen vehicles, assisting law enforcement agencies in recovery efforts.\r\n<h3>The benefits of AI-driven license plate detection technology<\/h3>\r\nAI-driven <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-for-license-plate-dectection\/\">license plate detection<\/a> systems offer several advantages over traditional methods. Some key benefits include increased accuracy, faster processing times, enhanced scalability, and reduced need for human intervention. By leveraging AI, these systems can provide more accurate results, allowing for better decision-making and resource allocation in various applications such as traffic management, security, and law.",
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"post_date": "2023-04-26 16:48:59",
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"post_content": "Workplace safety is essential for both employees and employers alike. Despite its importance, accidents and injuries still occur. According to the<a href=\"https:\/\/www.bls.gov\/iif\/home.htm\" target=\"_blank\" rel=\"noopener\"> Occupational Safety and Health Administration (OSHA)<\/a>, there were 5190 fatal workplace injuries in 2021 and 2.6 million nonfatal workplace injuries and illnesses reported by private industry employers.\r\n\r\nA <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safer work environment reduces the risk of accidents and injuries<\/a> while also improving productivity, morale, and employee retention. Businesses that prioritize safety often benefit from lower insurance costs, reduced absenteeism and turnover, and a better reputation in their industry.\r\n<h2>Understanding computer vision and its role in workplace safety<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Computer vision and machine learning<\/a> are revolutionizing how businesses are approaching workplace safety. Computer vision models can identify risks, and AI can propose solutions for the best possible outcomes. It mimics human behavior, but it's far more capable of handling multiple inputs and finding solutions to seemingly impossible challenges. It has the impressive ability to predict future trends and pinpoint health risks and notify managers before accidents materialize.\r\n<h2>How computer vision technology enhances safety measures<\/h2>\r\nBy running AI models on cameras and other <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge devices<\/a>, computer vision becomes another set of eyes in identifying potential risks. By tracking movements in video feeds and analyzing data, computer vision can establish patterns and identify areas where improvements can be made, such as optimizing workflow or reducing the risk of repetitive motion injuries. It becomes easier to predict potential hazards before they occur, allowing for proactive measures to be taken to prevent accidents and injuries.\r\n\r\nFor example, computer vision can detect and alert workers to potential hazards in real-time. It can detect when a worker is not wearing the appropriate <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">personal protective equipment (PPE)<\/a> and alerts them to put PPE on before continuing with their task.\r\n<h2>4 uses of computer vision and AI technology for workplace safety<\/h2>\r\n<p style=\"padding-left: 40px;\"><strong>1. Object detection and recognition<\/strong>\r\nComputer vision can automatically identify and track objects or obstacles within the workspace, allowing for swift recognition of potential hazards. This technology helps ensure <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">worker safety by providing real-time alerts and minimizing the risk of accidents<\/a> caused by unattended equipment, machinery malfunctions, or obstructed pathways.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>2. Facial recognition and employee tracking<\/strong>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">Facial recognition technology<\/a> integrated with computer vision can monitor employee movements throughout the workplace, ensuring personnel safety and supporting access control measures. By tracking authorized individuals, AI Vision can detect unauthorized access or identify employees that may be in hazardous areas and provide timely alerts to mitigate potential risks.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>3. Fall and accident detection<\/strong>\r\nOne of the key benefits of <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision<\/a> is its ability to detect if an employee has fallen or been involved in an accident. In the event of an incident, immediate notifications can be sent to the relevant parties, allowing for prompt responses and timely medical assistance if necessary. This technology is lessening the severity of potential injuries as well as promoting a safer working environment.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>4. Hazardous material identification<\/strong>\r\nMany industries require the handling of hazardous materials, and computer vision can help ensure proper compliance with safety regulations. By automatically identifying dangerous substances, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI vision systems<\/a> can monitor the appropriate use, storage, and disposal of hazardous materials, reducing the risk of exposure or environmental damage.<\/p>\r\n\r\n<h2>How to implement computer vision for workplace safety across industries<\/h2>\r\nLet\u2019s dive deeper into how different industries are adopting computer vision and applying it to improve <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">employee safety<\/a> and explore real-world examples of its effectiveness.\r\n<ul>\r\n \t<li><strong>Manufacturing and warehousing<\/strong>\r\nComputer vision can significantly improve safety in manufacturing and warehousing environments, where workers often face various <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards<\/a> associated with heavy machinery, elevated platforms, and moving equipment. By monitoring the workspace continuously, AI vision can detect anomalies, identify potential threats, and send alerts to prevent accidents. Moreover, it can also streamline equipment maintenance and enhance overall operational efficiency. Extreme temperatures, gas leaks, chemical exposures, fire, and smoke can be detected at the earliest signs of leaks or spills to prevent catastrophic environmental incidents.<\/li>\r\n \t<li><strong>Construction sites<\/strong>\r\nConstruction sites pose numerous risks to workers, including falls, equipment-related accidents, and exposure to hazardous materials. <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI Vision<\/a> can assist with mitigating these risks by monitoring site activities, tracking worker movements, and recognizing potential hazards. Computer vision can identify unauthorized personnel or intruders faster, ensuring only trained individuals access the site, contributing to a safer and more secure workplace. Whether it\u2019s hardhats, gloves, goggles, safety vests, or harnesses, <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">computer vision models can monitor adherence automatically<\/a>.<\/li>\r\n \t<li><strong>Healthcare facilities<\/strong>\r\nBy continuously monitoring patient rooms, hallways, and treatment areas, computer vision systems can detect abnormal behaviors or incidents, such as patient falls, unattended visitors, and potential security breaches. This real-time information enables staff to respond quickly, minimizing potential harm and maintaining a safe environment for everyone. Enforcing zones around no-go areas and sending alerts to supervisors when people cross into such zones can provide early warnings to prevent critical safety scenarios.<\/li>\r\n \t<li><strong>Retail and customer service<\/strong>\r\nWhile <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail and customer service<\/a> establishments may not face the same level of risk as other industries, computer vision comes into play when analyzing customer interactions, detecting unusual activities, and identifying potential loss risks. It becomes critical for analyzing video data real-time to prevent incidents such as theft, vandalism, and altercations. Additionally, this technology can provide valuable insights into customer behavior, leading to improved service and more positive shopping experiences.<\/li>\r\n<\/ul>\r\n<h2>AI for workplace safety<\/h2>\r\nComputer vision is becoming a powerful tool for detecting employee safety hazards earlier to prevent workplace accidents. By understanding the technology and implementing the right system, businesses can protect their employees and reap long-term benefits. By automating manual and repetitive tasks, computer vision contributes to a more proactive and data-driven approach to <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety<\/a>, making it an essential addition to any safety program.\r\nLearn more about our solutions for making your organization safer, <a href=\"https:\/\/www.chooch.com\/contact-us\/\">contact us<\/a> to talk to our team.",
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"post_content": "Employee safety is a top priority for businesses across all industries. <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">Workplace injuries<\/a>, physical security breaches, chemical spills, and fires not only cost businesses significant amounts of money but can also tarnish business reputations. As these hazards continue to impact businesses, companies are looking to emerging technology to deliver innovative solutions to detect, alert, and prevent <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace hazards<\/a>.\r\n\r\nOne AI safety technology being adopted by more companies is <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> which enables machines to interpret visual information faster than humans can and with greater accuracy. The use of computer vision is already common within many industries, but let\u2019s dive deeper into how manufacturers are adopting computer vision and applying it to improve employee safety and explore real-world examples of its effectiveness.\r\n<h3>Common applications of computer vision AI for workplace safety<\/h3>\r\nComputer vision has the potential to revolutionize <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety<\/a> by automating hazard detection and providing real-time alerts. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> helps businesses quickly identify and address safety risks, preventing accidents and improving overall employee welfare.\r\n\r\nEmployee safety hazards take many forms, depending on the industry and work environment. These are some of the more common examples where computer vision is used for early hazard detection.\r\n<h3>Computer vision AI safety applications include:<\/h3>\r\nAI can provide significant advantages for manufacturers when used for quality assurance, including:\r\n<ul>\r\n \t<li><strong>Monitoring hazardous conditions<\/strong>\r\nExtreme temperatures, gas leaks, chemical exposures, fire, and smoke can be detected at the earliest signs of leaks or spills to prevent catastrophic environmental incidents, which in the United States alone, happen once <a href=\"https:\/\/www.theguardian.com\/us-news\/2023\/feb\/25\/revealed-us-chemical-accidents-one-every-two-days-average\" target=\"_blank\" rel=\"noopener\">every two days costing $477M annually<\/a>.<\/li>\r\n \t<li><strong>Detecting emergency response<\/strong>\r\nStruck-by incidents, where workers are hit by moving objects or caught-in between accidents where individuals become trapped in machinery or equipment can be identified earlier. Enforcing zones around no-go area sand sending alerts to supervisors when people cross into such zones can provide early warning.<\/li>\r\n \t<li><strong>Identifying unsafe employee behaviors or practices<\/strong>\r\nSlip and fall, firearm, and proper ergonomic detections can be detected using <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> models installed on cameras and other edge devices.<\/li>\r\n \t<li><strong>Detecting personal protective equipment (PPE) violations<\/strong>\r\nHardhats, gloves, goggles, safety vests, harnesses are all required safety measures that ensure an organization\u2019s compliance with regulatory agencies, <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">computer vision models can monitor adherance automatically<\/a>.<\/li>\r\n \t<li><strong>Predictive maintenance and automated inspection<\/strong>\r\nSigns of wear, damage, or tampering with equipment or electrical <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards<\/a>, such as exposed wiring or overloaded circuits, can be monitored automatically by static cameras or drones running defect detection AI and watching for damage in asset intensive industries.<\/li>\r\n<\/ul>\r\n<h3>The importance of early hazard detection<\/h3>\r\nIdentifying and addressing hazards early is crucial for preventing accidents and maintaining a safe work environment. Early detection enables timely intervention, enabling businesses to correct unsafe conditions or practices before they escalate into serious incidents.\r\n\r\nFor example, if an employee is working on an elevated platform and computer vision models detect they\u2019re not wearing a safety harness, an alert can be sent to the employee and their supervisor to address the issue before a serious fall occurs.\r\n\r\nTraditional methods of hazard detection and limitationsTraditional methods of hazard detection often rely on manual inspections, safety audits, and employee reporting. While these methods can be effective, they have several limitations:\r\n<ul>\r\n \t<li><strong>Prone to human oversight<\/strong>\r\nInspections and audits can miss critical issues, especially when performed by individuals with limited safety expertise or those experiencing fatigue.<\/li>\r\n \t<li><strong>Labor intensive<\/strong>\r\nManual safety assessments can be labor-intensive, leaving less time for more productive tasks.<\/li>\r\n \t<li><strong>Reactive rather than proactive<\/strong>\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">Hazards<\/a> are normally detected only after they've already caused damage or injury, making it difficult to prevent accidents.<\/li>\r\n<\/ul>\r\nIn contrast, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> can provide real-time hazard detection and alerts, allowing for immediate corrective action to be taken. This proactive approach can significantly reduce the risk of accidents and injuries in the workplace.\r\n\r\nFor example, computer vision can detect when an employee is not wearing the proper personal protective equipment, such as a hard hat or safety glasses, and alert them to put it on before they start working. This can prevent injuries and ensure that employees are always following safety protocols.\r\n<h3>Real-world examples of computer vision in workplace safety<\/h3>\r\nComputer vision technology has already been successfully deployed to enhance safety in a variety of industries. Here are three case studies that demonstrate its effectiveness:\r\n<ul>\r\n \t<li><strong>Case Study 1: Manufacturing plant<\/strong>\r\nA manufacturing facility used computer vision to monitor worker compliance with PPE requirements. The system alerted supervisors in real-time of violations, allowing them to take immediate corrective action. Over time, PPE compliance rates improved, and <a href=\"https:\/\/www.chooch.com\/blog\/how-businesses-use-computer-vision-and-ai-for-workplace-safety\/\">workplace injuries<\/a> decreased significantly.<\/li>\r\n \t<li><strong>Case Study 2: Construction site<\/strong>\r\nOn a construction site, computer vision cameras were installed to detect workers entering dangerous zones without proper authorization or training. When a breach was detected, a real-time alert was sent to site managers, who could intervene and be proactive in preventing an accident.<\/li>\r\n \t<li><strong>Case Study 3: Warehouse<\/strong>\r\nUsing computer vision, a warehouse can monitor forklift operations, identifying instances where drivers performed unsafe maneuvers or failed to wear seat belts. The data gathered was then used to target specific training needs and promote safer operation, reducing the incidence of accidents and property damage.<\/li>\r\n<\/ul>\r\n<h3>Computer vision and AI safety technology is evolving<\/h3>\r\nComputer vision is becoming a powerful tool for detecting <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">employee safety hazards<\/a> earlier to prevent <a href=\"https:\/\/www.chooch.com\/blog\/how-businesses-use-computer-vision-and-ai-for-workplace-safety\/\">workplace accidents<\/a>. By understanding the technology and implementing the right system, businesses can protect their employees and reap long-term benefits. By detecting hazards early and proactively addressing them, businesses create a safer and more productive work culture for their teams. Learn more about our solutions for making your organization safer, <a href=\"https:\/\/www.chooch.com\/contact-us\/\">contact us<\/a> to talk to our team.",
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"post_content": "Artificial Intelligence (AI) is transforming the manufacturing industry by increasing efficiency, reducing costs, and improving overall product quality. One of the most promising applications of AI is in quality assurance (QA) processes on the production line.\r\n\r\nAI is revolutionizing manufacturing QA processes. By leveraging <a href=\"\/blog\/what-is-edge-ai\/\">Edge AI<\/a> technology on existing cameras and hardware, AI and computer vision is being used for any task that historically requires human eyes and human understanding but with dramatically greater speed and reliability. These <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI solutions<\/a> are evolving to both support and optimize your existing manufacturing processes.\r\n<h3>Understanding the role of AI in production line QA<\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI<\/a> is transforming the way quality assurance is orchestrated. By automating the monitoring and inspection of products at various stages of the production process, AI can significantly enhance QA practices, minimize human error, increase accuracy, and save time.\r\n\r\nHistorically, QA in manufacturing has been a labor-intensive process that has required skilled human inspectors to assess products for defects and deviations from established specifications. This approach was time-consuming, expensive, and prone to errors. But with the advent of automation and digital technologies, manufacturing processes have become more efficient, and companies have started to explore new ways to enhance QA practices.\r\n<h3>The evolution of quality assurance in manufacturing<\/h3>\r\nOver the years, manufacturers have implemented various QA methods, including statistical process control, Six Sigma, and Total Quality Management. These methods have helped manufacturers identify and address quality issues, but they still rely heavily on human intervention. With the rise of AI, manufacturers can now shift from traditional manual inspection methods towards smart, automated solutions that can save time and resources while ensuring consistent product quality.\r\n<h3>Key benefits of implementing AI for QA<\/h3>\r\nAI can provide significant advantages for manufacturers when used for quality assurance, including:\r\n<ul>\r\n \t<li><strong>Increased accuracy and consistency in identifying product defects<\/strong>\r\nAI-powered systems can detect even the slightest deviations from established specifications, ensuring that all products meet the required quality standards.<\/li>\r\n \t<li><strong>Reduced reliance on human inspectors<\/strong>\r\nBy automating the inspection process, manufacturers reduce the need for human inspectors, leading to significant cost savings over time.<\/li>\r\n \t<li><strong>Enhanced productivity due to faster inspection times<\/strong>\r\nAI-powered systems can inspect products at a much faster rate than human inspectors, leading to enhanced productivity and faster time-to-market.<\/li>\r\n \t<li><strong>Real-time data analysis and decision-making capabilities<\/strong>\r\nAI-powered systems can analyze data in real-time, providing manufacturers with valuable insights and enabling them to make informed decisions quickly.<\/li>\r\n \t<li><strong>Ability to predict and prevent potential quality issues before they arise<\/strong>\r\nAI-powered systems can detect patterns and trends in data, enabling manufacturers to predict and prevent potential quality issues before they occur.<\/li>\r\n<\/ul>\r\n<h3>Types of AI technologies for your production line<\/h3>\r\nIntegrating <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI technologies<\/a> into your production line can be a game-changer for your business. By automating quality assurance tasks, manufacturers can improve efficiency, reduce costs, and increase product quality. However, not all AI technologies are created equal, and it's essential to choose the right ones for your specific needs.\r\n\r\n<strong>Machine Learning<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Machine learning algorithms<\/a> can analyze large datasets from production processes and identify patterns or anomalies that could affect product quality. By using ML, manufacturers can predict potential quality issues before they occur, allowing them to take corrective action faster.\r\n\r\n<strong>Deep Learning<\/strong>\r\n\r\nDeep Learning is a more advanced form of ML that uses neural networks to learn from data. DL algorithms can analyze complex data sets and find correlations that may not be apparent to human operators. This technology is particularly useful for identifying defects in products or components, as it can classify them with high accuracy.\r\n\r\n<strong>Computer Vision and Image Recognition<\/strong>\r\n\r\nBy using cameras and other sensors, AI-powered systems can capture images or videos of products and components and analyze them in real-time. This technology can detect defects, inconsistencies, and other quality issues that may be missed by human operators. Combined with image recognition, manufacturers can automate visual QA tasks, improving accuracy and efficiency.\r\n<h3>Real-world applications of AI in production line QA<\/h3>\r\nMany manufacturers have already started implementing <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI solutions<\/a> to improve their quality assurance processes. Let\u2019s explore some practical applications of AI in production line QA.\r\n\r\n<strong>Automated Visual Inspection Systems<\/strong>\r\n\r\nIn industries where product appearance and finish are critical, automated visual inspection systems powered by AI can greatly improve the speed and accuracy of defect detection. Using <a href=\"https:\/\/www.chooch.com\/imagechat\/\">computer vision and image recognition<\/a>, these systems can identify cosmetic flaws, surface defects, or deviations from product specifications, enabling manufacturers to quickly address quality issues.\r\n\r\n<strong>Predictive Maintenance and Anomaly Detection<\/strong>\r\n\r\nAI-powered predictive maintenance systems can monitor equipment health and performance in real-time, detecting anomalies that may indicate impending failures or deteriorating performance. This allows manufacturers to proactively address potential issues before they lead to costly downtime or compromised product quality.\r\n\r\n<strong>AI-Powered Process Optimization<\/strong>\r\n\r\nBy analyzing production data and identifying patterns and trends, AI can provide insights into potential bottlenecks and inefficiencies in the manufacturing process. Manufacturers can use this information to optimize their production lines, reduce waste, and improve overall product quality and consistency.\r\n<h3>Drive to adopt AI in manufacturing processes<\/h3>\r\nAI is transforming the manufacturing industry by revolutionizing the way QA is conducted. By automating the inspection process and providing real-time data analysis, AI-powered systems can significantly enhance product quality, reduce costs, and improve productivity.\r\n\r\nBy understanding the various <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI technologies<\/a>, their applications, and the steps necessary for successful integration, manufacturers can position themselves at the forefront of innovation in quality assurance. As technology continues to evolve, we can expect to see even more benefits from AI in manufacturing. To learn more about Chooch\u2019s solutions, check out our <a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/ai-in-manufacturing-ebook.pdf\" target=\"_blank\" rel=\"noopener\">Ebook<\/a>, AI in Manufacturing, and <a href=\"https:\/\/www.chooch.com\/contact-us\/\">let\u2019s get in touch<\/a>.",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Artificial intelligence (AI)<\/a> has become a transformative force across industries, and manufacturing is no exception. The adoption of AI technologies is revolutionizing operations on production lines, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing processes<\/a>, and transitioning manufacturing into a new era.\r\n<h3>The emerging role of AI in the manufacturing industry<\/h3>\r\nAs the manufacturing sector continues to embrace innovation and digital transformation, AI is playing an increasingly critical role in the industry's future. Advances in machine learning, natural language processing, and <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">computer vision are empowering manufacturers<\/a> to make more intelligent decisions, optimize processes, and address challenges in previously inconceivable ways. In many cases, AI is being integrated into the manufacturing process to help organizations become more adaptive and responsive to market demands and fluctuations.\r\n\r\nWith the help of AI, manufacturers can achieve unprecedented levels of efficiency, accuracy, and productivity. AI-powered machines can work around the clock, without the need for breaks, and can perform tasks with unprecedented precision, reducing the risk of human error. This not only improves the quality of the products but also helps manufacturers save time and money.\r\n<div>\r\n\r\nThis growing role of <a href=\"https:\/\/mindtitan.com\/resources\/industry-use-cases\/ai-in-manufacturing\/\" target=\"_blank\" rel=\"noopener\">AI in manufacturing<\/a> is further fueled by Industry 4.0, the fourth industrial revolution focused on digitalization and connectivity. The new paradigm has accelerated the adoption of smart technologies across the entire value chain, and AI is unquestionably one of the key drivers of this change.\r\n\r\n<\/div>\r\n<div>\r\n<h3>Key benefits of AI implementation<\/h3>\r\n<\/div>\r\n<div>\r\n\r\nAs manufacturers implement <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI technologies<\/a>, they are realizing a host of benefits that extend beyond cost savings or efficiency improvements. These benefits include enabling faster time-to-market, personalization of products, improved safety, and higher levels of innovation. Furthermore, AI adoption is helping manufacturers take advantage of the vast amounts of data generated by the modern industrial processes, using advanced analytics to gain valuable insights, optimize strategies, and drive continuous improvement.\r\n\r\nOne of the most significant benefits of AI implementation is its ability to enable predictive maintenance. With AI-powered machines, manufacturers can detect potential issues before they become significant problems, allowing for proactive maintenance, and reducing downtime. This not only saves time and money but also helps to extend the lifespan of expensive equipment.\r\n\r\n<\/div>\r\n<div>\r\n\r\nAnother benefit of AI implementation is the ability to enhance product quality. AI-powered machines can detect even the slightest defects in products, ensuring that only high-quality products are released to the market. This not only helps with reputation building for the manufacturer but also helps reduce the risk of product recalls, which can be costly and damaging to the brand.\r\n\r\nAdditionally, AI can help manufacturers to optimize their supply chain management. By analyzing data from multiple sources, including suppliers, logistics\u2019 providers, and customers, <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI<\/a> can help manufacturers identify potential bottlenecks and inefficiencies in the supply chain, allowing for more effective planning and execution. This helps reduce lead times, improve on-time delivery, and reduce costs.\r\n\r\n<\/div>\r\n<h3>Use Case 1: Predictive Maintenance<\/h3>\r\n<p style=\"padding-left: 40px;\"><strong>How AI enables predictive maintenance<\/strong><\/p>\r\n<p style=\"padding-left: 40px;\">One of the most promising applications of <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">AI in manufacturing<\/a> is its ability to facilitate predictive maintenance. Instead of waiting for equipment to fail, AI-powered predictive maintenance systems constantly analyze and monitor data generated from machines to identify patterns, anomalies, and signals that indicate potential maintenance issues. By interpreting this data, AI can predict when equipment will require maintenance\u2014reducing downtime and minimizing the impact of unforeseen breakdowns.<\/p>\r\n<p style=\"padding-left: 40px;\">AI-driven predictive maintenance is significantly more effective than traditional reactive or scheduled maintenance approaches, which often lead to unnecessary costs and disruptions. By leveraging AI, manufacturers can better allocate resources, enhance overall equipment effectiveness (OEE), and extend the lifespan of their assets.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Real-world examples and success stories<\/strong><\/p>\r\n<p style=\"padding-left: 40px;\">Take for instance, Siemens, a global powerhouse in the fields of energy, <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>, and infrastructure, uses AI-powered models to monitor and analyze the performance of its wind turbines. By doing so, the company can predict potential issues and adjust maintenance schedules, accordingly, reducing the occurrence of unscheduled downtime and substantially increasing turbine efficiency.<\/p>\r\n<p style=\"padding-left: 40px;\">Similarly, Harley-Davidson, the iconic American motorcycle manufacturer, has implemented an AI-based predictive maintenance system in its plants, resulting in a significant reduction of downtime and a 3% increase in Overall Equipment Efficiency within the first year.<\/p>\r\n\r\n<h3>Use Case 2: Quality Control<\/h3>\r\n<div style=\"padding-left: 40px;\">\r\n\r\n<strong>AI-powered visual inspection systems<\/strong>\r\n\r\n<\/div>\r\n<p style=\"padding-left: 40px;\">Quality control and inspection are critical aspects of the manufacturing process, ensuring that products meet stringent quality standards and minimizing the risk of defects. AI-powered visual inspection systems are transforming this area, leveraging advanced <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision algorithms<\/a> to detect defects and deviations from the desired product specifications with far greater accuracy and speed than manual inspection.<\/p>\r\n<p style=\"padding-left: 40px;\">These AI systems can examine products at various stages of the production process, identifying potential issues, and in some cases, even taking corrective action. This results in greater efficiency, cost savings, and higher levels of customer satisfaction, all while reducing the dependence on human inspectors who may be prone to errors and fatigue.<\/p>\r\n<p style=\"padding-left: 40px;\"><strong>Improving product quality with AI<\/strong><\/p>\r\n<p style=\"padding-left: 40px;\">Foxconn, the world's largest contract electronics manufacturer, has leveraged <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">AI and machine learning<\/a> to achieve a defect detection rate above 90% in some of its facilities, a marked improvement over its previous manual inspection methods. Similarly, GE Appliances has implemented an AI-based visual inspection system to ensure the quality of its dishwashers, reducing defects by 50% and achieving a significant increase in customer satisfaction scores.<\/p>\r\n\r\n<h3>Use Case 3: Production Optimization<\/h3>\r\n<div style=\"padding-left: 40px;\">\r\n\r\n<strong>AI-driven process automation<\/strong>\r\n\r\nAI has the potential to revolutionize the way the production process is managed, providing invaluable insights, and enabling better decision-making through intelligent process automation. By analyzing various production-related data points in real-time, AI can identify inefficiencies, suggest optimizations, and dynamically adapt manufacturing operations to reduce waste, conserve energy, and boost productivity.\r\n\r\nThis streamlined approach to production management, fueled by AI-driven algorithms, allows manufacturers to optimize labor allocation, machine utilization, and workflow\u2014delivering higher-output and improved profitability.\r\n\r\n<strong>Enhancing efficiency and reducing waste<\/strong>\r\n\r\nTesla has integrated AI into its production processes to enhance efficiency, reduce production times, and minimize waste. As a result, the company has reported a 35% increase in vehicle production efficiency and a 75% decrease in production-related scrap material. Another example, Nestl\u00e9, the world's largest food company, has turned to AI to optimize its production processes, resulting in significant energy savings and reduced carbon emissions.\r\n\r\n<img class=\"wp-image-4429 aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/04\/manufacturing-employee-safety.jpg\" alt=\"Workplace safety\" width=\"950\" height=\"397\" \/>\r\n\r\n<\/div>\r\n<div>\r\n<h3>Use Case 4: Workplace Safety<\/h3>\r\n<p style=\"padding-left: 40px;\"><strong>Real-time visual data to prevent workplace injuries<\/strong><\/p>\r\n<p style=\"padding-left: 40px;\">Despite the advancements in manufacturing efficiencies, physical labor still results in <a href=\"https:\/\/injuryfacts.nsc.org\/work\/work-overview\/work-safety-introduction\/\" target=\"_blank\" rel=\"noopener\">hundreds of thousands of injuries every year<\/a>. Not only do these kinds of injuries lead to life altering scenarios for families of individuals enduring serious medical complications, but they also result in enormous hard and soft dollar costs for companies.<\/p>\r\n<p style=\"padding-left: 40px;\">Thanks to the increased capabilities of GPUs and CPUs, companies can now host compute-intensive AI models in remote or hardened environments. These <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision models<\/a> can monitor dynamic aspects of individuals such as their pose or gait or simply monitor for smoke, fire, or weapons. These triggers are used to send real-time alerts for unsafe working conditions in real-time to maximize <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\" target=\"_blank\" rel=\"noopener\">workplace safety<\/a> and minimize the number of incidents.<\/p>\r\n\r\n<\/div>\r\n<div>\r\n<h3>Use Case 5: Supply Chain Management<\/h3>\r\n<p style=\"padding-left: 40px;\"><strong>AI in demand forecasting and inventory management<\/strong><\/p>\r\n<p style=\"padding-left: 40px;\">The effective management of a supply chain is crucial to the success of any manufacturing business. AI is increasingly being applied within supply chain management to improve demand forecasting, inventory management, and production planning. Through the analysis of vast amounts of data from various sources, AI can generate accurate demand predictions and optimize production schedules to <a href=\"https:\/\/www.chooch.com\/blog\/artificial-intelligence-is-transforming-retail-shelf-management\/\">minimize stockouts<\/a> and overstock scenarios\u2014leading to cost savings and increased customer satisfaction.<\/p>\r\n<p style=\"padding-left: 40px;\">In addition to more accurate forecasting, AI can also improve inventory management by providing real-time visibility into stock levels. This ensures that manufacturers have a better understanding of their supply chain and can make informed decisions about when to reorder materials, reducing both waste and stock holding costs.<\/p>\r\n\r\n<\/div>\r\n<h3>AI is transforming the manufacturing industry<\/h3>\r\nAI is helping manufacturers achieve unprecedented levels of efficiency, accuracy, and productivity. The benefits of AI implementation extend far beyond simple cost savings or efficiency improvements, including faster time-to-market, personalization of products, improved safety, and higher levels of innovation. As the industry continues to embrace digital transformation, AI will undoubtedly play an increasingly critical role in shaping the future of manufacturing. To learn more about Chooch\u2019s solutions, <a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/ai-in-manufacturing-ebook.pdf\" target=\"_blank\" rel=\"noopener\">check out our Ebook<\/a>, AI in Manufacturing or <a href=\"https:\/\/www.chooch.com\/contact-us\/\">get in touch with us<\/a>.",
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"post_date": "2023-04-12 15:03:47",
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"post_content": "While the evolution of natural language processing has made remarkable advancements in recent years, <a href=\"https:\/\/www.chooch.com\/blog\/how-to-integrate-large-language-models-with-computer-vision\/\">large language models (LLM)<\/a> have not been particularly useful in analyzing visual information in photos, videos, and other images. <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a>, is the latest image-to-text generative AI technology. And it\u2019s a game changer.\r\n\r\nImageChat is Chooch\u2019s latest cutting-edge model that analyzes images and provides more detailed insights into visual images with staggering accuracy in most use cases. An ensemble of LLM and computer vision AI models make it capable of recognizing over 40 million visual elements. It\u2019s providing enterprises a revolutionary way to build computer vision models using text prompts.\r\n<h3>Detecting wildfires with ImageChat AI<\/h3>\r\n<div>One of the first <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat deployments<\/a> is in the detection of wildfires. As we are all aware, wildfires in California can cause significant damage and pose a serious threat to people and property. ImageChat is deployed on 1000 ground station video streams, providing unprecedented accuracy in smoke detection. ImageChat also generates language descriptions of images, providing organizations with additional information about the detected elements. For example, in the wildfire case, the model identifies the location of the smoke in the camera frame and a confidence value of the detection, discerning haze, fog and other related events from actual smoke. This information allows organizations to make certain every possible detection is being evaluated with very few false positives.<\/div>\r\n<h3>When computer vision meets large language models<\/h3>\r\n<div>\r\n\r\nTrained on over 11 billion parameters and 400 million images, ImageChat-1 is a dramatic step into the future, bridging the gap between language and visual information. This type of intelligence, where machines can comprehend visual data using language, is taking the computer vision category to a much higher level of sophistication.\r\n\r\n<\/div>\r\n<div>\r\n\r\nThe <a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat model<\/a> is built on Chooch's proprietary architecture, which uses a transformer-based neural network, pre-trained on vast amounts of visual and language data combined with object detectors to generate localized, highly accurate detection of the most subtle nuances in images for any enterprise use case. ImageChat can analyze images and frames of video by breaking them down into individual components, such as objects, people, and locations, providing detailed descriptions that can be queried with language prompts from a user or automatically via an API.\r\n\r\n<\/div>\r\n<h3>ImageChat is the future of image-to-text generative AI<\/h3>\r\n<div>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/imagechat\/\">ImageChat<\/a> is a profound milestone \u2013 the intersection of computer vision and language. With its remarkable precision, this technology has the potential to transform how we interpret video streams and images and extract valuable insights from them. Chooch anticipates enterprises will soon build their own ImageChat models using their unique visual and language data on top of the foundational ImageChat model. It will be instrumental in safeguarding and disseminating critical enterprise information to all stakeholders, ensuring business continuity and growth.\r\n\r\nImageChat is available to Chooch enterprise customers for deployment on any existing camera system. For those who want to try it out, ImageChat is also available as a free app on <a href=\"https:\/\/apps.apple.com\/us\/app\/chooch-ic2\/id1304120928\" target=\"_blank\" rel=\"noopener\">iOS<\/a> and <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.chooch.ic2&pli=1\" target=\"_blank\" rel=\"noopener\">Android<\/a>.\r\n\r\n<\/div>",
"post_title": "ImageChat<sup>TM<\/sup> \u2014 The Latest in Image-to-Text Generative AI",
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"post_content": "What exactly is machine learning. Broadly speaking, machine learning is a subset of artificial intelligence that leverages data and algorithms to imitate the way that humans learn. \u00a0By feeding large amounts of \u201ctraining\u201d data, computers can learn to perform almost any given task, with or without human intervention. As machines are fed more data, its prediction accuracy gradually improves. In many ways, the process mimics how humans learn.\r\n<h3>So how does machine learning impact our lives?<\/h3>\r\nWhen you purchase nearly anything online, machine learning helps guide you to a specific item you're looking for. Your credit card company uses machine learning to decide if the transaction is legitimate or fraudulent. Social media sites, like Instagram, use machine learning to suggest posts that align with your interests based on your browsing behavior, click patterns, the time you spend on certain posts. Machine learning enables retailers to put ads in front of you based on topics in which you\u2019ve shown interest. Pretty much everything today is powered by some level of machine learning. ML is helping machines do tasks that would normally be done by humans. It is changing the way we live our lives.\r\n\r\nMachine Learning is one of the most exciting areas of technology for software engineers. Engineers create algorithms that make machine learning possible. It is a highly skilled role, on the forefront of innovation.\r\n<h3><\/h3>\r\n<img class=\"alignleft wp-image-4591 size-medium\" src=\"\/wp-content\/uploads\/2023\/04\/blog-korhan-polat-chooch.png\" alt=\"Korhan Polat\" width=\"300\" height=\"268\" \/>\r\n<h3>Meet a Chooch Engineer: Korhan Polat, Machine Learning Engineer<\/h3>\r\nKorhan Polat joined Chooch in early 2022 as a machine learning engineer. Prior, he worked as an ML engineer at Anadolu Sigorta, one of the first commercial insurance companies in Turkey. He developed CNN models using PyTorch, for detection and segmentation of car damages from accident photos. He received both a BSc and MSc degrees in electrical and electronics Engineering from Bogazici University in Turkey. He specialized in machine learning, signal processing, and computer vision. Korhan focused his graduate studies on developing vision <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> for classification of brain tumor types from MRIs and unsupervised algorithms that automatically detect human sign language.<strong>\u00a0<\/strong>\r\n<h3>Korhan, how did you first become interested in machine learning as a profession?<\/h3>\r\nI have always been interested in science and technology. I enjoyed math and sciences when I was a student. And I like observing nature. I\u2019m good with numbers. So, the decision to go into <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning development<\/a> was easy for me.\r\n<h3>What do you like most about your job?<\/h3>\r\nMy job can be described as \u201cteaching machines how to see\u201d. We train AI models that match human perception or often surpass it. However, in some cases, the obvious for the human eye can be very difficult to detect with standard <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a>. That\u2019s when we come up with creative solutions as a team. I really like these challenges the most because we\u2019re forced to be creative, and it reinforces how lucky I am to be working with the amazing team of engineers at Chooch.\r\n<h3>What is there about AI gets you excited? Help mere mortals understand.<\/h3>\r\nAI is a field that has evolved rapidly and each month there\u2019s an exciting new AI model that makes everyone even more excited. Moreover, AI research is an endless endeavor to equip machines with human-like reasoning and perception. So, in many ways, AI research teaches us about ourselves, how we derive our own perceptions. This gets me very excited.\u00a0 I feel like I understand more about human perception as I work with technology to behave more like humans.\r\n<h3>Where do you think AI is going ultimately?<\/h3>\r\nAI has made massive leaps in the last decade. Initially, the discriminative models were revolutionary and took the lead. Lately, we\u2019re witnessing the rise of generative <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> that are getting the most attention right now. I predict that this trend will continue. Also, there will be even more discussions about AI ethics. As AI becomes more widely adopted in our everyday lives, we\u2019ll face many issues with an AI bias and justification.\r\n<h3>Where do you think AI will have the most compelling impact?<\/h3>\r\nWhat really excites me is AI\u2019s potential to have a positive impact in many areas, but one of the most promising is in healthcare. AI has the potential to help doctors and researchers diagnose diseases more quickly and accurately, develop new treatments, and improve patient outcomes. AI can also help improve access to healthcare in underserved communities and make <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> more affordable and efficient.\r\n<h3>Give us a sense of where AI will be ten years from now.<\/h3>\r\nRecent trends in AI research include the development of explainable AI, which aims to make AI systems more transparent and understandable to humans. Another trend is the use of unsupervised learning, which allows <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI systems<\/a> to learn from data without being explicitly told what to look for. Finally, there is a growing interest in using AI for social good, such as developing AI systems to help with healthcare or environmental sustainability.\r\n<h3>What will AI engineers be doing 10 years from now. How will their jobs change?<\/h3>\r\nRecent trends in AI research include the development of explainable AI, which aims to make AI systems more transparent and understandable to humans. Unsupervised learning, which allows models to learn from data without being explicitly told what to look for, will surely evolve significantly over the next few years. Finally, there is a growing interest in using AI for social good, such as developing <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">AI systems to help with healthcare<\/a>, or environmental sustainability. It\u2019s important to start shifting people\u2019s mindsets that AI should not be feared and has real benefits in helping people and the environment.\r\n\r\nThe concept of Artificial Intelligence is certainly not new. Most of us perhaps first became aware of AI with the evolution of spell check in documents and on websites. \u00a0But its power to impact daily lives has never been more powerful than it is right now. Often AI makes our work lives and personal lives easier and better without us even being aware that it\u2019s AI making all the things we take for granted possible.\r\n\r\nThe recent news about more modern, more highly evolved chat bots is providing a glimpse into future possibilities. And though no one has a crystal ball, it\u2019s safe to say that AI will ultimately change our lives at least as much as the personal computer has.\r\n\r\nEven more fascinating is Chooch\u2019s development of an <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI solution<\/a> that will allow people to be interactive with photos, videos and other images to learn more about what is in them. You can ask the images questions, and it will answer you back. This is going to take machine learning to an entirely new dimension. <a class=\"css-1rn59kg\" title=\"\/imagechat\" href=\"https:\/\/www.chooch.com\/imagechat\/\" data-renderer-mark=\"true\"><u data-renderer-mark=\"true\">Check it out yourself. <\/u><\/a>",
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"post_date": "2023-02-06 20:35:16",
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"post_content": "<div id=\"top-row\" class=\"style-scope ytd-video-secondary-info-renderer\">\r\n\r\nThe Chooch advanced <a href=\"https:\/\/www.chooch.com\/platform\/\">AI Vision platform<\/a> gives retailers the power to dramatically reduce costs, improve safety, reduce shrinkage, and drive profitability with computer vision.\r\n\r\nHear Michael Liou, President of Corporate Strategy & Development at Chooch talk about the potential of AI vision in this <a href=\"https:\/\/www.youtube.com\/watch?v=sE0TJm39M-0\">powerful discussion<\/a> around loss prevention at the National Retail Federation (NRF) Big Show. Other speakers include: Read Hayes of Loss Prevention Research Council (LPRC), Brandon Cox of Deloitte and Nicholas Borsotto of Lenovo. This recording is originally published by Lenovo Data Center.\r\n\r\n[embed]https:\/\/www.youtube.com\/watch?v=sE0TJm39M-0[\/embed]\r\n\r\n<\/div>\r\n<div id=\"top-row\" class=\"style-scope ytd-video-secondary-info-renderer\">\r\n\r\nLearn more about <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Chooch AI Vision<\/a> solutions on our <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail page.<\/a>\r\n\r\n \r\n\r\n<\/div>\r\n<div class=\"segment style-scope ytd-transcript-segment-renderer\" tabindex=\"0\" role=\"button\" aria-label=\"3 seconds all right well my name is Reed Hayes and I'm a faculty at the University of Florida as a criminologist in the\">\r\n<div class=\"segment-start-offset style-scope ytd-transcript-segment-renderer\" tabindex=\"-1\" aria-hidden=\"true\"><\/div>\r\n<\/div>\r\n<div class=\"segment style-scope ytd-transcript-segment-renderer\" tabindex=\"0\" role=\"button\" aria-label=\"36 minutes, 25 seconds pleasure with you thank you all right\"><\/div>",
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"post_content": "Analyzing big data is crucial for organizations to make smarter data-driven decisions\u2014but not all big data is created equally. It\u2019s important to make the distinction between structured data and unstructured data:\r\n<ul>\r\n \t<li><strong>Structured data<\/strong>\u00a0is information that follows a highly organized, predefined schema, making it easy to search through and query. If it can fit inside a relational database or Excel spreadsheet with intersecting rows and columns, it\u2019s structured data.<\/li>\r\n \t<li><strong>Unstructured data<\/strong>\u00a0is any information that doesn\u2019t fall into the neat categories of structured data. Examples of unstructured data include text, images, and audio files.<\/li>\r\n<\/ul>\r\nIn particular, videos are a prime example of unstructured data. Rather than the digital bits and bytes that make up video files, what\u2019s really of interest is high-level information about what the video contains and depicts\u2014from using\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a> on individuals who appear in the video to detecting dangerous events with <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">fire detection<\/a>\u00a0and\u00a0fall detection.\r\n\r\nBut how can you extract this information in an automated, efficient manner? In other words, how can you turn videos into structured data?\r\n<h2>How to Create Structured Data from Videos<\/h2>\r\nTo create structured data from videos, organizations are using sophisticated\u00a0<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">computer vision<\/a>\u00a0and artificial intelligence techniques. Computer vision platforms like\u00a0Chooch\u00a0can help anyone build state-of-the-art\u00a0AI models\u00a0that analyze videos frame by frame, detecting the people, objects, or events that you\u2019ve trained them to look for.\r\n\r\nOnce you\u2019ve gone through the\u00a0AI training\u00a0process, your computer vision model can automatically annotate each frame of the video. AI models can detect the motion of a person or object throughout the scene, as well as detect various actions and events. These annotations, which are saved as structured data, can then be searched and queried to retrieve the most relevant parts of the video.\r\n<h2>Use Cases of Computer Vision for Video Data<\/h2>\r\n<ul>\r\n \t<li><strong>Media AI:<\/strong>\u00a0Computer vision has a wide range of applications in the media and entertainment industries. For example, you can automatically tag the objects in a livestream video, allowing for better targeted advertising. You can also perform facial recognition on the people (e.g., celebrities) in a video, making it easier for people to find who and what they\u2019re looking for.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\"><strong>Safety & Security AI<\/strong><\/a><strong>:<\/strong>\u00a0In the field of safety and security, computer vision can help protect public spaces from threats and dangers. <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">Facial recognition models<\/a> can confirm that an individual is authorized to access the premises, while\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE detection<\/a>\u00a0models can make sure that workers are wearing the appropriate clothing and equipment on the job.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/solutions\/geospatial-ai-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Geospatial AI:<\/strong><\/a>\u00a0Computer vision models can efficiently analyze drone and satellite images and videos. For example, you can train an AI model to detect wildfires, analyze drought or industrial activity levels, identify different animal species, and much more.<\/li>\r\n<\/ul>\r\n<h2>Conclusion<\/h2>\r\nIt\u2019s now easier than ever for your unstructured video data to become structured, thanks to computer vision and AI. Want to learn more about how Chooch can help?\u00a0Get in touch with Chooch\u2019s team of computer vision experts for an <a href=\"\/see-how-it-works\/\">AI demo<\/a>.",
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"post_content": "The reasons for AI project failure are numerous, diverse, and complex. One of the biggest causes, however, is lacking the\u00a0necessary\u00a0technical skills\u2014whether from in-house data scientists and machine learning engineers, or from a strong third-party <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> consulting partner.\r\n<h2>AI Success With Computer Vision Consulting<\/h2>\r\nDespite the enormous potential of state-of-the-art computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a> technologies, many businesses are still struggling to fulfill their expectations. According to a 2019 report by Pactera Technologies, <a href=\"https:\/\/www.techrepublic.com\/article\/why-85-of-ai-projects-fail\/\">85 percent of AI projects ultimately fail<\/a>. Another report from Dimensional Research found that <a href=\"https:\/\/www.techrepublic.com\/topic\/artificial-intelligence\/\">nearly 8 out of 10 organizations<\/a> using AI and machine learning say that their projects in these domains have stalled.\r\n\r\nSo, with these statistics in mind, how can you beat the odds and make your own computer vision project a success, without having to get a triple degree in the fields of computer science, computer engineering, and image processing? While it\u2019s never a guarantee, there are a number of actions you can take to dramatically improve the likelihood that users will adopt your <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision solution<\/a>.\r\n\r\nBelow, we\u2019ll go over 5 steps to accelerate adoption, based on our years of computer vision consulting expertise.\r\n<h2>Computer Vision Consulting Step 1: Understand the Potential<\/h2>\r\nThe first step is to understand the potential that computer vision, AI, and big data can bring to your organization: for example,\u00a0using computer vision for real time analysis of medical imaging,\u00a0or for improving the quality of computer graphics. This means a careful and considerate assessment of your existing processes, workflows, and pain points.\r\n\r\nFrom there, you can highlight a few key priorities that you plan to focus on. Doing your research and finding a case study for companies in a similar position and industry can help you set your expectations for the project appropriately.\r\n\r\nAccording to the consulting firm McKinsey & Company, companies that focus on a few clearly defined themes and priorities during digital transformations are <a href=\"https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/digital-transformation-improving-the-odds-of-success\">1.7 times more likely to have the project exceed their expectations<\/a>. Understand how your chosen initiative will impact your broader business strategy\u2014for example, how it can help you obtain a competitive advantage, or improve your customer experience. Getting all hands on deck and everyone in agreement\u2014from key decision-makers like managers and executives, to the workers in the trenches\u2014is crucial to ensure the project\u2019s success.\r\n<h2>Computer Vision Consulting Step 2: Plan a Deployment<\/h2>\r\nFrom there, your next step is to plan and execute the roadmap you\u2019ve created for the project.\u00a0Having the right mix of people and skills is an essential qualification\u2014but unfortunately, one that too many businesses aren\u2019t able to meet. According to research by Boston Consulting Group, <a href=\"https:\/\/www.bcg.com\/publications\/2020\/increasing-odds-of-success-in-digital-transformation\">only 1 in 4 organizations<\/a> have access to the talent they need for a successful digital transformation.\r\n\r\nCommitting to visibility and transparency throughout the deployment process will be critical, so that key stakeholders can understand what\u2019s going on at all times. The same McKinsey study also found that companies whose senior leaders make digital transformation a \u201ctop priority\u201d are <a href=\"https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/digital-transformation-improving-the-odds-of-success\">1.5 times more likely to see results that beat their expectations<\/a>.\r\n<h2>Computer Vision Consulting Step 3: Train the System<\/h2>\r\nLike children and dogs, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> models must be well-trained. The training process can mean the difference between a model that achieves world-class performance, and a model that performs barely better than chance.\r\n\r\nThe sub-steps required in the process of computer vision training include:\r\n<ul>\r\n \t<li>Sourcing and\/or generating a large, diverse dataset.<\/li>\r\n \t<li>Dividing the original dataset into training, validation, and test sets, each one representative of the larger dataset.<\/li>\r\n \t<li>Selecting the right deep learning model architecture.<\/li>\r\n \t<li>Training the system, assessing its performance on the training and validation sets, and adjusting hyper-parameters such as the length of training or the size of the model. This cycle is repeated as necessary until you obtain a satisfactory level of performance.<\/li>\r\n \t<li>Assessing the system\u2019s final performance on the test set, which thus far\u00a0the trained model has not seen.<\/li>\r\n<\/ul>\r\n<h2>Computer Vision Consulting Step 4: Prove the Concept<\/h2>\r\nOnce you\u2019ve gained some confidence in your trained <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision system<\/a>, you can dip your toes in the water by deploying it as a proof of concept\u00a0(POC). Having a successful POC will ensure you\u2019re on the right track, and can help seal the deal for stakeholders to invest more time, money, and resources in the project.\r\n\r\nTo identify the best choices for a proof of concept, make sure that the domain in which the project will be deployed is relevant to your business strategy\u00a0as a whole. Try to strike the right balance between a domain that\u2019s adequately complex and relevant, without being too difficult to implement. In addition, your choices of metrics and key performance indicators (KPIs) should be easily measurable, in order to understand the impact of the project.\r\n<h2>Computer Vision Consulting Step 5: Generate an ROI<\/h2>\r\nLast but certainly\u00a0not least, your computer vision project needs to generate a return on investment before it can be judged a success. Patience may be key here: many tech executives expect the ROI of their AI projects to take as long as <a href=\"https:\/\/www.techrepublic.com\/topic\/artificial-intelligence\/\">3 to 5 years<\/a>.\r\n\r\nDetermining the ROI of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision and AI<\/a> initiatives may include, but is certainly not limited to, cold financial figures. The factors below are just a few to consider when assessing a project\u2019s ROI:\r\n<ul>\r\n \t<li>Savings in labor costs by automating manual work, and\/or the benefits of redirecting human employees to higher-level activities.<\/li>\r\n \t<li>Cost savings as a result of issues such as bottlenecks, downtime, and maintenance.<\/li>\r\n \t<li>Providing higher-quality products or a better customer experience.<\/li>\r\n \t<li>Improved safety and security measures.<\/li>\r\n \t<li>The benefits of making your organization more flexible, agile, and adaptable.<\/li>\r\n<\/ul>\r\n<h2>Conclusion<\/h2>\r\nBy following the 5 steps above, you\u2019ll find that your computer vision project will be a lot easier to implement\u2014and much more likely to succeed. Building your own computer vision solution can be complicated and challenging, which is why you can improve your experience with the help of computer vision consulting experts like Chooch AI. The <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a> is a user-friendly, all-in-one solution for building and deploying computer vision models, from data collection to training, that has helped countless organizations achieve their <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision and AI<\/a> goals.\r\n\r\nStill in the research stage for your next computer vision project? Feel free to ask us for computer vision consulting, essentially a chat about your needs and objectives for computer vision.",
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"post_date": "2023-01-18 10:32:28",
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"post_content": "In this 30-minute podcast, Jonathan Westover of HCI interviews Michael Liou of Chooch AI on <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety with computer vision<\/a>. \"If you're in a workplace, let's say a warehouse and you want to demonstrate a culture of safety and compliance, and you can count how many times people wearing their hard hats and ensuring people were under safety vests and detecting any smoke or fire.\" Listen now or read on.\r\n\r\n<iframe src=\"https:\/\/open.spotify.com\/embed-podcast\/episode\/2SejLNlEMx94Z8tHKrxfnL\" width=\"100%\" height=\"232\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nListen now at\u00a0HCI Podcast Site for more interviews or read the transcript below.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nWelcome to the Human Capital Innovations Podcast. In this HCI podcast episode, I talk with Michael Liou about the latest trends and developments related to <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">AI in the workplace<\/a>.\r\n\r\nMichael:\r\nThanks Jonathan. Thanks for having me today.\r\n\r\nJonathan H. Westover, Ph.D.:<img class=\"wp-image-1790 size-full alignright\" src=\"\/wp-content\/uploads\/2023\/07\/workplace-safety-ai.png\" alt=\"Workplace Safety AI\" width=\"300\" height=\"404\" \/>\r\nYeah, it's a real pleasure. I'm excited to have this conversation with you. It was fun getting to know you a little bit in the pre-interview as we were just talking and getting to know each other and getting ready for the episode and you have expertise in an area that I know listeners are super tuned into. That is AI; <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">AI in the workplace<\/a>. And how do we leverage artificial intelligence and deep machine learning to better our workplaces in a variety of ways? So that'll be the topic that we explore together today, and I'm really excited for the conversation as we get started. I wanted to share Michael's bio with everybody. Michael Liou is VP of Strategy and Growth at <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a>, where he brings a unique blend of strategy, marketing investment, venture capital, and product development skills.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nMichael brings an extensive background in venture capital where he has been an active early stage investor, investing in over 100 tech startups, including notable unicorns, such as Robinhood. Michael worked with Citi Private bank, serving ultra high net worth family offices in the Bay Area. Prior to his role at Citi, Michael founded Anvil Capital Advisors. He also spent 18 years at Goldman Sachs as a Managing Director overseeing several business units, both in New York and San Francisco. He holds a Master of Business Administration from NYU Stern School of Business, a Master of Science from Columbia University's School of Engineering and Applied Sciences and a Bachelor of Science from Brown University. He recently served on the board of the San Mateo Public Library Foundation. It is a real pleasure to have you Michael, so much expertise, such a rich career, and so many insights. I'm sure you're going to be able to provide, as we launch into the conversation about artificial intelligence in the workplace. Before we do that, though, is there anything else you would like to share with listeners by way of your personal background or context that would lend itself to this conversation?\r\n\r\nMichael:\r\nWell, thanks Jonathan. I think probably the only thing I would like to share is in my last 10 years of investing, I've encountered a lot of great companies and ironically <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> was actually the first company I invested in, actually back in 2016. And it actually was my largest seed check in the last five plus years or so. And so I've been quite fortunate to have a front row seat watching this company grow and develop this technology for the first three and a half years or so. And then find it somewhat ironic that in December of 2019, I was asked by the CEO Emrah Gultekin to actually join as Head of Strategy and Growth. And so now I find myself back in technology where I originally started with Bell Labs Research back in the Eighties, now working with some of the best and brightest people at this AI company.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nYeah, that's really great. And again, you have a really nice complimentary set of experiences in your portfolio, the work that you've done over the years throughout your career. And it's led you to this space now where you get to, as VP of Strategy, you get to lead out with this AI development and integration across organizations. So you're attuned to all the latest trends, all of the latest developments in the field. And I'm super interested in learning from you today and having a conversation about what those are and how we can best as people managers, as organizational leaders, how we can best leverage the existing technologies to improve the lives of our people to improve the situations that we find ourselves in within the workplace. So why don't we start by just exploring some of the current trends? What are you seeing in this space and what is, well let's start there? What are the current AI trends? And then we can start to move into how we see that moving into the workplace.\r\n\r\nMichael:\r\nSure Jonathan, and thanks again. So when we think about our capabilities as a visual AI company or computer vision, we understand that there are still some bottlenecks out there in terms of the development cycle and what this company has done has really shortened that cycle in terms of generating data sets, in generating these computer vision models, but also putting them out on edge devices. And that means that the compute is actually happening right on the factory floor, right in the warehouse, right in a retail outlet where we can now maintain a really accurate and a high speed of predictions, if you will. And at the same time, maintain that privacy and protection of data, which is really, really important. This is a trend that we've seen across all industries.<img class=\"wp-image-1995 size-full alignright\" src=\"\/wp-content\/uploads\/2023\/07\/workplace-safety-with-computer-vision-ai.png\" alt=\"Workplace Safety with Computer Vision AI\" width=\"500\" height=\"522\" \/>\r\nMichael:\r\nAnd then second, if you think about visual AI as a sensor, we can actually enhance human productivity, reduce risk and increase yields by kind of replacing some of the more tedious and manual tasks that humans have to go through every single day and then allowing those humans to potentially perform higher level functions. So imagine an assembly line of bottles are coming through and using machine vision to detect the defects on caps and labels. Well, if we can kind of automate that we can potentially re task people to do more complex tasks versus just looking endlessly at a line of bottles, looking for defects as an example. Or imagine-\r\n\r\nJonathan H. Westover, Ph.D.:\r\nCan I just comment on that as one back, a lifetime ago, back when I was a young and saving up money to go to college. I spent my time on an assembly line in a factory. So I've had some of that experience. It's not a pleasant job. You know, it's very tedious. It's mind numbing work. Time moves so slowly. If you want to leverage the potential of people, having them doing those kinds of manual menial tasks day in and day out is not the best way. And so I just wanted to note really quickly cause you made the comment, these technologies... I know people get afraid. They fear AI because they worry about displacement of workers. They worry about automation and the loss of jobs. And certainly there will be tasks and even some jobs and perhaps even some professions that are replaced by machines, but there's going to be so many more opportunities that are created and new jobs, new tasks, new roles that are going to be created because of the technology, just like we've seen at every stage of the industrial revolution.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nAnd as we talk about being in this being industrial revolution 4.0., yeah, we're going to have certain jobs that go away. We're going to have certain tasks that go away, but to the betterment of the workers, ultimately as we re-skill and up-skill and do more complex things that are more interesting and engaging and ultimately more fulfilling. We'll be able to thrive more, I think, as people in the workplace. So that's just a quick comment I wanted to make, because I really do think that that's an important benefit to everything that we see coming down the pipeline with AI, machine learning, automation and such.\r\n\r\nMichael:\r\nAbsolutely Jonathan. And as a matter of fact, one of the other advantages that, computer vision can provide is that degree of consistency and uptime. So imagine a warehouse scenario or an airport scenario where we're trying to look for people who are wearing the appropriate safety gear or looking for people of interest. The human being, when they're focusing on these tasks, can be efficient up to a certain point. And then thereafter, fatigue can potentially set in. You might be texting your friend. You might have to eat lunch or go to the bathroom, whereas a camera outfitted with the appropriate, accurate computer vision models can tirelessly continue to perform those menial tasks and also aggregate that data too. And that data can be stored and analyzed for future analysis.\r\n\r\nMichael:\r\nSo if you're in a workplace, let's say a warehouse and you want to demonstrate a culture of safety and compliance, and you can count how many times people wearing their hard hats and ensuring people were under safety vests and detecting any smoke or fire. When it's very, very early stage, you're going to have fewer accidents. You can decrease the <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">risk of workers in a workplace<\/a>. And ultimately, you're going to be able to decrease frequency and severity. And if that can be demonstrated to an insurance company, there is a significant chance that you can reduce the amount of premium that you paid to the insurance company of workers' compensation. So there isn't a real ROI here, not to mention the cultural and safety indemnity and long-term, and short-term disability benefits that come along with that reduction of risk.\r\n\r\nMichael:\r\nYeah, absolutely. In that, the risk management component is one that I have to admit, as people talk about the benefits of AI, that's not the first thing that most people start to talk about. And it's not the first thing I usually think about. But as an HR professional in the HR and people management space, safety and compliance is a big deal. And if you want to make sure, the premium issue and the cost savings component alone is worth doing this. But also, just the human cost of pain and suffering due to injuries or heaven forbid even death in the workplace; if we can eliminate more of those types of incidences. That's to everyone's benefit, that's the organization's bottom line benefit, but it's also to the team and the individuals' benefit because they're taken care of and safety and compliance is a really tough nut to crack.\r\n\r\nMichael:\r\nI think anyone who works in that space knows how difficult it is and best intentions don't mean much of anything. If you don't actually have the systems and processes in place to make sure that your people are kept safe and they are complying with the rules and the regulations. And so having the AI to assist you like you're describing will be a really huge benefit to organizations. Not just to the bottom line, but like you said, to the culture of safety, to the culture of, not just physical safety. We want psychological <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safety in the workplace<\/a>. We don't want people fearing how they're going to be taken care of at work. We want to take that off the table. We want that to just be a given that people are safe, people will be taken care of.\r\n\r\nMichael:\r\nAbsolutely. There's the financial repercussions. And then as you mentioned, there's the cultural repercussions, culturally meaning of reputational risk, a compliance risk, sanctioned risk, and of course, litigation risk as well too might. So why open yourself up as some of these near misses or behaviors can be tracked and potentially buttoned down so that you reduce the risk overall. It could be something as simple as detecting puddles or slippery surfaces before someone falls down, to sending an alert to someone who is on a ladder who should be wearing a safety harness before they ascend a ladder? Everyone likes to save time, but, you should also make sure that people aren't texting while they're driving a forklift. Or texting when they're near a earth mover, moving around on a commercial work site. So there are many applications of computer vision that seems somewhat innocuous, but when you add them all up, actually can lead to a greater culture and environment of safety and lower risk.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nYeah. I love it. And I just heard, I'm sure you have too. I've heard so many horror stories of people who are just, again, they're cutting corners, not because they're lazy, usually. They're cutting corners because they're trying to be efficient. They're trying to move more quickly. They're trying to produce for company. And a lot of times there is performance pressure being put on them. And so, they have to harness up and they're like, \"Ah, it's just a really quick thing. I can just jump on real quick and I'll be up and down, you know? And if I have to harness up, it's going to take more time.\" And so they just want to do it quickly. That kind of thing, those types of incidences happen all the time. And they're so avoidable, not just because you have someone monitoring and people know that they're monitored, and then they get the alerts like you're describing. But it holds management accountable too, to not put undue pressure on time and efficiency when it is sacrificing safety.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nBecause oftentimes when I've gone into organizations and I've seen a poor safety record, it's usually a dual problem. There's some training issues usually that have to be put in place. There's some processes that need to be fine tuned to make sure that people know what they need to be doing, how and when and all of that. But usually there's also a cultural component due to the pressure being put on the workers by administrators that is unsustainable. And it leads to people cutting corners and those safety corners in the long run are going to be very much more detrimental to the success of the organization than if they take a couple extra minutes to practice those safety procedures. So as you're describing, this visual component, it's an extra layer, it's an extra mechanism to ensure that you're holding everyone accountable, that you're making sure that everyone is taking safety very, very seriously. So what are some of the other trends that you see out there right now, and that your organization's working with in relation to AI in the workplace?\r\n\r\nCommercial Break:\r\nI'm excited to announce the publication of my new book from HCI Press, \"The Alchemy of Truly Remarkable Leadership: Ordinary, Everyday Actions That Produce Extraordinary Results\". Consider how the nature of work has shifted over the past 50 years with increased globalization, rapid technological advancement, and the shift in economic composition. The average job of today looks very different than the average job of 50 years ago. What will the jobs and organizations of tomorrow look like? Moreover, what does this all mean for organizational leaders? What are the core competencies and capabilities of organizations and their leadership that are prepared for continued disruption and geopolitical and socioeconomic shifts? Regardless of what the future holds, increasingly leaders need to be socially minded, data-driven, decisive ,champions of talent and disruptors of the traditional notions of leadership, teams, organizations, and work. \"The Alchemy of Truly Remarkable Leadership\" will help you to explore your own leadership competencies and capabilities and consider ways to apply and implement them into your workplace and personal life.\r\n\r\nMichael:\r\nI would say that safety and security in the workplace is certainly paramount because every industry has a warehouse or every industry has a logistics staging center. And if you think about the trends in retail with omnichannel being here to stay, you've got more pressure to build these logistics centers and these fulfillment warehouses. So there is this kind of breakneck speed to build all this infrastructure, which means more and more people will be entering to the workforce that will be exposed to these dangers. And so, as a result, in this kind of hurry to be competitive, there is this risk again that people may cut corners. There is an OSHA requirement that if you have an injury or accident in the workplace, this needs to be reported in 24 hours. And I'm pretty sure that's not being complied to.\r\n\r\nMichael:\r\nAnd you might be able to get away with cutting corners every single day, but all you need is that literally 1% chance to come to fruition where there's a death or dismemberment, and it just ruins everything. Ruins morale, increases risk. There's a ton of paperwork needs to be fulfilled. And so again, ensuring that there's this compliance and culture of safety, I think is paramount. And this goes for the construction industry as well, where you could argue they're even more dangerous jobs like roofing as an example, which have some of the higher risks out there, or jobs in slaughterhouses, which are also a very, very risky as well. But we've also focused on a public safety and so there's a little bit of overlap. So in addition to detecting PPE equipment in the industrial workplace, we've also developed models for things like smoke detection, fire detection, fall detection in public areas, nursing homes, hospitals, and we've also developed weapons detection to handgun, knife and rifle.\r\n\r\nMichael:\r\nAnd if one's able to detect these anomalies earlier, than potentially emergency response can be activated more quickly and render aid or help all a bit more expeditiously onsite. So imagine a mall where you typically have video cameras everywhere, and three people sitting in the security room, looking for events that don't happen. It might be better if they could actually be walking the premises and having an AI to say, \"Hey, there's a fight breaking out in front of Macy's\" or \"Hey, there's a slippery surface in front of JC Penny's. Someone better mop that up before they crack their skull. And then sue the mall operator as an example.\" So I think public safety is also another area. And this blends into the overall arching theme of smart city. Can we enable computer vision to help our cities safer? Ensuring that people are not driving and using mobile phones at the same time, making sure they have their seat belts on, detecting any areas where there might be a violence breaking out, or there may be a fire or smoke breaking out.\r\n\r\nMichael:\r\nThe current state of technology for detecting smoke and fire indoors is thermal sensors and smoke detectors. Well, in 30 foot ceiling warehouses, those flames need to be pretty big before they start setting those things off. What would happen if we could actually use computer vision to detect smoke and fire at a much earlier stage? You might be able to prevent a full-blown conflagration and a three alarm response from the fire department. You might be able to put it out on your own or elicit a smaller response and be a little bit more preventive. Now I must caution that these technologies aren't regulated. Obviously smoke detectors and heat sensors are regulated and do provide a very good level of safety. But we do think that computer vision, our AI models, could be complimentary into these existing technologies and again, potentially save lives and damage and speed response or initiate sponsor faster.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nYeah, yeah. And that speed response, I think, is one of the critical components whenever you're dealing with these sorts of issues. And you've raised a couple of times now, not just the traditional OSHA safety type concerns, but also things like weapon detection. I don't want to get into a whole political conversation on guns rights and gun issues. But the reality is that we live in a current climate and context where gun violence is fairly prevalent. And so if you can have this type of technology utilized in order to recognize when there's a threat and have quick rapid response to that threat, think about the potential number of lives that can be saved. And some of these incidences, we hear about there's one, what was it just a couple of weeks ago in Colorado, we had another incident. And so if you can utilize this type of technology, I think it's a really great opportunity to provide that level of safety and security for people.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nAnd you also brought up the regulatory piece and how that currently isn't in place for this technology. My next kind of thought feeds off of that comment. And that is, what are some of the ethical considerations that we need to really be thoughtful about and careful about as we're integrating more of these AI technologies into the workplace, or really into our communities at large? Because certainly, in terms of the surveillance piece and some of these other elements, there's certainly a potential for concern. So what is your organization doing to help with that kind of ethical AI drive to make sure that we're doing things properly and what should organizations and leaders listening to this podcast today? What should they be considering as they consider those ethical components to utilizing your technology and other similar technologies?\r\n\r\nMichael:\r\nYeah, this is a really, really important topic, Jonathan, when it comes to computer vision. And if you think about the models that are out there, there are two main concerns that you can now break this into. One is obviously privacy THe second is bias and bias in models comes from a bias in data that helps train those models. So that's really, really important. And when people talk about bias, they primarily focus more on the official recognition aspects. So it's known that a lot of, some of the public models that are out there, I have biases towards people of color, people who are Asian of descent as an example. And so people have to kind of go back to the drawing board, right? If they're going to use facial, they need to use it in a much less biased manner.\r\n\r\nMichael:\r\nSo that's one concern. Second is the privacy issue in the sense that most stores have camera monitoring systems already. And those videos are actually being digitally stored already. And the difference is that typically these videos are monitored by a staff of humans, either onsite or offsite for any security. And so they're kind of watching you anyway, right? What we need to be careful about with computer vision is that we need to use it for the basis of good - for the public good, if you will. And so when people say, \"Are you recording my face? Can you recognize who we are?\", it depends upon what computer models are actually loaded into the system. So, as an example, if I just have fire and smoke detection and fall detection loaded into our system, then that's all it's going to detect.\r\n\r\nMichael:\r\nSo it's not going to take human faces. It's not going to do demographics. It's not going to determine racial or ethnicity amongst the population. And so there is a degree of control that management has in terms of when they implement certain particular models. You could implement weapons detection, fire and smoke for just a public safety aspect. And again, there's no facial recognition happening. Those cameras, there's no database to cross reference people. So I think it's important to kind of clarify what can and cannot be done.\r\n\r\nMichael:\r\nThere are companies who do focus on facial, and there are companies who have extensive databases of people's identities matched up with faces. I can tell you at <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a>, we do not do that. We do have private enterprise inquiries where people who want to use facial for things like airplane check-in, airplane boarding, a cruise line check-in, a hospitality access control in the public and private sector, into restricted areas. And I think that's fine, right? As a matter of fact, when you use facial for access control, you kind of eliminate the risk for any type of potential compromise, like giving someone your passcode or giving someone your key card. And actually made things a little bit safer. But again, I think it's important to understand that the computer vision models will do and be trained specifically for its particular task.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nYeah. Excellent. Excellent. I think those are all important points and we really just scratched the surface of the conversation in relation to the ethical components. Really, we scratched the surface on all the AI potential within the workplace, but those ethical components, we can't forget about that. And sometimes we get super excited about the engineering piece of the technology and the capabilities. But if we sever the continued development of the technology from the ethical conversations, then that's where we can take the humanity out of the workplace potentially and find ourselves in other difficult situations. I appreciate that lens that you added to our conversation here today. And I would encourage listeners, have these meaningful conversations with your C-suite leadership and leadership down the organization and talk about how the types of applications that Michael has been describing, how it could be utilized while keeping in mind that those ethical components in the protections for your people and for the customers.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nMichael, it has been a real pleasure talking with you today. I do want to be mindful of your time. I recognize you're very busy and we'll need to move on to the rest of your day. But before we close, I wanted to give you a chance to reach out or to share with listeners how they can get connected with you, find out more about your organization and how you can benefit them. And then give us the final word on the topic for today.\r\n\r\nMichael:\r\nYeah, sure Jonathan. More information can be found on our website at chooch.ai. It's a fairly rich website with a number of different links to videos, capabilities, verticals, as well as demo links to our best capabilities. We are very horizontal on Jonathan. So we have applications in many different industries, ranging from oil and gas to healthcare, media, retail, industrial warehouses, and even geospatial capabilities. So I think the team here has worked very, very hard in creating a very powerful and flexible platform. And we're looking to help other organizations. I hate to overuse this word, use computer vision for digital transformation, reduce risk, potentially enhance safety, increase yield, decrease costs, and just make their workplace more efficient.\r\n\r\nJonathan H. Westover, Ph.D.:\r\nWonderful. Thank you, Michael. It has been a real pleasure talking with you. I encourage listeners to reach out, get connected with Michael, find out more about what he and his company can do for you. And as always, I hope everyone can stay healthy and safe, that you can find meaning and purpose at work each and every day. And I hope you all have a great week.\r\n\r\n ",
"post_title": "Human Capital Innovations Podcast: Workplace Safety AI",
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"post_date": "2023-01-18 10:30:47",
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"post_content": "Industrial and manufacturing are two of the most high-risk sectors for workers\u2014but that doesn\u2019t mean you can\u2019t enact safeguards and protections for worker safety while on the job. By using AI to enforce compliance with all applicable environment, health, and safety (EHS) regulations, you\u2019ll be much more likely to avoid accidents and the long-term complications they entail for both workers and their employers.\r\n\r\nAccording to the U.S. Bureau of Labor Statistics, there were <a href=\"https:\/\/www.bls.gov\/news.release\/pdf\/osh.pdf\" target=\"_blank\" rel=\"noopener\">2.8 million nonfatal workplace injuries and illnesses in 2019<\/a>. This includes over 400,000 nonfatal injuries and illnesses in the manufacturing sector, which accounts for 15 percent of all workplace accidents in private industry. Meanwhile, according to the<img class=\"alignleft wp-image-1995\" src=\"\/wp-content\/uploads\/2023\/07\/ppe-detection-with-computer-vision.png\" alt=\"PPE Detection with Computer Vision AI\" width=\"347\" height=\"362\" \/> National Safety Council, the construction sector experienced the most workplace deaths in 2019, making it arguably the <a href=\"https:\/\/injuryfacts.nsc.org\/work\/industry-incidence-rates\/most-dangerous-industries\/\" target=\"_blank\" rel=\"noopener\">\u201cmost dangerous industry.\u201d<\/a>\r\n\r\nFar too many injuries in industrial and manufacturing settings are preventable\u2014and many of these are due to improper usage of <a href=\"https:\/\/www.chooch.com\/blog\/how-to-detect-ppe-compliance-in-auto-parts-manufacturing-with-ai\/\">personal protective equipment (PPE)<\/a>. For industrial and manufacturing occupations, appropriate PPE may include:\r\n<ul>\r\n \t<li><strong>Hard hats, safety helmets, and other headwear.<\/strong> Safety headgear protects workers from head injuries such as impacts, falling and flying objects, and burns and electrical shocks.<\/li>\r\n \t<li style=\"list-style-type: none;\">\r\n<ul>\r\n \t<li style=\"list-style-type: none;\">\r\n<ul>\r\n \t<li><strong>Safety glasses and other protective eyewear.<\/strong> Safety glasses protect workers from hazards such as debris and flying particles, heat, light, and radiation.<\/li>\r\n \t<li><strong>Safety vests and other reflective apparel.<\/strong> Safety vests improve workers\u2019 visibility, alerting other people to their presence and protecting them from accidental impacts from vehicles and heavy machinery.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<strong>The benefits of enforcing proper industrial PPE usage include:<\/strong>\r\n<ul>\r\n \t<li><strong>Fewer workplace accidents.<\/strong> Wearing PPE reduces both the frequency and the severity of <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace accidents<\/a>.<\/li>\r\n \t<li><strong>Better company culture.<\/strong> Employees feel better working for a company that demonstrates it cares about worker safety.<\/li>\r\n \t<li><strong>Fewer lost wages and productivity.<\/strong> Injured employees lose out on wages and may have to go on short- or long-term disability, while employers miss out on the employee\u2019s productivity and expertise.<\/li>\r\n \t<li><strong>Lower insurance premiums.<\/strong> Limiting the number of workplace accidents also helps employers avoid additional insurance costs.<\/li>\r\n \t<li><strong>Decrease in litigation.<\/strong> Fewer workplace accidents means less stress and expenses associated with personal injury or wrongful death lawsuits.<\/li>\r\n<\/ul>\r\n<h3>Detecting proper industrial PPE usage with computer vision<\/h3>\r\nDespite the benefits of wearing PPE, your managers and employees are only human\u2014so how can you ensure that\u00a0your workers are actually using PPE properly at all times? The answer lies in AI and computer vision models that can analyze camera footage in a fraction of a second, detecting safety headgear, glasses, and vests (and sending alerts when they\u2019re not detected).\r\n\r\nThe feature-rich, yet user-friendly, <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a> brings the power of computer vision to the masses. From the Chooch AI dashboard, users can easily oversee and manage their devices and models. Chooch gives users the freedom to train and deploy sophisticated custom AI models that fit their needs and use cases\u2014including detecting whether workers are properly using PPE while on the job site.\r\n\r\nYou can see the PPE detection process in action in our short video <a href=\"https:\/\/www.chooch.com\/blog\/safety-ai-model-ppe-detection-video\/\">AI Models for PPE Compliance<\/a>. Learn more how computer vision can help <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">protect your workers and create a workplace safety culture<\/a>.\u00a0 You can also <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">get in touch with our team<\/a> of AI experts for a chat about your business needs and objectives",
"post_title": "Save Lives and Lower Costs\u2014PPE Detection with Computer Vision AI",
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"post_content": "Edge AI is the intersection of <a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\">edge computing<\/a> and artificial intelligence: an AI paradigm that performs as much computation as possible on <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-edge-device\/\">\u201cedge\u201d devices<\/a> that are physically located close to the source of the data. This is in contrast to traditional approaches that first upload the data to remote servers running in the cloud, where the computation is then performed.\r\n\r\nWhat are the benefits of edge AI over \u201ctraditional\u201d Internet of Things (IoT) and cloud computing methods? The advantages of <a href=\"https:\/\/www.chooch.com\/blog\/the-value-of-edge-ai\/\">edge AI<\/a> include:\r\n<ul>\r\n \t<li><strong>Greater privacy and security:<\/strong> If data privacy and security are a concern for your organization, edge AI may be the best fit. Data is stored and processed locally, instead of being sent to a third-party cloud computing provider.<\/li>\r\n \t<li><strong>Improved latency:<\/strong> Since you don\u2019t have to wait for results to come back from the cloud, <a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\">edge computing<\/a> has much lower latency.<\/li>\r\n \t<li><strong>Lower costs:<\/strong> Edge computing saves you money on the costs of data traffic and cloud computing services. Once you purchase the edge device itself, your expenses are fairly low.<\/li>\r\n \t<li><strong>Better reliability:<\/strong> Edge AI can continue to function even in the event of an outage that disrupts communications or cloud operations.<\/li>\r\n<\/ul>\r\nEdge AI is especially useful for resource-intensive applications such as computer vision, which relies heavily on images and videos. There\u2019s no need to pay the costs of sending this visual data to the cloud when you can analyze it yourself on an <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-edge-device\/\">edge device<\/a>.\r\n\r\nRecent technological advancements in CPUs and GPUs have made edge devices more powerful than ever before. This means that it\u2019s easier than ever to run powerful <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> on the <a href=\"https:\/\/www.chooch.com\/blog\/the-value-of-edge-ai\/\">edge<\/a>.\r\n\r\nWhat\u2019s more, this trend is only expected to accelerate in the near future. According to the intelligence firm MarketsandMarkets, the global market value of <a href=\"https:\/\/www.chooch.com\/blog\/the-value-of-edge-ai\/\">edge AI<\/a> will skyrocket from $590 million in 2020 to <a href=\"https:\/\/www.marketsandmarkets.com\/Error_Page.asp\" target=\"_blank\" rel=\"noopener noreferrer\">$1.8 billion in 2026<\/a>\u2014a blistering annual growth rate of 21 percent.",
"post_title": "What is Edge AI?",
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"post_date": "2023-01-18 10:29:22",
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"post_content": "5G and edge computing are inextricably intertwined technologies: each one enables the other. <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge computing<\/a> depends on fast speeds and low latency in order to transfer large quantities of data in near real time\u2014exactly what 5G is good at providing. For its part, 5G needs applications such as edge computing in order to justify its rollout to wider coverage areas. 5G allows for more and more computing to be done at the edge where the users and devices are physically located, offering unprecedented connectivity and power. The rollout of new technology developments such as\u00a0edge computing\u00a0and the 5G wireless network standard has created waves of excitement and speculation across the entire industry.\r\n\r\nEven better news: 5G and edge computing can act as force multipliers for applications such as <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision and AI<\/a>. Edge devices, connected to the vast Internet of Things (IoT) via 5G, can collect, process, and analyze images and videos themselves, without having to send this data to the cloud for processing.\r\n<h2>5G and Edge Computing for AI and Computer Vision<\/h2>\r\n<img class=\"alignleft wp-image-2569\" src=\"\/wp-content\/uploads\/2023\/06\/5g-and-edge-computing-ai-and-computer-vision.png\" alt=\"5G and Edge Computing for AI and Computer Vision\" width=\"575\" height=\"218\" \/>\r\n\r\nMobile industry organization GSMA, for example, predicts that by 2025, 5G connections will account for\u00a0<a href=\"https:\/\/www.gsma.com\/mobileeconomy\/wp-content\/uploads\/2020\/03\/GSMA_MobileEconomy2020_Global.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">20% of connections worldwide<\/a>\u00a0and 4% in North America. 5G is predicted to deliver speed improvements that are\u00a0<a href=\"https:\/\/www.highspeedinternet.com\/resources\/4g-vs-5g\" target=\"_blank\" rel=\"noopener noreferrer\">up to 10 times faster<\/a>\u00a0than the current 4G network\u2014so it\u2019s no wonder that users are rushing to adopt this latest innovation. Meanwhile, IT analyst firm Gartner projects that in 2022,\u00a0<a href=\"https:\/\/www.gartner.com\/smarterwithgartner\/gartner-top-10-trends-impacting-infrastructure-operations-for-2020\" target=\"_blank\" rel=\"noopener noreferrer\">half of enterprise data<\/a>\u00a0will be generated and processed on the edge, away from traditional data centers and cloud computing.\r\n\r\nThe possibilities of computer vision at the edge, powered by 5G, are nearly limitless. <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge computing<\/a> and 5G can significantly enhance and unlock the potential of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision and AI technologies<\/a>.\r\n\r\nHere\u2019s how the process typically works: an edge device captures visual data through a camera or other sensor, and then send\r\n\r\ns it to the device\u2019s GPU (graphics processing unit). An\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI model<\/a>\u00a0stored on the device then uses the GPU to rapidly analyze the contents of these images or videos, and can immediately send off the results using 5G technology.\r\n\r\nThere are many different reasons you might prefer to run computer vision models on edge devices, rather than in the cloud:\r\n<ul>\r\n \t<li>Since you don\u2019t own the servers yourself, running AI models in the cloud is a recurring expense. On the other hand, edge devices allow you to make a single, relatively cheap capital investment that you then own and can operate as you please.<\/li>\r\n \t<li>As data volumes continue to rise, processing this data locally and independently can help deal with data bloat.<\/li>\r\n \t<li>Use cases such as self-driving cars depend on near-instantaneous, highly accurate insights, and can\u2019t afford the latency of exchanging data with the cloud.<\/li>\r\n<\/ul>\r\nBelow are a few more computer vision use cases in which high speed and high accuracy are of the essence:\r\n<ul>\r\n \t<li><strong>Image recognition<\/strong>\u00a0models can identify the objects and people in a given image, accurately choosing from hundreds or thousands of categories. For example, edge devices can examine surveillance videos at a construction site to ensure that all <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">workers are wearing\u00a0PPE (personal protective equipment)<\/a>.<\/li>\r\n \t<li><strong>Facial authentication<\/strong>\u00a0models can determine whether a given individual is authorized to access a restricted area, with very high accuracy, in just a fraction of a second.<\/li>\r\n \t<li><strong>Event detection<\/strong>\u00a0models analyze streams of visual data over time to detect a given event, such as\u00a0<a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">smoke and fire detection<\/a>\u00a0or\u00a0<a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">fall detection<\/a>.<\/li>\r\n<\/ul>\r\nIn all of\u00a0these use cases and more, <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge computing<\/a> and 5G can help boost speeds and lower latency, delivering immediate value for local users.\r\n<h2>Case Study: Wind River 5G Edge AI<\/h2>\r\nWind River is a leading technology firm that builds software for embedded systems and edge computing. The company recently partnered with NVIDIA\u2014which builds powerful, compact edge devices like the\u00a0NVIDIA Jetson\u2014to create a converged technology stack with multiple edge functions, including 5G, intelligent video analytics, and augmented and virtual reality.\r\n\r\nThe company\u2019s demo, which was showcased at NVIDIA\u2019s GPU Technology Conference (GTC) 2021, was built together with Chooch and other technology partners. Chooch helped provide multiple containerized solutions for intelligent video analytics and computer vision, with use cases ranging from safety and surveillance to <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> and <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a>. Powered by NVIDIA\u2019s GPUs, Wind River and Chooch could demonstrate the tremendous potential that <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge computing<\/a>, now enabled by 5G, can have for users of all industries.\r\n\r\nWant to learn more?\u00a0<a href=\"https:\/\/gtc21.event.nvidia.com\/media\/Learn%20How%20Wind%20River%20and%20NVIDIA%20are%20Enabling%20a%20Converged%20Edge%20Platform%20to%20Deliver%20Computer%20Vision%2C%20AR_VR%2C%20and%205G%20Services%20%5BS32165%5D\/1_m0yynon2\" target=\"_blank\" rel=\"noopener noreferrer\">Check out this short presentation<\/a>\u00a0from Wind River\u2019s Gil Hellmann at NVIDIA\u2019s GTC 2021. You can also\u00a0<a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener noreferrer\">get in touch with Chooch\u2019s team of computer vision experts today<\/a>\u00a0for a chat about your business needs and objectives, or to start your free trial of the <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch computer vision platform<\/a>.",
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"post_content": "Large-scale industrial operations manage infrastructure networks spanning hundreds of miles. An energy company, for example, needs to maintain vast networks of electrical wires, distribution poles, transmission towers, electrical substations, and other critical assets. Because these assets are often located in remote and dangerous locations, inspecting them for maintenance issues demands high-risk expeditions, skilled labor, and an enormous amount of time and financial resources.\r\n\r\n<img class=\"wp-image-7671 size-full aligncenter\" src=\"https:\/\/www.chooch.com\/wp-content\/uploads\/2023\/01\/detecting-leak-and-rusty-pipe-using-computer-vision.jpg\" alt=\"Detecting Rusty Pipe\" width=\"1000\" height=\"525\" \/>\r\n\r\nToday, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> empowers industrial companies to achieve safer, more accurate, and more cost-effective inspections of key infrastructure components. Whether the computer vision inspection strategy involves IoT-connected smart cameras installed in remote locations, drones collecting visual data by air \u2013 or satellite imagery \u2013 a well-trained visual AI system can detect maintenance problems, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">environmental hazards, and safety<\/a> concerns with higher degrees of accuracy than traditional methods of infrastructure inspection.\r\n\r\nGet a demo of AI for infrastructure inspection with <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">Industrial AI<\/a>.\r\n<h3>Traditional Methods of Infrastructure Inspection<\/h3>\r\nThe inspection and maintenance of critical infrastructure components is a necessary part of nearly every large-scale industry. Some common infrastructure types requiring inspections include:\r\n<ul>\r\n \t<li>Power lines<\/li>\r\n \t<li>Utility infrastructure<\/li>\r\n \t<li>Construction sites<\/li>\r\n \t<li>Cell towers<\/li>\r\n \t<li>Coastal shoreline erosion<\/li>\r\n \t<li>Bridges<\/li>\r\n \t<li>Roads and interstates<\/li>\r\n \t<li>Hydroelectric dams<\/li>\r\n \t<li>Wind turbines<\/li>\r\n \t<li>Solar farms<\/li>\r\n \t<li>Industrial agriculture<\/li>\r\n \t<li>Wastewater and effluent<\/li>\r\n<\/ul>\r\nEach of the above use cases has developed its own standards and procedures for infrastructure inspection and maintenance \u2013 including inspections for compliance with environmental statutes. Traditional methods for performing these inspections may include the use of:\r\n<ul>\r\n \t<li>Human visual inspectors<\/li>\r\n \t<li>Photogrammetry<\/li>\r\n \t<li>Orthomosaics<\/li>\r\n \t<li>3D models<\/li>\r\n \t<li>Geospatial information services<\/li>\r\n \t<li>Aerial inspections by helicopter<\/li>\r\n \t<li>Drone-based aerial photography<\/li>\r\n \t<li>Satellite imagery<\/li>\r\n \t<li>LiDAR<\/li>\r\n \t<li>Ultrasound<\/li>\r\n \t<li>Liquid penetration inspection (LPI)<\/li>\r\n \t<li>Radiography<\/li>\r\n \t<li>Infrared camera footage<\/li>\r\n \t<li>Surveillance cameras installed at various inspection sites<\/li>\r\n<\/ul>\r\nAside from using advanced technology, trained and experienced professionals are an integral part of most inspection processes. These inspectors frequently endure dangerous conditions to perform in-person evaluations using the human eye alone with no special instrumentation. Often traveling to remote areas by helicopter, working out of bucket trucks \u2013 or climbing up towers, wind turbines, bridges, and electrical distribution poles \u2013 human inspectors need to evaluate the condition of key assets to answer a host of questions.\r\n\r\nInfrastructure inspectors may need to consider questions such as:\r\n<ul>\r\n \t<li>Is it rusty?<\/li>\r\n \t<li>Is it structurally sound?<\/li>\r\n \t<li>Are trees growing over a transformer box?<\/li>\r\n \t<li>Are guy-wires intact?<\/li>\r\n \t<li>Are key actions happening on schedule?<\/li>\r\n \t<li>Is wastewater flowing properly through drain pipes?<\/li>\r\n \t<li>Is it overheating?<\/li>\r\n \t<li>Is it the wrong color?<\/li>\r\n \t<li>Are power lines broken or hanging too low?<\/li>\r\n \t<li>Is it leaking?<\/li>\r\n<\/ul>\r\n<h3>The Challenges of Traditional Infrastructure Inspections<\/h3>\r\nLarge-scale industrial operations spend millions of dollars each year to conduct visual inspections that rely on human eyes and human understanding without any special equipment. Until the recent introduction of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a>, the use of human inspectors for these visual evaluations was a necessity. However, the following challenges continue to plague any inspection activities that rely on human eyes:\r\n<ul>\r\n \t<li><strong>Slow and laborious:<\/strong> Getting human workers on-site to perform inspections at thousands of sites across hundreds of miles of distance is difficult, costly, and time-consuming \u2013 resulting in infrequent inspections and detection delays. For example, a wastewater treatment facility could be dumping contaminated effluent into a river for days \u2013 even months \u2013 before a human inspector detects the problem. Similarly, the infrequent inspection of hydroelectric facilities, cell towers, wind turbines, and other infrastructure components means that an inexpensive problem could grow into a devastating catastrophe.<\/li>\r\n \t<li><strong>Risky and dangerous:<\/strong> The process of sending human workers to remote sites to climb cell towers and key pieces of infrastructure is fraught with dangers. For example, workers climbing cell towers face the risk of electrocution, inclement weather (excessive wind, rain, lightning, hail, and snow), objects falling at high speeds, and protective equipment failures. The remoteness of inspection sites \u2013 combined with dangerous inspection tasks and safety training failures \u2013 elevates the chances and severity of injuries.<\/li>\r\n \t<li><strong>Error-prone:<\/strong> Whether they are performing inspections on-site, or remotely while viewing visual data collected by cameras, drones, and other detection equipment, inspectors can only achieve certain levels of accuracy due to the inherent limitations of their human faculties. Human inspectors are commonly overworked, lacking adequate rest, bored, inadequately trained, or without sufficient experience and expertise. These challenges result in errors, mistakes, and inconsistent inspection results.<\/li>\r\n \t<li><strong>Expensive:<\/strong> Hiring, training, and employing skilled human inspectors is costly \u2013 so is transporting inspectors to and from remote inspection sites. Utility companies pay as much as $1,000 per mile for aerial inspections by helicopter. Insurance costs related to these often dangerous inspection activities are another significant expense.<\/li>\r\n<\/ul>\r\nConsider the simple inspection task of monitoring for tree overgrowth around power lines. It\u2019s not uncommon for tree branches to break through power lines, destabilize distribution poles, and short-circuit transformers. Early detection and trimming of trees is essential to prevent blackouts, fires, and electrocution <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards<\/a>. However, it\u2019s costly, time-consuming \u2013 and virtually impossible \u2013 to detect all instances of tree overgrowth. Invariably, an undetected branch could grow in such a way that leads to an expensive or catastrophic problem.\r\n\r\nAnother simple yet problematic inspection task relates to monitoring wastewater effluent for signs of particles, discharges, and discoloration. Human inspectors need to continually check discharge pipes to ensure that wastewater is running clean and on schedule. If not, the problem could represent a costly violation of Environmental, Social, and Governance (ESG) criteria or Socially Responsible Investing (SRI) standards. However, due to the limited number of human inspectors \u2013 and logistical challenges associated with constant monitoring \u2013 it\u2019s not uncommon for factories, plants, and wastewater treatment facilities to unknowingly discharge untreated water directly into the ocean \u2013 sometimes for weeks or months before they detect it.\r\n<h3>Leveraging Computer Vision for Better Infrastructure Inspections<\/h3>\r\nComputer vision technology provides a cost-effective solution for conducting accurate and timely inspections of <a href=\"https:\/\/www.chooch.com\/blog\/industrial-computer-vision-inspection-better-monitoring-of-critical-infrastructure\/\">industrial infrastructure<\/a> assets. In addition to achieving more accurate and consistent results than human-led inspections, visual AI for infrastructure inspection is dramatically safer and more affordable.\r\n\r\nComputer vision strategies for infrastructure inspection leverage the following features:\r\n<ul>\r\n \t<li>High-definition IoT-connected cameras \u2013 including infrared cameras \u2013 mounted in remote locations that observe site conditions.<\/li>\r\n \t<li>Deployment of drones for aerial footage, visual measurements, and automatic identification of potential problems.<\/li>\r\n \t<li>High-resolution satellite topography imagery to show the current condition and status of assets on the ground.<\/li>\r\n \t<li>Edge servers running sophisticated <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> that analyze and interpret visual data, identify maintenance issues, detect environmental <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">hazards<\/a>, and spot instances of fire and overheating.<\/li>\r\n \t<li>Instant alerts, reports, and metrics sent to decision-makers for immediate action on potential problems.<\/li>\r\n<\/ul>\r\nWith Chooch, companies that manage large infrastructure networks can rapidly train computer vision models to detect all types of visually perceivable problems and maintenance concerns. Through the use of drones, on-site surveillance cameras, and satellite imagery, Chooch AI systems can monitor critical infrastructure assets without the time, risk, and cost of transporting human inspectors to remote locations \u2013 and do so faster and more accurately.\r\n\r\nChooch offers industrial companies immediate access to a wide library of pre-built visual <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> for the most common inspection use cases. For more unique scenarios, operators can add layers of training to existing models \u2013 or train entirely new models \u2013 depending on the inspection needs. Armed with these tools and the <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a>, customers can develop visual AI models that instantly detect the following concerns:\r\n<ul>\r\n \t<li>Tree overgrowth<\/li>\r\n \t<li>Rusty, damaged, or defective structures<\/li>\r\n \t<li>Overheating, smoke, flares, and fire<\/li>\r\n \t<li>Leaks in pipes<\/li>\r\n \t<li>Wastewater effluent and discharges<\/li>\r\n \t<li>Retention pond and drainage problems<\/li>\r\n \t<li>Low-hanging or broken power lines<\/li>\r\n \t<li>Virtually any other visually detectable inspection issue<\/li>\r\n<\/ul>\r\nAt the end of the day, the ROI benefit of computer vision for industrial inspections is clear. Whether it\u2019s a large-scale industrial operation, utility company, or governmental organization, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> empowers faster and more accurate detection of maintenance and environmental concerns \u2013 orders of magnitude more affordable than relying on human inspectors alone. Even better, Chooch AI can design and deploy a custom visual AI inspection strategy in only 6 to 9 days.\r\n\r\nGet a demo of AI for infrastructure inspection with <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">Industrial AI<\/a>.",
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"post_date": "2023-01-18 10:26:25",
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"post_content": "Computer vision engineers work in the domain of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">computer vision<\/a>: the subfield of computer science and artificial intelligence that seeks to make computers \u201csee\u201d images and videos at a high level, in the same way that humans can. More specifically, those with computer vision engineering skills can uses the AI tools to make it their job to solve real-world problems.\r\n\r\n<img class=\"aligncenter wp-image-2633 \" src=\"\/wp-content\/uploads\/2023\/07\/computer-vision-engineer-skill.jpg\" alt=\"Computer Vision Engineer Skills\" width=\"884\" height=\"464\" \/>\r\n\r\nThe fields of machine learning and <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">artificial intelligence<\/a>, along with subfields such as computer vision, have never been a hotter employment prospect. According to Indeed, computer vision engineers in the U.S. have one of the highest salaries in the technology industry, <a href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/03\/17\/machine-learning-engineer-is-the-best-job-in-the-u-s-according-to-indeed\/\" target=\"_blank\" rel=\"noopener noreferrer\">with an average base pay over $158,000<\/a>.\r\n\r\nBut what do <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> engineers do, exactly, and what skills to you need to be a computer vision engineer?\r\n<h2>What does a computer vision engineer do?<\/h2>\r\nThe job roles and responsibilities of computer vision engineers may include:\r\n<ul>\r\n \t<li>Designing and developing systems and software that use computer vision.<\/li>\r\n \t<li>Creating and\/or using computer vision libraries and frameworks.<\/li>\r\n \t<li>Sourcing and preparing computer vision training datasets.<\/li>\r\n \t<li>Experimenting with computer vision models by training and testing models and analyzing the results.<\/li>\r\n \t<li>Reading computer vision research papers to learn about new developments in the field.<\/li>\r\n<\/ul>\r\n<h2>The computer vision engineer skills you need to have<\/h2>\r\nWhat skills do computer vision engineers need in order to carry out these job roles and responsibilities?\r\n\r\nMost employers prefer <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> engineers to have education (i.e. a bachelor\u2019s, master\u2019s, or PhD) in a subject such as computer science, engineering, or mathematics. This education should likely have included coursework on topics such as computer vision, artificial intelligence, machine learning, deep learning, image processing, signal processing, data science, and software development. Mathematics courses on linear algebra, calculus, and probability and statistics are also highly useful for computer vision engineers.\r\n\r\nIn addition to this theoretical background, computer vision engineers also need practical skills to implement real-world solutions. Computer vision engineers should be able to train and optimize <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener noreferrer\">AI models<\/a> and deploy them in production scenarios. Familiarity with libraries and frameworks for computer vision, machine learning, deep learning, and data science\u2014e.g. OpenCV, sklearn, PyTorch, and TensorFlow\u2014is highly valuable.\r\n\r\nThe Python programming language currently dominates the field, with <a href=\"https:\/\/towardsdatascience.com\/what-is-the-best-programming-language-for-machine-learning-a745c156d6b7\" target=\"_blank\" rel=\"noopener noreferrer\">57 percent of machine learning developers and data scientists<\/a> using Python. In addition, knowing other languages is also helpful: OpenCV is primarily written in C++ (although it has interfaces for Python and Java), and MATLAB is very popular for image processing.\r\n<h2>The future of computer vision engineer jobs<\/h2>\r\nWant to become a <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> engineer? Many computer vision engineers take the traditional route to their choice of career: getting a degree in a STEM subject such as computer science or mathematics, often doing relevant internships or performing relevant research along the way.\r\n\r\nEven without a formal education in computer vision and computer science, however, becoming a computer vision engineer isn\u2019t out of reach. Many companies looking to hire computer vision engineers are open to non-traditional candidates who can replace education with experience (e.g. by showing previous work on computer vision projects or open-source software).\r\n\r\nChooch is a great way to get started in the field of\u00a0computer vision. We offer a robust <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision platform<\/a> that can automatically train fast, highly accurate <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a>. The possible applications include everything from facial authentication to diagnosing illnesses and detecting manufacturing anomalies.\r\n\r\nWant to learn more about becoming a computer vision engineer? <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\" target=\"_blank\" rel=\"noopener noreferrer\">Sign up today<\/a> and create your free account on the Chooch platform.",
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"post_date": "2023-01-18 10:16:26",
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"post_content": "The massive growth of IoT devices and its new applications is driving <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge ai<\/a> with an explosive increase in revenues expected to go from 2.8 billion U.S. dollars in 2019 to 9 billion by 2024. The rise of <a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\">edge computing<\/a> has been significantly transforming how organizations are collecting their data, processing it, and gaining insights for more data-driven business decisions. But, what is an edge device?\r\n<h3>What are edge devices?<\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\">Edge computing<\/a> is a distributed topology where data storage and processing are done close to the edge devices where it's being collected, rather than relying on a central location that can be thousands of miles away.\r\n\r\nEdge devices are hardware components that control data flow at the boundary between two networks where they serve as network entry (or exit) points.\r\n\r\nEnterprises and service providers use edge devices for transmitting, routing, processing, monitoring, filtering, translating, and storing data passing between networks.\r\n\r\nExamples of <a href=\"https:\/\/www.chooch.com\/blog\/leak-detection-and-remote-site-monitoring-with-ai-models-on-edge-devices\/\">edge devices<\/a> include cameras, sensors, routers, integrated access devices, multiplexers, and a variety of metropolitan area network and wide area network access devices.\r\n<h3>What are benefits of running inferencing on edge devices?<\/h3>\r\n<strong>Speed and Latency<\/strong>\r\n\r\nAnalyzing data, especially real-time data, in edge devices, eliminates latency issues that can affect performance. The less time it takes to analyze data, the more value that comes from it. For example, when it comes to autonomous vehicles, time is of the essence, and most of the data it gathers and processes is useless after a couple of seconds.\r\n\r\n<strong>Enhanced Security<\/strong>\r\n\r\nThe distributed architecture that comes with edge computing enables organizations to distribute security risks as well, which diminishes the impact of attacks on the organization as a whole. Edge computing enables organizations to overcome local compliance and privacy regulations' issues, as well.\r\n\r\n<strong>Cost Savings<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/gartner-hype-cycle-edge-computing-2023\/\">Edge computing<\/a> helps companies to reduce costs associated with transporting, managing, and securing data. By keeping data within your edge locations, you optimize bandwidth usage to connect all of your locations.\r\n\r\n<strong>Reliability<\/strong>\r\n\r\nBusiness operations continuity may require local processing of data to avoid possible network outages. Storing and processing data in <a href=\"https:\/\/www.chooch.com\/blog\/leak-detection-and-remote-site-monitoring-with-ai-models-on-edge-devices\/\">edge devices<\/a> improves reliability, and temporary disruptions in network connectivity won't impact the devices' operations.\r\n<h3>How do edge devices work?<\/h3>\r\nEdge devices have a very simple working principle; it serves as network entry or exit and connects two different networks by translating one protocol into another. Moreover, it creates a secure connection with the cloud.\r\n\r\nAn edge device is a plug-and-play; its setup is quick and straightforward. It is configured via local access and also has a port to connect it to the internet and the cloud.\r\n<h3>How do edge devices offer a better AI experience?<\/h3>\r\nMany Artificial intelligence use-cases are better done on edge devices, offering maximum availability, data security, reduced latency, and optimized costs.\r\n\r\nRunning <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Machine learning models<\/a> can be computationally expensive in cloud-based environments. Meanwhile, inference needs relatively low computing resources. When <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> are trained on the cloud, data needs to be transferred from end-devices to predict outputs. This needs a stable connection, and since the volume of data is large, the transfer can be slow or, in some cases, impossible.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> moves algorithms closer to the data source where they're processed locally without requiring any connection offering real-time analytics in less than few milliseconds\r\n\r\nThe volumes of data are significantly increasing, so is the need to process it autonomously. Enabling Deep Learning algorithms to perform EdgeTraining locally is a must-have feature for many applications such as autonomous vehicles.\r\n\r\nIoT edge devices are now able to run machine learning models locally within the device using TensorFlow, Pytorch, or other <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> tools. The thing that enables capabilities to be handled directly on a device. Localizing the data reduces the latency that results from sending the data to the cloud, and it enables more immediate insights generated by devices.\r\n\r\nMassive changes are initiated with Edge AI raising demand for IoT smart devices, and the emergence of more advanced technologies. As organizations are increasingly adopting <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> to make their operations better and enable real-time performance, the market will significantly grow to keep pace with the computing requirements of these smart items.\r\n<h3>How does Chooch use Edge AI to help businesses?<\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Chooch Edge AI<\/a> helps organizations to take their video analytics and IoT applications to the next level. With over 90% of accuracy delivered in less than 0.2 seconds, Chooch provides massive results for many solutions in Artificial Intelligence of Things, <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">manufacturing<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">fire and smoke detection<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>, <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a>, and more.\r\n\r\nChooch AI creates complete solutions from AI training in the cloud through to deployment. Edge deployments are managed from the cloud, with models that include object recognition, facial authentication, action logging, complex counting, and more.\r\n\r\nCurrently, Chooch's Edge AI Vision is able to deploy up to 8 models and 8,000 classes for robust Visual AI on a single edge device. Chooch's AI inference engines are very fast, generating responses under 0.5 seconds, and processing ten simultaneous calls per second.\r\n\r\n[caption id=\"attachment_1949\" align=\"aligncenter\" width=\"488\"]<a href=\"\/see-how-it-works\/\"><img class=\"wp-image-1949\" src=\"\/wp-content\/uploads\/2023\/07\/edge-ai.png\" alt=\"Edge AI\" width=\"488\" height=\"307\" \/><\/a> <center><b>Need Computer Vision?<br \/>Want to learn more? <a href=\"\/see-how-it-works\/\">See how it works.<\/a><\/b><\/center>[\/caption]",
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"post_date": "2023-01-18 10:14:16",
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"post_content": "Imagine a manufacturing facility with a team of quality control inspectors that never get tired, never get distracted, and always perform their jobs with laser-point accuracy. Even better, these defect detection inspectors provide their services for a fraction of the usual cost.\r\n\r\nUntil recently, this idea was nothing more than a pipedream. But today, global industrial manufacturers and consumer packaged goods (CPG) companies are deploying AI-based computer vision technologies to detect manufacturing defects. These AI systems are detecting flaws with levels of accuracy that far exceed the capacity of human inspectors \u2013 and they\u2019re also a lot less expensive than human workers.\r\n\r\nIn this article, we\u2019ll look at why computer vision <a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">defect detection systems<\/a> are needed in manufacturing facilities and how visual AI defect detection works. We\u2019ll also discover some exciting real-world use cases for this technology in manufacturing and CPG facilities.\r\n\r\n<img class=\"wp-image-2368 alignleft\" src=\"\/wp-content\/uploads\/2023\/06\/defect-detection.png\" alt=\"Defect Detection\" width=\"655\" height=\"335\" \/>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2><\/h2>\r\n<h2>The Need for Visual AI Defect Detection in Manufacturing<\/h2>\r\nManufacturing and CPG defects can be costly. According to <a href=\"https:\/\/www.marsh.com\/ie\/services\/risk-analytics\/insights\/quantifying-full-costs-of-product-defect.html\">Marsh.com<\/a>, product defects can trigger enormous costs and expenditures related to:\r\n<ul>\r\n \t<li>Notifying retailers and customers about defective products<\/li>\r\n \t<li>Identifying and tracking down defective products<\/li>\r\n \t<li>Transporting and repackaging defective products<\/li>\r\n \t<li>Destroying and disposing of defective products<\/li>\r\n \t<li>Replacing defective products with better-built items free of defects<\/li>\r\n \t<li>Adverse publicity that damages the manufacturer\u2019s reputation<\/li>\r\n \t<li>Loss of revenue that results from adverse publicity<\/li>\r\n \t<li>The cost of marketing and public relations efforts to rehabilitate sales and rebuild customer trust<\/li>\r\n<\/ul>\r\nOf these expenditures, <a href=\"https:\/\/www.agcs.allianz.com\/content\/dam\/onemarketing\/agcs\/agcs\/reports\/AGCS-Product-Recall-Report.pdf\">Allianz claims<\/a> that \u201cthe biggest single cost of a product recall event is the loss of sales and business interruption, both from the recall itself and the reputational damage.\u201d There is also the potential for legal costs related to <a href=\"https:\/\/www.law.cornell.edu\/wex\/manufacturing_defect\">defective product liability lawsuits<\/a>. According to Allianz, \u201cdefective product incidents have caused insured losses in excess of $2 billion over the past five years, making them the largest generator of liability losses.\u201d\r\n\r\nConsidering these costs, the ROI benefits of catching defects before they leave the manufacturing facility is obvious. However, human defect inspectors can only do so much to identify manufacturing errors at industrial factories and CPG facilities. While most manufacturing facilities have a longstanding history of relying on human defect inspectors, humans employed in visual inspection tasks are prone to getting tired, distracted, and making serious and costly errors.\r\n\r\nThis is where a visual AI quality control system can help. <a href=\"https:\/\/www.chooch.com\/blog\/visual-ai-railway-inspections-better-detection-of-railroad-defects-and-obstacles\/\">Visual AI systems for defect detection<\/a> are not only more affordable than human defect detection staff, but they are also more accurate when it comes to finding and reporting defects.\r\n<h2>How Visual AI Defect Detection Works<\/h2>\r\nModern Visual AI technologies rely on powerful cloud-based servers that allow them to rapidly ingest visual information for machine-learning training purposes. By training a computer vision system with hundreds of thousands or millions of images of specific types of product defects, these systems can learn to rapidly identify similar defects with a high degree of accuracy. Visual AI defect detection systems can identify flaws like bottles missing bottlecaps, cracks in pipelines, poorly painted surfaces, missing parts, broken items, misshaped items, cracked glass, cracked metal casings, and virtually any other type of errors that human visual inspectors identify.\r\n\r\nAfter setting up a high-quality camera system along the assembly line of a manufacturing facility \u2013 and connecting the cameras to the visual AI system \u2013 facilities can detect, flag, remove, and replace defective products more efficiently and successfully, thereby circumventing the massive costs and workflow inefficiencies that these errors cause. Best of all, this is achieved vastly more affordably than relying on human laborers.\r\n\r\nThe most advanced visual AI inspection technologies \u2013 such as <a href=\"https:\/\/www.chooch.com\/\">Chooch.com<\/a>\u00a0\u2013 can spawn lightweight \u201cEdge AI\u201d systems that run in the cloud. These systems can immediately integrate with an existing IoT infrastructure of cameras. They can also automatically deploy to the Edge through cloud-based dashboards and APIs. Finally, Edge AI technology makes the integration and replication of a trained visual <a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">AI defect detection system<\/a> easier and more efficient across an entire enterprise.\r\n<h2>Use Case Examples for Visual AI Defect Detection in Manufacturing<\/h2>\r\nWe have identified an endless range of visual AI defect detection use cases. These use cases apply to industrial manufacturing plants, CPG facilities, quality controls for infrastructure, airline industry safety, and more. Ultimately, whenever a manufacturing facility employs human laborers to visually inspect for defects, an AI-based computer vision system can likely perform the same task with greater efficiency and fewer errors.\r\n\r\nHere are several use case examples for a <a href=\"https:\/\/www.chooch.com\/blog\/visual-ai-railway-inspections-better-detection-of-railroad-defects-and-obstacles\/\">visual AI defect detection<\/a> system:\r\n\r\n<strong>Industrial manufacturing plants: <\/strong>Industrial manufacturing facilities must meet specific levels of quality \u2013 not only because they must provide defect-free products to their customers, but also due to industry standards and government regulations. With a <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Chooch computer vision system<\/a>, organizations can train a visual AI system to detect the most important defects that interfere with product quality at an industrial manufacturing plant. These could relate to cracks in casings, broken products, missing parts, unsightly scratches, dust on painted items, structural integrity problems, poorly painted items, and more.\r\n\r\n<strong>CPG production facilities: <\/strong>Consumer packaged goods manufacturers must meet some of the highest safety and quality standards that exist. Visual inspectors need to identify discolored potato chips, unsightly or rotten food products, packaging defects, and other defects. An appropriately trained Chooch.AI system can identify these kinds of defects \u2013\u00a0 including blackened potato chips, misshapen food products, uncapped soft drinks, leaking products, broken glass, and poorly packaged items.\r\n\r\n<strong>Quality control for infrastructure: <\/strong>When it comes to the large machinery used in manufacturing \u2013 and essential infrastructure items required for mining, oil drilling, and other large-scale operations \u2013 visual AI systems can ensure that vital pieces of an operational infrastructure are free of problems and defects. For example, an IoT network of visual AI cameras can monitor oil pipelines and machinery for signs of stress and wear. These systems can also monitor an oil rig for safety-related problems, detecting them before a costly shutdown or dangerous accident occurs.\r\n\r\n<strong>Airline industry safety: <\/strong><a href=\"https:\/\/www.boeing.com\/commercial\/aeromagazine\/aero_19\/717_story.html\">Boeing reports<\/a> that the airline industry spends approximately $40 billion per year on inspections and maintenance related to the safety and proper functioning of jets, engines, and other airline equipment. These expenditures relate to \u201cthe costs of the labor and materials required to perform servicing, repair, modification, restoration, inspection, test, and troubleshooting tasks during on-airplane and shop maintenance activities.\u201d Visual AI <a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">defect detection systems<\/a> \u2013 including drone-based visual AI systems \u2013 facilitate visual inspections related to airline equipment maintenance and safety. These systems offer a more cost-effective, accurate, and higher-quality means of detecting problems before they become unnecessarily dangerous or expensive.\r\n\r\nHere is a table that shows additional use case applications of visual AI defect detection in manufacturing:\r\n<table class=\"postTable\" width=\"100%\">\r\n<tbody>\r\n<tr>\r\n<td colspan=\"3\">\r\n<p style=\"text-align: center;\"><strong>Use Cases for Defect Detection with Computer Vision<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><\/td>\r\n<td><strong>Products<\/strong><\/td>\r\n<td><strong>Potential Defects<\/strong><\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Nonferrous Metals<\/strong><\/td>\r\n<td>Wires, cables, aluminum, stainless steel<\/td>\r\n<td>Scratches, cracks, dirt, dents<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Building Materials<\/strong><\/td>\r\n<td>Wood boards, sashes, metal fittings, tiles, other materials<\/td>\r\n<td>Scratches, cracks, surface defects, dents<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Electronic Parts<\/strong><\/td>\r\n<td>Electronic materials, electronic components, circuit boards, electrical panels, other items<\/td>\r\n<td>Scratches, chips, cracks<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Auto Parts<\/strong><\/td>\r\n<td>Material parts, resin parts, fabrics, other materials<\/td>\r\n<td>Scratches, dents, dirt, cracks<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Raw Materials<\/strong><\/td>\r\n<td>Chemical fibers, rubber, glass, paper, pulp products<\/td>\r\n<td>Scratches, cracks, dirt, dents<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Food<\/strong><\/td>\r\n<td>Processed foods, beverages, food packaging, bottling<\/td>\r\n<td>Foreign objects, labeling errors, leaks, packaging damage, missing bottlecaps<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Medical<\/strong><\/td>\r\n<td>Pharmaceutical medicines, medical devices, surgical equipment, wound dressings, syringes, other items<\/td>\r\n<td>Foreign objects, labeling errors, cracks, defects, dirt, impurities, sanitary issues<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<h2>Build Your Visual AI Defect Detection System with Chooch.AI<\/h2>\r\nAt <a href=\"https:\/\/www.chooch.com\/\">Chooch.com<\/a>, we design visual AI systems for virtually any industry and any application. Whether the visual inspection use case relates to defect detection, medical lab analysis, safety equipment monitoring, facial recognition for security systems, product inventory control, or another visual job, Chooch.com computer vision technology can complete the task faster, with greater accuracy, and a lot more cost-effectively than human labor.\r\n\r\nWant to learn more about Chooch.com and how a visual AI system can satisfy your unique use cases? <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\">Sign up for a free account on the AI platform.<\/a>",
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"post_content": "The benefits of <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">detecting falls<\/a> using action detection, computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI<\/a> outweigh the costs. By reducing the risk of falls and the costs that come with delayed fall response, AI models provide our partners and customers with real value.\r\n\r\nFalls can cause serious injuries when they are not detected early. AI models for Action detection help in detecting falls which increases safety for everyone.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/psX3mN65yJQ\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nThese <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">AI models increase safety<\/a> by capturing images of falls and sending alerts with location data to relevant authorities for emergency help.\r\n\r\nThey can improve safety in the following scenarios:\r\n<ul>\r\n \t<li>In eldercare facilities where falls are the leading cause of fatal injury. By detecting falls early, Seniors can get the help they need as soon as possible.<\/li>\r\n \t<li>In industrial settings where employees work above ground level, carry heavy objects, or operate heavy machinery. This ensures that employees receive medical attention as soon as again.<\/li>\r\n \t<li>In cities so that residents who have had falls can get immediate emergency help.<\/li>\r\n<\/ul>\r\nAI Models from <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> are pre-trained and ready to be deployed for <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">fall detection<\/a>. Pre-training ensures that these models can be deployed very fast, often within days and not weeks.\r\n\r\nAfter a model has been deployed successfully, it is then trained remotely. Moreover, custom models can be deployed according to partner specifications.",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">Computer vision<\/a> and artificial intelligence need a lot of data. The more volume and variety of data that you can show to your model during AI training, the more high-performance and robust the model will be when examining data in the real world that it hasn\u2019t seen before. There\u2019s just one issue: what if you only have a limited amount of data in the first place? That's where data augmentation comes in.\r\n\r\nFor example, suppose you want to train a computer vision model to recognize different cat breeds. To achieve the best results, your model should train on a balanced dataset that has roughly the same number of images for each breed. It should be easy enough to find thousands of images of the most popular breeds, such as Persians and Siamese cats, but what about extremely rare breeds such as the\u00a0<a href=\"https:\/\/www.vetstreet.com\/cats\/laperm\" target=\"_blank\" rel=\"noopener noreferrer\">LaPerm<\/a>\u00a0or the\u00a0<a href=\"https:\/\/www.yourcat.co.uk\/types-of-cats\/sokoke-cat-breed-information\/\" target=\"_blank\" rel=\"noopener noreferrer\">Sokoke<\/a>?\r\n\r\nWithout correcting this imbalance, your model can achieve a very high accuracy on the training data simply by learning to recognize the most popular (and therefore overrepresented) breeds. However, this strategy won\u2019t do as well in the real world\u2014for example, a breed recognition quiz that treats all breeds equally, with a single question about each one.\r\n\r\nDon\u2019t have enough data to train your\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener noreferrer\">AI model<\/a>? No problem. Below, we\u2019ll discuss a powerful strategy for <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> to accomplish the (seemingly) impossible: augmenting 2D images.\r\n<h2>Data Augmentation for 2D Images<\/h2>\r\n<img class=\"size-full wp-image-2538 aligncenter\" src=\"\/wp-content\/uploads\/2023\/07\/computer-vision-with-data-augmentation.png\" alt=\"Computer Vision with Data Augmentation\" width=\"600\" height=\"338\" \/>\r\n\r\nData augmentation is a useful technique for expanding the size of your dataset without having to find or generate new images. How is this possible? Suppose you have a single image of a cat that you want to augment within your dataset. The transformations that you can make to this image without changing the correct answer (e.g., \u201cPersian\u201d or \u201cAmerican Shorthair\u201d) include:\r\n<ul>\r\n \t<li>Flipping the image (horizontally or vertically)<\/li>\r\n \t<li>Rotating the image<\/li>\r\n \t<li>Scaling the image (e.g., zooming in or out)<\/li>\r\n \t<li>Cropping the image<\/li>\r\n \t<li>Placing the foreground object onto a new background<\/li>\r\n \t<li>Altering the hue of the image<\/li>\r\n<\/ul>\r\nWhat are the benefits of augmenting 2D <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images for computer vision<\/a>? By slightly modifying the original image, you can create perhaps dozens of augmented images. This makes it harder for the AI model to overfit (i.e., learning to recognize the data itself instead of learning the underlying patterns and concepts). For example, a robust <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI model<\/a> for cat breeds should still be able to identify the correct breeds even when the original images are rotated to be upside down.\r\n<h2>How to Augment 2D Images with Chooch<\/h2>\r\nThere\u2019s just one question left: how can you perform 2D data augmentation for <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>? Without technical experts on hand, trying to build your own scripts and workflows might suck up valuable time that\u00a0you could instead spend fine-tuning the model and getting better results.\r\n\r\nFortunately, there\u2019s an answer to this question: powerful, user-friendly computer vision platforms like Chooch. With Chooch, you can augment your existing images in just a few clicks, performing various transformations to make your dataset more robust.\r\n\r\nWorking in Chooch's user-friendly dashboard, you can upload an annotated image of your choice, and then select the pre-built transformations and augmentations you want to perform. You can then deploy this model wherever you need it, including on\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge devices<\/a>.\r\n\r\nIn order to generate the most useful augmented images, data augmentation in Chooch can only be performed on source <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images<\/a> with bounding box annotations. The transformations available within the Chooch dashboard for data augmentation include:\r\n<ul>\r\n \t<li>Shifting, scaling, and rotating<\/li>\r\n \t<li>Horizontally flipping<\/li>\r\n \t<li>Cutting out the background<\/li>\r\n \t<li>Adding noise and blurring<\/li>\r\n \t<li>Changing the brightness and contrast<\/li>\r\n<\/ul>\r\nYou can choose the default values for these transformations, or tweak and fine-tune the intensity of each augmentation yourself. You can also adjust the number of augmented images to be generated from each source image. Once you've adjusted the settings to your liking, Chooch will create the augmented dataset with just a click in a matter of seconds.\r\n\r\nWant to learn more about how Chooch can help augment your datasets and improve your AI models\u2019 performance, please visit the <a href=\"https:\/\/www.chooch.com\/blog\/training-computer-vision-ai-models-with-synthetic-data\/\">Synthetic Data<\/a> page or\u00a0<a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">request computer vision consulting<\/a>.",
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"post_content": "As the name suggests, an AI computer is any computing machine that can do\u00a0work in the field of\u00a0artificial intelligence. Thanks to the rapid pace of technological developments, even modest consumer hardware can today be considered an \u201cAI computer,\u201d capable of running cutting-edge\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener noreferrer\">ai models<\/a>.\r\n\r\nThe past few decades have seen tremendous strides in <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI computing technology<\/a>. In the 1980s and 1990s, for example, when computing power came at a premium, machines had to be specially built and configured to do AI work. Now, the average laptop has three of the most crucial components for any AI computer:\r\n<ul>\r\n \t<li><strong>Graphical processing unit (GPU):<\/strong>\u00a0Originally\u00a0created for real-time computer graphics, the GPU excels at any task that requires massively parallel processing, including many types of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> (such as deep learning).<\/li>\r\n \t<li><strong>Central processing unit (CPU):<\/strong>\u00a0Work that can\u2019t be offloaded to the GPU on an AI computer is instead run on the CPU, which you can think of as the machine\u2019s \u201cbrain.\u201d But technological progress has made CPUs more and more powerful with a higher number of cores capable of handling many tasks that were previously GPU-exclusive.<\/li>\r\n \t<li><strong>Software:\u00a0<\/strong>The past decade has seen an explosion in the availability of <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">AI and machine learning<\/a> frameworks and software. Even relative beginners to programming can use these tools to spin up powerful AI models in just a few lines of code.<\/li>\r\n<\/ul>\r\nThe choice of operating system is also an important factor when building an AI computer:\r\n<ul>\r\n \t<li>UNIX-based operating systems such as Linux and macOS are significantly more convenient for programmers, and many AI frameworks have been optimized for Linux versions such as Ubuntu and Red Hat.<\/li>\r\n \t<li>Windows computers can present compatibility issues with some AI tools, but the operating system itself is so widespread that it should always be taken into account.<\/li>\r\n \t<li>macOS is convenient in terms of software, but Macintosh hardware still lags behind\u00a0Linux and Windows when it comes to sheer power (although Apple has been trying to catch up by introducing new chips).<\/li>\r\n<\/ul>\r\n<h2>How does AI computing software work?<\/h2>\r\nThe dominant form of AI these days is deep learning, which uses an <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI model<\/a> known as the neural network. <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Machine learning<\/a> engineers use deep learning software frameworks such as PyTorch, Keras, and TensorFlow to\u00a0build AI models\u00a0with just a few keystrokes, and then train them on the GPU.\r\n\r\nComposed of many interconnected nodes called \u201cneurons\u201d organized in multiple layers, neural networks are a rough simulation of the structure of the human brain. Each connection between two neurons has a corresponding weight, whose value determines the importance given that connection (larger values represent stronger connections). Neural networks are trained using an algorithm called backpropagation, that automatically adjusts the weights of the neural network when the model makes an incorrect prediction.\r\n\r\nThe convolutional neural network (CNN) is a special form of neural network optimized for analyzing visual imagery, such as photographs and videos.\u00a0<a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener noreferrer\">AI platforms<\/a>\u00a0such as Chooch can process and interpret any kind of visual input, from X-rays and sonograms to video cameras and infrared satellite images.\r\n\r\nWant to turn your enterprise IT systems into a powerful AI computer? We can help. Chooch is a robust, feature-rich, easy-to-use <a href=\"https:\/\/www.chooch.com\/platform\/\">platform for visual AI and computer vision<\/a>.\u00a0<a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener noreferrer\">Get in touch with our team of AI experts today<\/a>\u00a0for a chat about your business needs and objectives, or to start your free trial of the Chooch platform.\r\n\r\n<strong>Why is this AI Computer thing happening now?<\/strong>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Artificial intelligence (AI)<\/a> and machine learning have never been so widespread or so accessible to the masses - that's why AI computers have become a thing.\u00a0To incorporate artificial intelligence into your own workflows, of course, you need an AI computer.\r\n\r\nIn a 2020 report, the consulting firm McKinsey & Company found that half of organizations\u00a0<a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/global-survey-the-state-of-ai-in-2020\" target=\"_blank\" rel=\"noopener noreferrer\">\u201chave adopted AI in at least one function.\u201d<\/a>\u00a0Also, businesses who derive the most value from AI report that they\u2019ve experienced benefits such as better performance, higher growth rates, and stronger leadership.",
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"post_date": "2023-01-18 10:05:25",
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"post_content": "To build accurate computer vision models, you need data\u2014and lots of it. Now, you can generate images with <a href=\"\/see-how-it-works\/\">synthetic data<\/a> and augmented data on the <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a>, and then use these synthetic images to train and deploy computer vision models. What you'll learn in this webinar is how to use different technologies with the same goal: deploying accurate computer vision even faster. Watch the video or read the transcript below.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/2zPmQXqK1Kk\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe>\r\n\r\nEmrah Gultekin: So thank you all for joining in today. And what we're going to do today, we're going to run through a lot of material. And so if you have questions, you can ask them during or after the webinar. But basically, what we're talking about today is <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a>. And it's a part of generating data so that you can train the AI. And so at the end of the day, what we're talking about really is some of the inferencing that goes on, and the problem that you're trying to solve. So let's say you're tasked to detect something and it's not in a pre-trained model, there are no pre-trained classifications for it or if they are, they're not really good. So at the end of the day, what you're trying to do here is, you're trying to generate better inferences. So this is where it all happens.\r\n\r\nAnd the inference or the prediction ... You're sending in video feed, or images and what's happening is you're getting responses for that. So it's detecting simple things like cars and people, faces and so forth, very complex things like parts or scratches or types of cells, and so forth. So you can do a lot of stuff. So it really ends up here in inferencing. Which is important for us and important for the clients as well and also a part of all the ecosystem partners out there. But what's happening is, if you go back to the cycle here, it really begins with data. So today, we're going to be talking about these things and that is data generation to train a model. So it goes into here. And we're not going to be talking about model training today but all of you who are on this call know something about this.\r\n\r\nBut what you do is you create data, and then you train the model, then you do the inferencing. And the inferencing helps you create data again. So this is like a cycle here that goes back and forth. So today we're going to be talking about data generation through a series of tools. This is not just synthetic data, you've got manual annotation, you've got smart annotation and data augmentation, so forth. So today we're going to be talking clearly about this. The result is inferencing all the time, so increasing the accuracy, increasing the stability of the model and creating those dynamic models that we all dream of. So the question becomes, where do you get the data?\r\n\r\nSo the data, you have public data sets, you've got client data sets, you can do web scraping and so forth. But at the end of the day, the issue has always been ... And this is particular in visual detection, and that's what we're talking about today, the visual AI. In particular, what you're seeing is that the models that you train, you need to have a lot of data. And this is like in the thousands of images per class. So where do you really get that data? There are ways to do it. You can scrape, you can get client data, you can get public datasets and so forth. But it's usually not annotated, it's unstructured. And it's not enough. So that's the question here is, where do you get it? And one way to do is to synthesize the data. And that's what we're going to talk about today is getting some base data, some real data and then synthesizing that to create diverse data sets in order to generate the data set necessary to train the model or train multiple models at the same time.\r\n\r\nSo this is what this is about. And on our <a href=\"https:\/\/www.chooch.com\/platform\/\">platform<\/a>, you can do this but you can also import already generated data sets from somewhere else and generate the model. So you don't really have to do it on our system, you can do it on a partner system, on another ecosystem partner who actually does this type of data set creations as well or if there are people who do annotation and so forth. So you can actually upload data sets that are already created somewhere else and then generate the model with one click basically.\r\n\r\nSo the problem here is data sets require lots of labeled, high quality, and usually copyright free images. So that's a lot of stuff and it's very difficult to overcome that. And so in order to do this, your goal is to generate a computer vision model that can work in real environments. And this means you really need a lot of images in sufficient variation. This could be coming through video frames or it could be coming through images itself and whatnot, but you really need a substantial amount of them. And the data that you generate has to have a minimum of labeling errors. So it's not enough to just have raw data, maybe having files that are labeled but you also need to annotate them, especially if you're doing object detection. And this is a very slow process to do this manually and intrusive lots of errors and takes a long time. It's not scalable, basically.\r\n\r\nSo manual annotation, it's necessary. You have to do that because that's how you see the AI and you see the model, see the data set but it's not scalable for a number of reasons. Humans are not scalable. So the conclusion is you generate synthetic images of the annotated objects to train a model and detect real world situation. So this is really what we're doing here. So you need lots, labeled, it needs to be high quality. So the workflow and methodology we have here at <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Chooch AI<\/a> is quite simple actually. You sculpt the problem with the client or with the partner. Basically what the case is, what is the client trying to detect? What are they trying to detect? And usually, the answer to this is not that simple.\r\n\r\nAnd sometimes you have to walk through with the client to understand what they're actually trying to achieve with visual detection. You sculpt the case and basically on the technical side, you start to check the performance of the pre-trained models or existing models. So you want to be able to use a pre-trained models as much as possible to deploy a model or inference engine to the client. But you also have to understand that usually, these pre-trained models are not sufficient for production purposes unless they're done for a specific purpose. Then what happens is you generate data. And basically what you're doing is you're reading this data to augment the data that already exists there or generate something from scratch.\r\n\r\nThe best way to understand if your data set is good it to train a model and then test it. And then when you deploy it, you get feedback from the users and then that goes into the data set again. So it's the cycle where you're generating models which are dynamic. So if you're just generating a model that's static and you say this works out of the box for everything, that's usually not the case. You need user feedback, you need different types of feedback to enhance the data set as you move forward. So this is a more extensive workflow here. But basically what it is, understand the available data in the detection requirements from the client. You generate the data for data gaps, that's the second step of that, train and test the models.\r\n\r\nSo you're trying to increase the accuracy of the model. And that is the F1 score that we talk about which is a harmonized mean between precision and recall. You want to increase that. Then you deploy the models, receive feedback from users on correct, incorrect or unknown objects or maybe there's something new that needs to be trained, you want to put a new class in. So you can receive that feedback directly from users annotated or not annotated. And put that feedback back into the data set and retrain the model. So you have that cycle there where the workflow is very crucial to the scalability of this entire system. So these are some of the tools that we use on our system, you have obviously manual data set labeling, you got smart annotation, 2D synthetic data, 3D synthetic data, data augmentation. So today, we're going to be talking about these which are really related to data set generation, you go into model training and dense training, cloud inferencing, F1 score calculation, unknown object detection, user feedback and edge inferencing, device stream management.\r\n\r\nSo you have this gamut that you need to be able to do to deploy at scale with clients. But today we're going to be talking about these with which is <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a>, and some of the annotation tools that we have. We have manual annotation, obviously very, very important. And then you have video annotation, which is annotating a video and then having the tracking system track that object throughout the video and generate a data set based on that, which generates hundreds of thousands of images through a video, basically. And then you have smart annotation with AI, which is using the inference engine, again, to annotate new data. So this is part of that cycle where you need the inference engine to do the annotation work. And so our core is the inference engine as a company. So we've focused totally on that. And it's important to understand that really closes the loop on the dynamic model creation cycle.\r\n\r\nData augmentation is also very important here. And then you have synthetic 2D data and then synthetic 3D data. So we're going to go through these today. So manual annotation is very straightforward. It's pretty much people drawing bounding boxes or polygons around objects. It's a painstaking process and it requires training, and it's not very skilled. But actually it's a very important part of this because if you don't have the manual annotation, you can't really teach the AI new things and increase their accuracy of the inference engine. So even if you have a machine annotating, it is really based on the human who annotated initially that data set to train the model to do the inferencing for the annotation. So it's a very important part of this process.\r\n\r\nAnd this is a basic thing from our dashboard. You have the ... Basically just growing bounding boxes around it and you can label it whatever you want. And you continue doing this through the entire data set. And then you have these different classes over here, which will show up as you annotate. And this is part of that. So you see the entire gamut of images that are produced in that data set. And you can see a video over here as well which is actually part of this. And then you can see here that you have <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a>, you have smart annotation, and you have augmentation as well. And you can add images to this data set and whatnot. The video annotation is also very important because it allows you to scale the amount of frames that are being put into the data set.\r\n\r\nAnd this is an important tool, especially if you're doing something very specific for a specific task, it actually tracks that entire object, unknown object it tracks unknown object, unknown action through the life cycle, through that video, and then generates images into that data set. So this is a conveyor, uploading this ... It's just an MP4 video. And basically, what you do is you click on it, and you start annotating. And you can choose which area of the video you want to annotate. You've got an orange part here, and then you've got a blue part, and basically you click on annotate process and it's processing it. And it'll just follow these through the video and generate a data set for it. So you do once and it's already generated like 80 images each of that. So this is a very important tool especially if you're doing something very specific in a specific field or you have lots of video and you want to annotate it. And then you generate this data set for you.\r\n\r\nAnother powerful tool or smart annotation. And this is something that we launched a few months ago. And basically what it is, is you're using the inference engine to annotate already known objects. And these give a pre trained by our system or can be pre-trained by the user, by the enterprise who's already using the system. And you can use your custom object models, or you can use basically pre-trained Chooch object models as well. And it automatically annotates entire image data without manual labeling. So it'll annotate everything and then you can review the annotation done by the machine. This is a very important tool, again, the inference engine. Inference engine has to be very strong to be able to do these types of things. So we got some spark plugs here.\r\n\r\nYou click on the smart annotation, and you press my object models, press a spark plug and you press start and these are unannotated, these images, and what basically happens is when you press start on this, it automatically annotates everything in the data set with the spark plug. And then you can use that to train the model. So you're actually ... What you're doing is you're constantly building a data set, automating the building of the data set through these tools in order to train the model as you move forward. Data augmentation as well is very, very important. And this is something that's done on the back end of some of the already existing deep learning frameworks. But what you really want to do is be able to tune it, if you're a power user. And so in some instances, your data ... Even if you have a lot of data, it's still not enough to train the model because it doesn't have the right views or there's not enough noise and so forth.\r\n\r\nSo you want to be able to do data augmentation here and to do it on the system as well. It helps generalize the existing data set basically. So we have a part data set here, it's part of the spark plug. And then what you're basically doing is upload ... Let's say you have these five images of these different things, and they're annotated and you annotate them basically. And then you have these images here with the different parts, press augmentation and you can ... This is over here and press the augmentation thing and it pops this up. And you can go up to 100 iterations, we don't recommend that, we recommend three or four X the amount, it just over fits but you can play around with it basically. So it's rotation, horizontal flip, cut out, shifting, scaling, basic things that you do and there are default settings here but if you want to play around with it, you can do that. That's fine.\r\n\r\nAnd scaling, rotation, noise, blur, brightness, contrast, and you start the augmentation on the entire data set basically, randomize it ... Based on randomization principle and then it generates all this. So you've got 1000 of each here, which is a lot. And then you can use that to train the data set basically. You can see here that this is quite different from the original one, that's flipping around, changing the noise, changing the lighting, and coloring and all that. Another tool, which is very, very important is the 2D synthetic data. It's almost like a combination of augmentation and synthetic data. It's a lot of augmentation, actually. But what you're basically doing is you're creating bounding box free transformations, and it's auto background removal. So when you annotate something, it segments that out and places in different environments basically, and that's what this is about.\r\n\r\nRotates it, flips it around, does a lot of different distancing, and then creates that data set for it. And it's all randomized and that's basically part of this process. So we go back to a part data set here. And this is the same part. So I have a quick release part here. And I want to create a 2D of this. And I'm creating another thousand maxim object count per image. And you can choose the different themes that you have. You can use your themes, or you can use our themes. And this is basically just background. So based on what that environment is, it could be the sky, it could be industrial and so forth. And you choose the raw data here, conveyor belt, background and they're generated. So I've got a conveyor belt background here, I generate that and basically it generates all these images with these parts on conveyor belt.\r\n\r\nAnd these may not look that realistic but it's good enough for the randomization of the data set. And that's what you're trying to do. Randomize the data set so that it creates a more robust model. So we can move on to 3D <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a>, which is also important. But it's important to understand that not all companies have CAD files. And this is a requirement for 3D synthetic data, you need to have a object file and the material file. So it's textures and the object, and you just basically upload it. And it's similar to the 2D that you choose a background theme and number of images and it generates it with the bounding boxes inside of it. Let's do this in 3D data set. So you can see here it's ATR 42 object. And then it looks like this, you rotate around and do what you need to do with it.\r\n\r\nAnd basically, what you're doing is how many images do you want to generate, press ... You have advanced settings here for power users, and that's like gray scale, in object files as well. You can take out and randomize that and then basically press choose themes or your own theme, and then this is in the air so it's sky. And then basically say generate. And it'll generate these and all you have is you get this data set where you have 3000 images of the ATR 42. And then these are some of the sample images that come out. So again, these are semi-realistic but good enough for the data set to be generated to train the model. And that's what this is really about. Normally best practice is always to use real data to augment any type of synthetic data and vice versa. So if you do have real data, it's important to have that as context because things may be out of context as well.\r\n\r\nSo it's important to generate these together at the same time. So what's the result here. I mean, why are we doing this? This is I. Very well. It's higher F1 scores, higher accuracy and dynamic models. So model drift, you've got problems with accuracy, and you need that feedback and data set to generate constantly higher F1 scores. So here, what you can't do on the system is you can upload a data set, that's what we're going to do, a test data set, parts testing data set. You upload the entire data set which is already annotated. And it'll start generating F1 and then give you that score. So in order to make this a very quick process of understanding how that model actually is delivered and is being used and what the accuracy is, you need to be able to have this test data set. And then you get the different types of F1 scores here based on that.\r\n\r\nWe usually recommend deploying after 90, you can deploy on production. Anything below 90, it depends on the use case but can be problematic. So you can see that here and then automatically deploy on any of the devices that you might have or just use the API cloud. So we can go on to questions now and I will just stop sharing this. Actually I'll keep sharing because I might refer back to it. Yeah.\r\n\r\nJeffrey Goldsmith: Yeah. Thanks, Emrah. That was a pretty complete overview of how we generate data. So we do have a few questions. For the benefit of those who don't know, please explain why should we use synthetic data? I think we've answered that.\r\n\r\nEmrah Gultekin: Let me get into detail on these questions because this is quite important. You don't have to. Synthetic data is not a must for everything, it's a tool. It's a component of something to do if you choose to do that, depending on how much data you already have. If you do have real data, that's always better. But from our experience in the market, it's very difficult to come across that and synthesize that data. I'll give you an example from text recognition. So text detection, text recognition, it would not exist today without synthetic data. That stuff is all synthetic, understanding texts in the natural environment. So we believe that's going to be the case with object detection and image classification as well. But it's not a must to be able to train a model, you don't need it to train a model but these are just tools to help. And again, there are companies that we partner with that generate <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a> as well, or do annotation work. And you can upload those into the system and train the model. Our core as a company has always been the inference engine.\r\n\r\nJeffrey Goldsmith: Okay, the next question, is it possible to try the synthetic data generation tool before committing to a trial in the system?\r\n\r\nEmrah Gultekin: Oh, yes it is. So just get in touch with us. We'll upgrade you to enterprise for a trial without a fee basically, so you can use it. It's out of the box.\r\n\r\nJeffrey Goldsmith: Yeah. It just requires that you ... For the 3D synthetic generation, as Emrah said, you need a CAD file to make that go.\r\n\r\nEmrah Gultekin: CAD and material file, yeah texture. If you don't have the MTL File, the textures won't be randomized basically.\r\n\r\nJeffrey Goldsmith: So is this a web based tool or does require local scripting coding and Docker deployment?\r\n\r\nEmrah Gultekin: So this is a web based tool. And it all resides on the cloud. So you can basically log in from anywhere and use it. So the annotation tools and some of the synthetic tools, that's all web based. And the inferencing is also web based, unless you want the inference engine deployed on the edge or on-prem and you can do that through the system as well. So you can set up a device like any of the Nvidia GPUs, even the Jetson devices and then pull the Docker and have the inferencing run on the edge as well. But yeah, you can use it on the cloud.\r\n\r\nJeffrey Goldsmith: Yeah. And you can sign up today in the upper right corner of the website and start using it. We have a question. What challenges have you seen in using 3D synthetic data? Challenges, Emrah?\r\n\r\nEmrah Gultekin: Yeah, so 3D synthetic data or 2D synthetic data, it's not a panacea. It's not going to solve all your problems, it's not going to ... And so the issue has been the expectations of the market. And the 3D synthetic data in particular is harder to come by because it requires a 3D model of that particular object. And so that usually ... If you're a manufacturer of that object, you probably have it. But outside of that, you're not going to get a 3D model unless you work with let's say, a 3D model designing company that can generate those. But that's been the challenge with 3D is just getting the CAD and the texture files ready. And that's something that is overcome by some of the clients that we work with.\r\n\r\nJeffrey Goldsmith: Okay, the next question is quite important. I suppose this person missed the beginning of the presentation. Can we create models using Chooch or just generate data? And the answer is yes. The point of creating data is to generate models. So we generate data, we create models, and then we do inferencing, the whole life cycle is there it at <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Chooch AI<\/a>.\r\n\r\nEmrah Gultekin: So the whole point of generating the data is to create a model. And so you can do that on the system as well. We can go into more detail on that on another webinar. But basically, our whole thing has been the inference engine, which is the model itself. And by the way, to test the data set, the best thing to do is to train it because you're never going to be able to test it otherwise properly.\r\n\r\nJeffrey Goldsmith: Right. And that leads us into our next question, which is, could you give a more intuitive explanation of what an F1 score is?\r\n\r\nEmrah Gultekin: Yeah. So F1 it's what data scientists like to use but it's basically another ... It's a fancy name for accuracy. So accuracy is made up of two things. It's basically precision, which you would say false positive. So is this an airplane? If it says it's a helicopter, that's a false positive. And then you have recall, where it should have detected something but it didn't. And that would be a false negative. So it's basically just an average of those two. It's just a fancy name for accuracy.\r\n\r\nJeffrey Goldsmith: Yeah. We are getting some questions in chat too. So I employ you to enter your questions in the Q&A, but we'll get to the chat questions after we're done with the Q&A. Let's see here. Any recommendations on how much synthetic versus real data is needed for a successful computer vision model batch size?\r\n\r\nEmrah Gultekin: Yeah. We can talk about our system where you create the models, we recommend a minimum of 1000 images per class. And so that would be ... Depending on the use case that you're doing, real data, you want to have at a minimum of 100, 150. So about 10 to 15% of that. And then you can generate some 2D on that and some data augmentation. 3D is a separate deal. You can generate more on that and there are different types of best practices for that. But you want to get to about 1000 images. How you get to there, that's up to you. You can use synthetic, you can use ... If you have real, that's always better. But a minimum of 1000 and then just keep going up from 1000 for production purposes.\r\n\r\nJeffrey Goldsmith: Okay. Do you support deployment on mobile? We actually have a mobile app. But I really want to ...\r\n\r\nEmrah Gultekin: Yeah, so the deployment on a mobile is through an API. So we'll be hitting the cloud. If you're talking about the models being deployed on mobile, we don't do that at the moment. But we do deploy on Nvidia Jetson devices, also Intel CPUs as well. But in terms of the mobile apps, it's traditionally been just API call on the cloud.\r\n\r\nJeffrey Goldsmith: Yeah, we actually have a mobile app if you search in any app store for Chooch IC2. You can install it on your phone and try it. It basically sends screen grabs from your video feed to the cloud and it sends back metadata. It's pretty cool, actually. So next question, how important is it to train the data with different backgrounds? Can we load our own backgrounds with different conditions and lightings to train? There we go.\r\n\r\nEmrah Gultekin: Yeah, it's a good question. So we have some preset backgrounds that you can use. But usually what happens is the client has their own warehouse or manufacturing area, then they can load up that background as well. And so you can use that background to synthesize your data as well. So that's under my themes. And basically, you just ... You can upload to a data set itself or you can upload to raw data, and just pull that in when you click onto my themes.\r\n\r\nJeffrey Goldsmith: Okay, great question. What if my data is sensitive and cannot go out of my company? Is it possible to deploy it on company servers?\r\n\r\nEmrah Gultekin: Yeah, so this is a great question. And what we're doing is we have ... The inference engine is Dockerized. And you can deploy the inference engine on prem, which means none of that video inferencing or image inferencing will leave your device or your on prem installation. For the entire system, we're working on Dockerizing that, including the training system and the data generation system, and we have clients who are waiting for that actually. It's quite important. So we're going to have that out by the end of the summer where you can basically take it and use it anywhere you want on any of the servers.\r\n\r\nJeffrey Goldsmith: Yeah. And the next question is a comment. We need a webinar on model creation. Well, I'm sure we'll publish one on that very soon. Is there an advantage to using a tight bounding polygon over a simple bounding box, advantages of background removal?\r\n\r\nEmrah Gultekin: Yeah, that's a very good question. So depending on your use case, bounding box or polygon, you're going to choose between the two basically. And data scientists know which ones work for what. For 90% of use cases that we do, bounding box is fine. But even the bounding box itself, it actually segments that piece out. And that's how the 2D generation is generated. So background removal is crucial. You cannot do background removal without polygon segmentation. And that's how the 2D synthetic works. But in terms of inferencing and creating data set, bounding boxes are usually enough for generic deployments. We have sensitive deployment like satellite imagery or radiology, you definitely need polygon.\r\n\r\nJeffrey Goldsmith: Yep. We already answered to some degree, perhaps you're getting to dive into this a little bit. Do you support edge inferencing?\r\n\r\nEmrah Gultekin: Yeah, we support edge inferencing. The inference engine is exportable into the GPU devices and also Intel devices. And it's basically a Docker that you pull and it gets set up within half an hour. And you can put models onto it, you can do updates, you can erase models, you can visualize the inferencing on it. So yes, edge inferencing is a crucial part of this whole system because if you don't have edge inferencing, it's virtually impossible to scale video in the long run.\r\n\r\nJeffrey Goldsmith: And we've actually got documentation on how to deploy to the edge on our website, under the products section at the bottom. There's a few different help documents. Next, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a> looks different. How can we know it will perform well?\r\n\r\nEmrah Gultekin: Yeah, so it looks different and you're going to have to iterate on it. And that's what we do as well. And the way to iterate is you create a model, basically. And you create the model on the system and see how it performs, test it on the system that you've created the synthetic data on, it's basically all together. So you're right, you need to iterate on these. It's not going to perform well the first time. It'll perform ... Depends on the use case, obviously but usually it takes us about four to five iterations to get to a model that is production ready.\r\n\r\nJeffrey Goldsmith: Pretty technical question here. Do you use GANs or VAEs to create synthetic data?\r\n\r\nEmrah Gultekin: This is a good question. And we are not using GANs or VAEs to create yet, but we're using randomization. I think some our ML engineers can answer this much better. But basically, we're in the process of putting GANs into the system, though, as we speak. It's a good question.\r\n\r\nJeffrey Goldsmith: Here's a pretty good on thought leadership question, what impact will synthetic data generation have on the future of computer vision?\r\n\r\nEmrah Gultekin: It's going to be a ... So think of it this way, you do have a lot of data out there. But it's unstructured. And our duty as people in the computer vision industry or AI and generalist is to structure that data. So the way you structure data is by seeding it through the inference engine in order to detect things. So the impact it will have on <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is enormous in terms of getting the models out and having different types of models. We think it is. Synthetic data is very, very important. But again, it's a tool. It's not the end all in this. So we don't want to overemphasize something. It is an important tool but it is part of a larger thing that's going on in computer vision.\r\n\r\nJeffrey Goldsmith: Is the background combined with data and synthesized? I'm not quite sure I understand the question there.\r\n\r\nEmrah Gultekin: Yeah. I think I do but maybe I'm wrong. So in 3D synthetic data, you have the object file, the CAD file, the material file, MTL and then you choose a background as well. So you choose the background images. It could be an already generated theme on the system that we have or it could be your own theme. So it is synthesizing randomize with those backgrounds. So the background is not synthesized. The background is what you put into the system. So you might have like 2000 images of background of your warehouse and that's where you're going to be placing those different objects.\r\n\r\nJeffrey Goldsmith: Okay. The last question in Q&A and then we'll move over to the chat. Can you please explain using randomization for synthetic data generation?\r\n\r\nEmrah Gultekin: Yeah. So in terms of randomization, you have different tools here that you are able to ... There are toggle switches basically. So if you're a power user, you can randomize the way you want and basically, place things in the places you want or you just use general randomization. And it's on a randomization principle, where you can basically just let the machine randomize what it's doing. But what's important here is if you are a power user, you want to be able to control that randomization. And so you have those toggle switches in advanced settings to do that.\r\n\r\nJeffrey Goldsmith: Emrah, are there any particular verticals that ... E.g manufacturing where you see more adoption of synthetic data?\r\n\r\nEmrah Gultekin: Honestly, the clients don't care about the use of <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a> or real data, or what type of data you're generating. What they really care about is how the model performs out of the box, how the model performs in their environment. So what we're seeing more and more though is that the synthetic data is being used more in certain verticals where there's a lack of data or lack of structured data, and manufacturing is one of them actually. But we're seeing it more in geospatial.\r\n\r\nJeffrey Goldsmith: We keep getting questions, which is fine, we still have some time here. How can you generate, for example, a partially covered stop sign with automated labeling? I mean ...\r\n\r\nEmrah Gultekin: It's a good question. And so this goes into CGI really and it is a universe unto itself. So you need a CGI person to create that base data to generate that. [crosstalk 00:54:46]. So you generate one of them and then basically the system randomize the rest.\r\n\r\nJeffrey Goldsmith: Exactly. Is it possible to train them on like ... We answered this to some degree but is it possible to train the model and export it in order run it without Chooch?\r\n\r\nEmrah Gultekin: This is a good question because what happens is these models that you see on the Chooch system are not really single models. They're always ensembles, and they're encoded with information from the past, from the purpose that it has already generated over the years. So it's not possible to do it without the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch system<\/a> on prem or on a device, but you're able to export the system as well. So you can export the system to wherever you want to, but you can't take these models out of a vacuum and have them perform.\r\n\r\nJeffrey Goldsmith: Let's see here, could you say which one works better, training the model with synthetic data entirely and later fine tuning the model with real data or training the model with a hybrid data, synthetic data and real data? Depends on the use case.\r\n\r\nEmrah Gultekin: So it's always better to have real data, that is key. It's always important to have that. The more real data you have, the better your model's going to perform in the long run. But if you don't have real data, you need to synthesize it. And that's where the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">synthetic data<\/a> comes into play. So if you do have real data, it's important to have that with the data set that you're basically trying to train.\r\n\r\nJeffrey Goldsmith: Yeah. Last question ... Oh, there's even one more in the Q&A. They keep coming. So I want to share the preset with coworkers. Where can I find the video link? I'll post it on our blog tomorrow, sometime in the afternoon but it'll also be automatically sent to everyone who signed up for the Zoom webinar and on LinkedIn as well. Let's see. Does Chooch work with all the major public cloud vendors, AWS, Azure, IBM, etc?\r\n\r\nEmrah Gultekin: Yeah, so currently we're on AWS. And we're looking into some of the others yet so that ... The Dockerization of the entire system is important for us as a milestone. So once we have it Dockerized, you can basically take it anywhere. You don't even have to contact us. It could work on private cloud, on prem and so forth.\r\n\r\nJeffrey Goldsmith: Okay, so let's move to the chat here. First question is, is it a web service? And the answer is yes. If you go to Chooch.ai, go up to the upper right corner of the screen and sign up and you'll see our web service right there. The second question, OpenVINO needs pre-trained models, correct? So perhaps your solution is more adequate for TensorFlow training. Does that make sense? Look at the chat.\r\n\r\nEmrah Gultekin: Yeah. Okay. The backend of this is PyTorch, Balloon, TensorFlow, TensorRT for the compression and so forth. These deep learning frameworks, some of them perform better in different environments. So it's always an ensemble on our system. But TensorFlow is definitely one of them. So we do use TensorFlow for some of the image classification.\r\n\r\nJeffrey Goldsmith: Another question from the same attendee, your platform is only used to generate training data sets and annotations or do you put together object AI recognition applications too?\r\n\r\nEmrah Gultekin: Yeah. That's a good question. So our core is actually the inferencing. So the models arc is our core. These are only tools to beef up the data set in order to make the model better. So, that's really what this is about. But the core is the model. So you just click create model, and you click the data set that you've generated and it'll just generate that model, and then you can test the model on the same system.\r\n\r\nJeffrey Goldsmith: And we're almost done with the questions here and we're 10 minutes before the top of the hour. Does your system handle domain adaptation? Domain adaptation. Do you see that question, Emrah?\r\n\r\nEmrah Gultekin: Yeah, I don't understand the question.\r\n\r\nJeffrey Goldsmith: Yeah. Matthew, could you rephrase that potentially, if you're still on the call and we'll answer that in a moment. How much accuracy increased going from 2D to 3D synthetic?\r\n\r\nEmrah Gultekin: So we can say, in general, depending on how much data you have, from real data manual data, which you might have 100 images of and then you create 2D let's say with augmentation, 1000 images, you're going from 50% accuracy to about 90% on average. But it really depends on the use case. From 2D to 3D, there's no comparison because it's a different thing. 3D synthetic data is very, very different. So we don't have metrics on that. But from going from real data to any type of synthetic with augmentation, you're basically reducing the accuracy by leaps and bounds.\r\n\r\nJeffrey Goldsmith: Okay, looks like this our last question. Usually, 2D image, object detection is feasible. What about 3D image object recognition? Where are we on that?\r\n\r\nEmrah Gultekin: We don't do 3D object detection or recognition. It's 2D. And the reason is because the market ... All the sensors are 2D, all the cameras are ... It's an important question and we see a future on that. But the current market is all 2D.\r\n\r\nJeffrey Goldsmith: Okay. Well, thank you everyone for your questions. Oops, there's one more. Hi, in my experience, that question about domain adaptation, isn't it a sub discipline of machine learning which deals with scenarios in which a model train on a source distribution is used in the context of a different but related target distribution?\r\n\r\nEmrah Gultekin: Right, okay, so you're talking about the domain in that sense? Yeah. If the domain is changing and you have ... Let's say the views are changing, the domain is changing, usually you need to tweak the model. And that's part of the process of creating a dynamic user feedback for a grift. That's really what this is about. So if you go back to the earlier slides where you're getting new user feedback with annotated or with just raw images or video streams, with a change in domain, you've put that into shadow data sets which are checked by humans and checked by a machine. And then either you retrain the model, or you create a new model if the domain is very, very different. So there are few layers that are going on to trigger different models, depending on the scene and on the domain.\r\n\r\nJeffrey Goldsmith: So second last question. If anybody has anything else, please post it now. So what kind of support do you have for transfer learning? Transfer-\r\n\r\nEmrah Gultekin: Yeah. So the whole system is based on transfer learning actually, that's how we've generated these models and these classifications as well. So it's based on transfer learning from a base data set that we've trained initially, and we keep retraining that as well.\r\n\r\nJeffrey Goldsmith: Okay, great. Well, I think we are done here. Thanks for the talk. Can you share the recording with us? Absolutely, I can share the recording with you. It'll be on the blog tomorrow. You'll get notified by Zoom. Look for a link on the LinkedIn invite for the event and we'll post it there. Emrah, thanks for the presentation. It was really well done. And thanks to the team for all your hard work putting together this technology. Please get in touch with us if you want any one on one support. And we'll talk to y'all soon.\r\n\r\nEmrah Gultekin: Thank you very much, everybody.\r\n\r\n<strong>Learn more about <a href=\"\/see-how-it-works\/\">computer vision with synthetic data<\/a>.<\/strong>",
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"post_content": "Learn more about <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision and AI<\/a> with this short list of top-level computer vision and AI terminology. Don\u2019t know what AI training is? An AI model? Image recognition? <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">What is an edge device?<\/a> Find out from these quick computer vision definitions.\r\n<ul>\r\n \t<li><strong>Action recognition:<\/strong> A subfield of computer vision that seeks to identify when a person is performing an action, such as running, sleeping, or falling.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/api\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>AI API<\/strong><\/a><strong>:<\/strong> An application programming interface (API) for users to gain access to artificial intelligence tools and functionality. By offering third-party AI services, AI APIs\u00a0save developers from having to build their own AI in-house.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-computer\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>AI computer<\/strong><\/a><strong>:<\/strong> Any computer that can perform computations for artificial intelligence and machine learning, i.e. performing AI training and running AI models. Thanks to recent technological advances, even modest consumer-grade hardware is now capable of being an AI computer, equipped with a powerful CPU and GPU.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>AI demo<\/strong><\/a><strong>:<\/strong> A demonstration of the features and capabilities of an AI platform, or of artificial intelligence\u00a0in general.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>AI model<\/strong><\/a><strong>:<\/strong> The result of training an AI algorithm, given the input data and settings (known as \u201chyperparameters\u201d). An AI model is a distilled representation that attempts to encapsulate everything that the AI algorithm has learned during the training process. AI models can be shared and reused on new data for use in\u00a0real-world environments.<\/li>\r\n \t<li><a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>AI platform<\/strong><\/a><strong>: <\/strong>A software library or framework for users to build, deploy, and manage applications that leverage artificial intelligence. AI platforms are less static and more extensive than AI APIs: whereas AI APIs return the results of a third-party pre-trained model, AI platforms allow users to create their own AI models for different purposes.<\/li>\r\n \t<li><strong>AI training<\/strong><strong>:<\/strong> The process of training one or more AI models. During the training process, AI models \u201clearn\u201d over time by looking at more and more input data. After making a prediction about a given input, the AI model discovers whether its prediction was correct; if it was incorrect, it adjusts its parameters to account for the error.<\/li>\r\n \t<li><strong>Algorithm: <\/strong>A well-defined, step-by-step procedure that can be implemented by a computer. Algorithms must eventually terminate and are used to perform a particular task or provide the answer to a certain problem.<\/li>\r\n \t<li><strong>Annotation:<\/strong> The process of\u00a0labeling the input data in preparation for AI training. In <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>, the input images and video must be annotated according to the task you want the AI model to perform. For example, if you want the model to perform image segmentation, the annotations must include the location and shape of each object in the image.<\/li>\r\n \t<li><strong>Anomaly detection<\/strong><strong>:<\/strong> A subfield of AI, <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a>, and data analysis that seeks to identify anomalies or outliers in a given\u00a0dataset. Anomaly detection is applicable across a wide range of industries and use cases: for example, it can help discover instances of bank fraud or defects in manufacturing equipment.<\/li>\r\n \t<li><strong>Artificial general intelligence (AGI): <\/strong>A type of artificial intelligence that can accomplish a wide variety of tasks as well as, or even better than, human beings. So far, attempts to build an AGI have been unsuccessful.<\/li>\r\n \t<li><strong>Artificial intelligence (AI)<\/strong><strong>:<\/strong> A field of computer science that seeks to bring intelligence to machines, usually by simulating human thought and action. AI enables computers to learn from experience and adjust to unseen inputs.<\/li>\r\n \t<li><strong>Artificial Intelligence of Things (AIoT)<\/strong> <strong>:<\/strong>The intersection of artificial intelligence with the Internet of Things: a vast, interconnected network of devices and sensors that communicate and exchange information via the Internet. Data collected by IoT devices is then processed by AI models. Common AIoT use cases include wearable technology and smart home devices.<\/li>\r\n \t<li><strong>Artificial narrow intelligence (ANI):<\/strong> A type of artificial intelligence that, in contrast with an AGI, is designed to focus on a singular or limited task (e.g., playing chess or classifying photos of dog breeds).<\/li>\r\n \t<li><strong>Artificial neural network (ANN):<\/strong> Also, just called a \u201cneural network,\u201d a machine learning model that consists of many interconnected artificial \u201cneurons.\u201d These neurons exchange information, roughly simulating the human brain. ANNs are the foundation of deep learning, a subfield of machine learning.<\/li>\r\n \t<li><strong>Backpropagation:<\/strong> The main technique by which ANNs learn. In backpropagation, the weights of the connections between neurons are modified via gradient descent so that the network will give an output closer to the expected result.<\/li>\r\n \t<li><strong>Bayesian network:<\/strong> A probabilistic model in the form of a graph that defines the conditional probability of different events (e.g., the probability of event A happening, given that event B does or does not happen).<\/li>\r\n \t<li><strong>Big data:<\/strong> The use of datasets that are too large and\/or complex to be analyzed by humans or traditional data processing methods. Big data may present challenges in terms of velocity (i.e., the speed at which it arrives) or veracity (i.e. maintaining high data quality).<\/li>\r\n \t<li><strong>Chatbot: <\/strong>A computer program that uses natural language processing methods to conduct realistic conversations with human beings. Chatbots are frequently used in fields such as customer support (e.g., answering simple questions or processing item returns).<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Computer vision<\/strong><\/a><strong>: <\/strong>A subfield of computer science, artificial intelligence, and machine learning that seeks to give computers a rapid, high-level understanding of images and videos, \u201cseeing\u201d them in the same way that human beings do. In recent years, computer vision has made great strides in accuracy and speed, thanks to deep learning and neural networks.<\/li>\r\n \t<li><strong><a href=\"https:\/\/www.chooch.com\/platform\/\">Computer vision platform<\/a>:<\/strong> An IT solution for building and deploying computer vision applications, bundling together a software development environment with a set of associated computer vision resources.<\/li>\r\n \t<li><strong>Computer vision solution<\/strong> <strong>:<\/strong>A tool or platform that helps users integrate <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> into their workflows, even without in-depth\u00a0knowledge of computer vision or AI. Thanks to the wide range of applications for computer vision, from healthcare and retail to manufacturing and security, businesses of all sizes and industries are increasingly adopting computer vision solutions.<\/li>\r\n \t<li><strong>Convolutional neural network (CNN):<\/strong> A special type of neural network that uses a mathematical operation known as a \u201cconvolution\u201d to combine inputs (e.g., nearby pixels in an image). CNNs excel at higher-dimensional input such as images and videos.<\/li>\r\n \t<li><strong>Data augmentation<\/strong><strong>:<\/strong> A technique to increase the size of your datasets by making slight modifications to the existing images in the dataset. For example, you can rotate, flip, scale, crop, or shift an image in multiple ways to create dozens of augmented images. Incorporating augmented data can help the model learn to generalize better instead of overfitting to recognize the images themselves.<\/li>\r\n \t<li><strong>Data collection<\/strong><strong>:<\/strong> The process of\u00a0accumulating large quantities of information for use in\u00a0training an AI model. Data can be collected from proprietary sources (e.g. your own videos) or from publicly available datasets, such as the<a href=\"https:\/\/image-net.org\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0ImageNet<\/a>\u00a0database. Once collected, data must be annotated or tagged for use in\u00a0AI training.<\/li>\r\n \t<li><strong>Data mining: <\/strong>The use of automated techniques to uncover hidden patterns and insights in a dataset and make smarter data-driven predictions and forecasts. Data mining is widely used in fields such as marketing, finance, retail, and science.<span style=\"font-weight: 400;\"><img class=\"alignright wp-image-1943 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/artificial-intelligence-and-machine-learning.png\" alt=\"Artificial Intelligence and Machine Learning\" width=\"250\" height=\"250\" \/><\/span><\/li>\r\n \t<li><strong>Deep learning:<\/strong> A subfield of artificial intelligence and <a href=\"https:\/\/www.chooch.com\/blog\/6-applications-of-machine-learning-for-computer-vision\/\">machine learning<\/a> that uses neural networks with multiple \u201chidden\u201d (deep) layers. Thanks to both algorithmic improvements and technological advancements, recent years have seen deep learning successfully used to train AI models that\u00a0can perform many advanced human-like tasks\u2014from recognizing speech to identifying the contents of an image.<\/li>\r\n \t<li><strong>D<a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">efect detection<\/a>:<\/strong>A subfield of computer vision that seeks to identify defects, errors, anomalies, and issues with products or machinery.<\/li>\r\n \t<li><strong>Dense classification: <\/strong>A method for training deep neural networks from only a few examples, first proposed in the 2019 academic paper<a href=\"https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/papers\/Lifchitz_Dense_Classification_and_Implanting_for_Few-Shot_Learning_CVPR_2019_paper.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0\u201cDense Classification and Implanting for Few-Shot Learning\u201d<\/a>\u00a0by Lifchitz et al. Broadly, dense classification encourages the network to look at all aspects of the object it seeks to identify, rather than focusing on only a few details.<\/li>\r\n \t<li><strong>Digital ecosystem:<\/strong> An interconnected collection of IT resources (such as software applications, platforms, and hardware) owned by a given organization, acting as a unit to help the business accomplish its goals.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-definitions\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Edge AI<\/strong><\/a><strong>: <\/strong>The use of AI and machine learning algorithms running on edge devices to process data on local hardware, rather than uploading it to the cloud. Perhaps the greatest benefit of Edge AI is faster speeds (since data does not have to be sent to and from the cloud back and forth), enabling real-time decision-making.<\/li>\r\n \t<li><strong>Edge device<\/strong><strong>:<\/strong> An Internet-connected hardware device that is part of the Internet of Things (IoT) and acts as a gateway in the IoT network: on one hand, the local sensors and devices that collect data; on the other, the full capability of IoT in the cloud. For fastest results, many edge devices are capable of performing computations locally, rather than offloading this responsibility to the cloud.<\/li>\r\n \t<li><strong><a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge platform<\/a>: <\/strong>An IT software development environment that simplifies the process of deploying and maintaining edge devices.<\/li>\r\n \t<li><strong>Ensemble learning: <\/strong>The use of predictions from multiple AI models trained on the same input (or samples of the same input) to reduce error and increase accuracy. Due to natural variability during the training phase, different models may return different results given the same data. Ensemble learning combines the predictions of all these models (e.g. by taking a majority vote) with the goal of improving performance.<\/li>\r\n \t<li><strong>Event detection: <\/strong>A subfield of computer vision that analyzes visual data (i.e. images or videos) in order to detect when an event has occurred. Event detection has been applied\u00a0successfully to use cases such as fall detection and smoke and fire detection.<\/li>\r\n \t<li><strong>Facial authentication<\/strong><strong>: <\/strong>A subfield of <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a> that seeks to verify a person\u2019s identity, usually for security\u00a0purposes. Facial authentication is often performed on edge devices that are powerful enough to identify a subject almost instantaneously and with a high degree of accuracy.<\/li>\r\n \t<li><strong>Facial recognition<\/strong> <strong>:<\/strong>The use of human faces as a biometric characteristic by examining various facial features (e.g. the distance and location of the eyes, nose, mouth, and cheekbones). <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">Facial recognition<\/a> is used both for facial authentication (identifying individual people with their consent) as well as in video surveillance systems that capture people\u2019s images in public.<\/li>\r\n \t<li><strong>Generative adversarial network (GAN):<\/strong> A type of neural network that attempts to learn through competition. One network, the \u201cgenerator,\u201d attempts to create realistic imitations of the training data (e.g., photos of human faces). The other network, the \u201cdiscriminator,\u201d attempts to separate the real examples from the fake ones.<\/li>\r\n \t<li><strong>Genetic algorithm: <\/strong>A class of algorithms that takes inspiration from the evolutionary phenomenon of natural selection. Genetic algorithms start with a \u201cpool\u201d of possible solutions that evolve and mutate over time until reaching a stopping point.<\/li>\r\n \t<li><strong>GPU:<\/strong> Short for \u201cgraphics processing unit,\u201d a specialized hardware device used in computers, smartphones, and embedded systems originally built for real-time computer graphics rendering. However, the ability of GPUs to efficiently process many inputs in parallel has made them useful for a wide range of applications\u2014including training AI models.<\/li>\r\n \t<li><strong>Hash:<\/strong> The result of a mathematical function known as a \u201chash function\u201d that converts arbitrary data into a unique (or nearly unique) numerical output. In facial authentication, for example, a complex hash function encodes the identifying characteristics of a user\u2019s face and returns a numerical result. When a user attempts to access the system, their face is rehashed and compared with existing hashes to verify their identity.<\/li>\r\n \t<li><strong>Image enrichment:<\/strong> The use of AI and machine learning to perform automatic \u201cenrichment\u201d of visual data, such as images and videos, by adding metadata (e.g. an image\u2019s author, date of creation, or contents). In the media industry, for example, image enrichment is used to quickly and accurately tag online retail listings or new agency photos.<\/li>\r\n \t<li><strong>Image quality control:<\/strong> The use of AI and machine learning to perform automatic quality control on visual data, such as images and videos. For example, image quality control tools can detect image defects such as blurriness, nudity, deepfakes, and banned content, and correct the issue or delete the image from the dataset.<\/li>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/imagechat\/\"><strong>Image recognition<\/strong><\/a><strong>: <\/strong>A subfield of AI and computer vision that seeks to recognize the contents of an image by describing them at a high level. For example, a trained image recognition model might be able to\u00a0distinguish between images of dogs and images of cats. Image recognition is contrasted with image segmentation, which seeks to divide an image into multiple parts (e.g. the background and different objects).<\/li>\r\n \t<li><strong>Image segmentation:<\/strong> A subfield of computer vision that seeks to divide an image into contiguous parts by associating each pixel with a certain category, such as the background or a foreground object.<\/li>\r\n \t<li><strong>Industrial Internet of Things (IIoT)<\/strong> <strong>:<\/strong>The use of Internet of Things (IoT) devices in industrial and manufacturing contexts. IIoT devices can be used\u00a0to\u00a0inspect industrial processes, detect flaws and defects in products and manufacturing equipment, promote workplace safety by detecting the use of personal protective equipment (PPE), and much more.<\/li>\r\n \t<li><strong>Inference: <\/strong>The use of a trained machine learning model to make predictions about a previously unseen dataset. In other words, the model infers the dataset\u2019s contents using what it has learned from the training set.<\/li>\r\n \t<li><strong>Internet of Things\/IoT: <\/strong>A vast, interconnected network of devices and sensors that communicate and exchange information via the Internet. As one of the fastest-growing tech trends (with an estimated<a href=\"https:\/\/securitytoday.com\/Articles\/2020\/01\/13\/The-IoT-Rundown-for-2020.aspx?Page=2\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0127 new devices<\/a>\u00a0being connected every second), the IoT has the potential to transform industries such as manufacturing, energy, transportation, and more.<\/li>\r\n \t<li><strong>JSON response:<\/strong> A response to an API request that uses the popular and lightweight<a href=\"https:\/\/www.json.org\/json-en.html\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0JSON (JavaScript Open Notation) file format<\/a>. A JSON response consists of a top-level array that contains one or more key-value pairs (e.g. { \u201cname\u201d: \u201cJohn Smith\u201d, \u201cage\u201d: 30 }).<\/li>\r\n \t<li><strong>Labeling: <\/strong>The process of\u00a0assigning a label that provides the correct context for each input in the training dataset, or the \u201canswer\u201d that you would like the AI model to return during training. In computer vision, there are two types of labeling: annotation and tagging. Labeling can be performed in-house or through outsourcing or crowdsourcing services.<\/li>\r\n \t<li><strong>Liveness detection<\/strong><strong>: <\/strong>A security feature for facial authentication systems to verify that a given image or video represents a live, authentic person, and not an attempt to fraudulently bypass the system (e.g. by wearing a mask of a person\u2019s likeness, or by displaying a sleeping person\u2019s face). Liveness detection is essential to guard against malicious actors.<\/li>\r\n \t<li><strong>Machine learning<\/strong><strong>: <\/strong>A subfield of AI and computer science that studies algorithms that can improve themselves over time by gaining more experience or viewing more data. Machine learning includes both supervised learning (in which the algorithm is given the expected results or labels) and unsupervised learning (in which the algorithm must find patterns in unlabeled data).<\/li>\r\n \t<li><strong>Machine translation:<\/strong> The use of computers to automatically translate text from one natural (human) language to another, without assistance from a human translator.<\/li>\r\n \t<li><strong>Machine vision:<\/strong> A subfield of AI and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> that combines hardware and software to enable machines to \u201csee\u201d at a high level as humans can. Machine vision is distinct from computer vision: a machine vision system consists of both a mechanical \u201cbody\u201d that captures images and videos, as well as computer vision software that interprets these inputs.<\/li>\r\n \t<li><strong>Metadata:<\/strong> Data that describes and provides information about other data. For visual data such as images and videos, metadata consists of three categories: technical (e.g. the camera type and settings), descriptive (e.g. the author, date of creation, title, contents, and keywords), and administrative (e.g. contact information and copyright).<\/li>\r\n \t<li><strong>Motion tracking:<\/strong> A subfield of computer vision with the goal of following the motion of a person or object across multiple frames in a video.<\/li>\r\n \t<li><strong>Natural language processing (NLP):<\/strong> A subfield of computer science and artificial intelligence with the goal of making computers understand, interpret, and generate human languages such as English.<\/li>\r\n \t<li><strong>Near-edge AI<\/strong><strong>: <\/strong>The deployment of AI systems on the \u201cnear edge,\u201d i.e., computing infrastructure located\u00a0between the point of data collection and remote servers in the cloud.<\/li>\r\n \t<li><strong>Neural network: <\/strong>An AI and machine learning algorithm that seeks to mimic the high-level structure of a human brain. Neural networks have many interconnected artificial \u201cneurons\u201d arranged in multiple layers, each one storing a signal that it can transmit to other neurons. The use of larger neural networks with many hidden layers is known as deep learning.<\/li>\r\n \t<li><strong>No-code AI: <\/strong>The use of a no-code platform to generate AI models without the need to write lines of computer code (or be familiar with computer programming at all).<\/li>\r\n \t<li><strong>Object recognition<\/strong><strong>:<\/strong> A subfield of computer vision, artificial intelligence, and machine learning that seeks to recognize and identify the most prominent objects (i.e., people or things) in a digital image or video.<\/li>\r\n \t<li><strong>Optical character recognition (OCR):<\/strong> A technology that recognizes handwritten or printed text and converts it into digital characters.<\/li>\r\n \t<li><strong>Overfitting:<\/strong> A performance issue with machine learning models in which the model learns to fit the training data too closely, including excessive detail and noise. This causes the model to perform poorly on unseen test data. Because overfitting is often caused by a lack of training data, techniques such as data augmentation and synthetic data generation can help alleviate it.<\/li>\r\n \t<li><strong>Pattern recognition:<\/strong> The use of machine learning methods to automatically identify patterns (and anomalies) in a set of input data.<\/li>\r\n \t<li><strong>Pre-trained model:<\/strong> An AI model that has already been trained on a set of input training data. Given an input, a pre-trained model can rapidly return its prediction on that input, without needing to train the model again. Pre-trained models can also be used for transfer learning, i.e. applying knowledge to a different but similar problem (for example, from recognizing car manufacturers to truck manufacturers).<\/li>\r\n \t<li><strong>Presentation attack<\/strong><strong>:<\/strong> An attempt to thwart biometric systems by spoofing the characteristics of a different person. With facial recognition software, for example, presentati<img class=\"alignright wp-image-1870\" src=\"\/wp-content\/uploads\/2023\/07\/ai-training-model.png\" alt=\"AI Training Model\" width=\"300\" height=\"300\" \/>on attacks may consist of printed photographs or 3D face masks presented to the camera by the attacker. Techniques such as liveness detection are necessary to avoid presentation attacks.<\/li>\r\n \t<li><strong>Recurrent neural network (RNN):<\/strong> A special type of neural network that uses the output of the previous step as the input to the current step. RNNs are best suited for sequential and time-based data such as text and speech.<\/li>\r\n \t<li><strong>Reinforcement learning: <\/strong>A subfield of AI and machine learning that teaches an AI model, using trial and error, how to behave in a complex environment in order to maximize its reward.<\/li>\r\n \t<li><strong>Robotic process automation (RPA):<\/strong> A subfield of business process automation that uses software \u201crobots\u201d to automate manual repetitive tasks.<\/li>\r\n \t<li><strong>Robotics: <\/strong>An interdisciplinary field combining engineering and computer science that seeks to build intelligent machines known as \u201crobots,\u201d which have bodies and can take actions inthe physical world.<\/li>\r\n \t<li><strong>Segmentation: <\/strong>A subfield of AI and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> that seeks to divide an image or video into multiple parts (e.g. the background and different objects). For example, an image of a crowd of people might be segmented into the outlines of each individual person, as well as the image\u2019s background. Image segmentation is widely used for applications such as healthcare (e.g. identifying cancerous cells in a medical image).<\/li>\r\n \t<li><strong>Sentiment detection:<\/strong> A subfield of AI and computer vision that seeks to understand the tone of a given\u00a0text. This may include determining whether a text has a positive, negative, or neutral opinion, or whether it contains a certain emotional state (e.g. \u201csad,\u201d \u201cangry,\u201d or \u201chappy\u201d).<\/li>\r\n \t<li><strong>Strong AI: <\/strong>A synonym for artificial general intelligence (AGI). \u201cStrong AI\u201d refers to a theoretical AI model that could duplicate or even surpass human capability across a wide spectrum of activities, serving as a machine \u201cbrain.\u201d<\/li>\r\n \t<li><strong>Structured data<\/strong><strong>:<\/strong> Data that adheres to a known, predefined schema, making it easier to query and analyze. Examples of structured data include student records (with fields such as name, class year, GPA,\u00a0etc.), and daily stock prices.<\/li>\r\n \t<li><strong>Supervised learning: <\/strong>A subfield of machine learning that uses both input data and the expected output labels during the training process. In this way, the computer can easily identify and correct its mistakes.<\/li>\r\n \t<li><a href=\"\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Synthetic data<\/strong><\/a><strong>:<\/strong>Realistic but computer-generated image data that can be used\u00a0to\u00a0increase the size of your datasets during AI training. Using a 3D model and its associated texture, synthetic data (and the corresponding annotations or bounding boxes) can be generated with a wide variety of poses, viewpoints, backgrounds, and lighting conditions.<img class=\"alignright size-full wp-image-2517\" src=\"\/wp-content\/uploads\/2023\/07\/synthetic-data-for-ai.png\" alt=\"Synthetic data for AI\" width=\"500\" height=\"282\" \/><\/li>\r\n \t<li><strong>Tagging:<\/strong> The process of\u00a0labeling the input data with a single tag in preparation for AI training. Tagging is similar to annotation, but uses only a single label for each piece of input data. For example, if you want to perform image recognition for different dog breeds, your tags may be \u201cgolden retriever,\u201d \u201cbulldog,\u201d etc.<\/li>\r\n \t<li><strong>Transfer learning:<\/strong> A machine learning technique that reuses a model trained for one problem on a different but related problem, shortening the training process. For example, transfer learning could apply a model trained to recognize car makes and models to identify trucks instead.<\/li>\r\n \t<li><strong>Turing test:<\/strong> A metric proposed by Alan Turing for assessing a machine\u2019s \u201cintelligence\u201d by testing whether it can convince a human questioner that it is a person and not a computer.<\/li>\r\n \t<li><strong>Unstructured data<\/strong><strong>:<\/strong> Data that does not adhere to a predefined schema, making it more flexible but harder to analyze. Examples of unstructured data include text, images, and videos.<\/li>\r\n \t<li><strong>Unsupervised learning: <\/strong>A subfield of machine learning that provides only input data, but not the expected output, during the training process. This requires the computer to identify hidden patterns and construct its own model of the data.<\/li>\r\n \t<li><strong>Video analytics<\/strong><strong>:<\/strong> The use of AI and <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> to automatically analyze the contents of a video. This may include facial recognition, motion detection, and\/or object detection. Video analytics is widely used in industries such as security, construction, retail, and healthcare, for applications from loss prevention to health and safety.<\/li>\r\n \t<li><strong>Visual AI<\/strong> <strong>:<\/strong>The use of artificial intelligence to interpret visual data (i.e. images and videos), roughly synonymous with computer vision.<\/li>\r\n \t<li><strong>Weak AI:<\/strong> A synonym for artificial narrow intelligence (ANI). \u201cWeak AI\u201d refers to an AI model that focuses on equaling or surpassing human performance on a particular task or set of tasks, with an intentionally limited scope.<\/li>\r\n<\/ul>\r\nWant to learn more about\u00a0computer vision services\u00a0from Chooch AI? <a href=\"https:\/\/www.chooch.com\/contact-us\/\">Contact us<\/a> for\u00a0computer vision consulting.",
"post_title": "Computer Vision Definitions",
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"post_content": "Powered by artificial intelligence and <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a>, computer vision can help digitally transform your business. Today, sophisticated <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">computer vision AI models<\/a> can learn to recognize a wide variety of faces, objects, concepts, and actions, just as well as\u2014if not even better than\u2014humans can. But...\r\n\r\nThere\u2019s just one problem: where are you going to get the data? For best results during AI training, you need to collect potentially hundreds or thousands of images or videos. The more training data you can obtain, the better your computer vision models can learn how to classify different visual phenomena.\r\n\r\nFor example, suppose that you want to build a <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> model that can differentiate between images of dogs and cats. If you only use high-quality photographs as your training data, with each animal close up and facing the camera, then your model might struggle to generalize to real-world situations, with\u00a0types of data that it hasn\u2019t seen before. Some difficulties with image recognition are:\r\n<ul>\r\n \t<li><strong>Different viewpoints:<\/strong> The subject may be photographed from many viewpoints, e.g. from behind, from below, from far away, etc.<\/li>\r\n \t<li><strong>Different lighting conditions:<\/strong> The color and appearance of an object (e.g. an animal\u2019s fur) may vary significantly, depending on the amount of light in the photograph.<\/li>\r\n \t<li><strong>Variations within classes: <\/strong>Objects that are classified in the same category may still appear dissimilar\u2014e.g. animals with wildly different colors, breeds, and appearances that are all classified as \u201cdog\u201d or \u201ccat.\u201d<\/li>\r\n \t<li><strong>Occlusion:<\/strong> Computer vision models need to recognize objects that are partially hidden within the image. For example, the model can\u2019t simply learn that all dogs appear to have four legs\u2014or else it won\u2019t recognize dogs inside blankets and dogs looking out a car window.<\/li>\r\n<\/ul>\r\nIn order to be responsive to these issues, your <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> model needs to be trained on large amounts of labeled, high-quality, and highly diverse data. Unfortunately, generating this type of data manually can be difficult and time-consuming.\r\n<h2>Solving the Problem of Limited Data for Computer Vision<\/h2>\r\n<img class=\"wp-image-2517 size-full alignright\" src=\"\/wp-content\/uploads\/2023\/07\/solving-problem-data-for-computer-vision.png\" alt=\"Solving the Problem of Limited Data for Computer Vision\" width=\"500\" height=\"282\" \/>\r\n\r\nOne solution is to perform data augmentation: increasing the amount of training data by making slight modifications to each image. For example, an image of a dog may be slightly rotated, flipped, shifted, or cropped (or all of the above). This will help the model learn the underlying truth about what a dog looks like, rather than over-fitting by learning to <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">recognize the image<\/a> itself and knowing that \u201cdog\u201d is the right answer. A single image can produce a dozen or more augmented images that can help your computer vision model extrapolate better in real-world scenarios.\r\n<h2>Generating Synthetic Data for Computer Vision<\/h2>\r\nIn addition to simple data augmentations, there\u2019s another solution to the problem of data sparsity: you can generate synthetic data.\r\n\r\nIf you have a realistic 3D model of the object you want to recognize, you can instantly generate hundreds or thousands of images of that <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">3D object with synthetic data generation<\/a>.\r\n<ul>\r\n \t<li>You can vary nearly everything about the object and image\u2014viewpoints, pose, backgrounds, lighting conditions, etc.\u2014so that your <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> model gets a vastly greater range of realistic training data.<\/li>\r\n \t<li>You can automatically create bounding box annotations around the object\u2019s position, rather than having to label each image yourself in a slow, manual process.<\/li>\r\n \t<li>If your 3D model is made up of many smaller parts (e.g. a lawnmower or other machinery), you can even generate data for object segmentation, so that the computer vision model learns to recognize each individual object part.<\/li>\r\n<\/ul>\r\nBy using synthetic data generation, computer vision users can rapidly build and iterate AI models and deploy them to the edge.\r\n\r\nChooch is a computer vision vendor that helps businesses of all sizes and industries\u2014from <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> and construction to <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail<\/a> and <a href=\"https:\/\/www.chooch.com\/solutions\/geospatial-ai-vision\/\">geospatial<\/a>\u2014build cutting-<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge computer vision<\/a> models. Our synthetic data generation feature makes it easy for users to increase the diversity and versatility of their training data. All you need are two files: an\u00a0.OBJ file that describes the object\u2019s 3D geometry, and an\u00a0.MTL file that describes the object\u2019s appearance and textures.",
"post_title": "Training Computer Vision AI Models with Synthetic Data",
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"post_date": "2023-01-18 10:02:50",
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"post_content": "Counting and identifying cells is a tedious and time-consuming process. In many cases, highly paid Ph.D. scientists perform these tasks in the fields of histology, immunology, oncology, and pharmaceutical research. Unfortunately, the painstaking process involves long hours of looking at samples under a microscope and manually counting each cell \u2013 even worse, traditional cell counting methods leave a lot to be desired in terms of accuracy.\r\n\r\n<img class=\"alignnone wp-image-2799\" src=\"\/wp-content\/uploads\/2023\/07\/detecting-white-blood-cell-under-microscope.jpg\" alt=\"Detecting White Blood Cell Under Microscope using Computer Vision\" width=\"921\" height=\"482\" \/>\r\n\r\nEven the highest-trained scientists working under the best laboratory conditions rarely achieve better than 80% accuracy. Meanwhile, the time and labor costs required to complete these tasks represent a massive expenditure for laboratories.\r\n\r\nInterestingly, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> for cell counting offers a compelling solution to these inefficiencies. By leveraging an advanced visual AI platform like <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Chooch AI<\/a>, labs can dramatically improve the speed, accuracy, and cost-efficiency of cell counting activities. Many labs have boosted cell counting accuracy up to 98% \u2013 while reducing their skilled labor requirements. In this respect, computer vision for cell counting offers a <a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/ebook-roi-of-computer-vision-ai.pdf?__hstc=113074139.f77b90cfb712429f39082a80be0e8412.1671140693943.1674588135002.1674593019654.56&__hssc=113074139.26.1674593019654&__hsfp=1855668024\">clear ROI benefit<\/a> compared to traditional methods.\r\n<h2>Traditional Methods of Cell Counting and Identification<\/h2>\r\nThe hemocytometer still prevails as the most practical solution for the vast majority of cell counting use cases. This 19th-century tool consists of a slide with two gridded counting chambers. Scientists manually count the number of cells in the counting chambers to achieve a \u201cworkable\u201d estimate of the concentration of cells. Samples generally require dilution, which can render less accurate results. The hemocytometer process is 80% accurate at best. This method is labor-intensive, and renders less than perfect results, but it\u2019s adequate for many use cases.\r\n\r\nAnother cell counting method involves the use of plating and Colony Forming Unit (CFU) counting. Scientists dilute a cell sample and plate the sample on a petri dish with a growth medium. From there, each cell grows into a colony of cells or CFU after at least 12 hours of growth. Next, scientists manually count each colony to determine the concentration of cells. This method is particularly useful when testing cell resistance to drugs. However, like the hemocytometer, this method is labor-intensive and monotonous, so fatigue and human counting errors are common.\r\n\r\nThere are two more approaches to cell counting that render faster, more accurate results for many use cases. One involves the use of automated cell counters for cell and bacteria enumeration. The other involves the use of flow cytometer equipment. Unfortunately, these last two methods are so expensive that they\u2019re only available to the most distinguished and well-funded research laboratories. Even for the facilities that can afford them, cell counters and flow cytometer equipment bring considerable operational costs and maintenance burdens.\r\n\r\nFor these reasons, <a href=\"https:\/\/www.researchgate.net\/publication\/327441734_Computer_vision_based_automated_cell_counting_pipeline_A_case_study_for_HL60_cancer_cell_on_hemocytometer\" target=\"_blank\" rel=\"noopener noreferrer\">a recent study<\/a> concluded: \u201cIn the low-resource-setting laboratories, standard hemocytometers are the only choice for quantification of cells and bacteria.\u201d In this respect, there is a need for an affordable, automated, and accurate method for counting cells and bacteria that is more efficient than hemocytometers.\r\n<h3><img class=\"alignnone wp-image-2800\" src=\"\/wp-content\/uploads\/2023\/07\/counting-cells-under-microscope.jpg\" alt=\"Counting Cells Under Microscope\" width=\"799\" height=\"419\" \/><\/h3>\r\n<h2>Disadvantages of Manually Counting Cells<\/h2>\r\nThe process of manually counting cells under a microscope comes with a number of disadvantages that, in most cases, laboratories have accepted as an inherent part of the process. These disadvantages include:\r\n<ul>\r\n \t<li>Accuracy problems: Scientists can only achieve an 80% accuracy level under the best of circumstances when using a hemocytometer.<\/li>\r\n \t<li>Expensive labor: The average annual salary for a cell culture scientist in the United States is $85,042. Employing a team of scientists devoted to cell counting represents a significant cost for any research facility.<\/li>\r\n \t<li>Slow process: Scientists can take 30 minutes or longer to count the cells in a single hemocytometer slide. This brings a significant delay to any research or diagnostic activities that require cell counting, which slows down the completion of research, patient diagnoses, and the release of new medicines.<\/li>\r\n \t<li>Scientists prone to fatigue and distraction: The monotonous nature of manually counting cells fatigues scientists and hinders counting accuracy.<\/li>\r\n \t<li>Limitations of human perception: Human perception is limited in terms of the ability to perceive the difference between cells, cell debris, and other particles. In fact, it\u2019s not uncommon for two scientists to give a significantly different result when counting the same sample.<\/li>\r\n \t<li>Highly diluted samples obscure results: Scientists need to dilute samples, which reduces the concentration of cells to make cell counting easier. However, this dilution process can interfere with the ability to produce statistically significant calculations. In other words, the sample could be diluted so much that counting produces inaccurate results.<\/li>\r\n<\/ul>\r\n<h2>Leveraging Visual AI for Better Cell Counting Results<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision technology<\/a> is perfectly suited to automate the task of manually identifying and counting cells. For example, the <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a> is capable of identifying and counting cells with dramatically more accurate results than its human counterparts \u2013 even compared to the results of Ph.D. scientists and experienced research physicians.\r\n\r\nThe tremendous ROI benefits of computer vision technology for cell counting include:\r\n<ul>\r\n \t<li>Faster cell counting results: <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Chooch AI<\/a> completes 30-minute cell counting tasks in milliseconds, allowing the platform to achieve millions of human counting hours in just a few minutes.<\/li>\r\n \t<li>Improved accuracy: Chooch AI achieves 98% accuracy for most cell counting procedures. This is a striking improvement over the 80% standard for cell counting via traditional methods.<\/li>\r\n \t<li>Faster, better workflows: The speed, accuracy, and cost-efficiency benefits of computer vision for cell counting allow laboratories to analyze higher volumes of samples faster and more affordably.<\/li>\r\n \t<li>Labor cost savings: Chooch AI brings tremendous labor cost savings while freeing skilled scientists and doctors to devote their time to more important tasks.<\/li>\r\n \t<li>More competitive drug research: With pharmaceutical companies racing to test and release new drugs as quickly as possible, the efficiency of visual AI helps drug companies bring new medicines to market in record time.<\/li>\r\n \t<li>Better patient outcomes: Computer vision speeds the process of conducting a complete blood count analysis, and accurately counting blood, plasma, and lymph cells. This empowers healthcare practitioners to achieve better patient outcomes by reducing instances of delayed diagnoses and misdiagnoses.<\/li>\r\n \t<li>Lower sample dilution requirement: Computer vision solutions for cell counting are capable of accurately counting the cells in less diluted samples. By reducing the level of dilution, scientists can achieve more accurate counting results.<\/li>\r\n<\/ul>\r\nOne of the primary advantages of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Chooch AI<\/a> is speed of implementation. With Chooch, laboratories can develop and deploy sophisticated computer vision models for cell counting in a matter of days. This is markedly faster than the six to nine months it generally takes to design and implement a visual AI model.\r\n\r\nChooch AI offers research laboratories immediate access to a wide library of pre-built computer vision models for the most common cell counting use cases. For more unique scenarios, laboratories can add layers of training to existing models \u2013 or train entirely new models from scratch \u2013 depending on the needs of the use case.\r\n\r\nThe ROI benefits of computer vision for cell counting are clear. With these new tools, medical labs diagnose patients faster and more accurately; drug companies develop new life-saving medications with greater efficiency; and, skilled scientists and doctors have extra time to devote to more pressing tasks.",
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"post_content": "<span style=\"font-weight: 400;\">Chooch AI models have been developed and deployed for a growing number of applications and demos are available for these healthcare applications. Now, we'll be demoing a wide variety of applications in healthcare at HIMSS 2021 in the startup area C100-78. Please contact us to meet. We also contribute a blog post to HIMSS about the value of <a href=\"https:\/\/www.himss.org\/resources\/value-computer-vision-healthcare\">computer vision in healthcare<\/a> and did a <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> podcast with HIMSS.<\/span>\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/nezqrfAP-g8?controls=0\" width=\"800\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nLet us know if you would like to learn more details about how computer vision works.\r\n\r\n<b>Microscopy.<\/b><span style=\"font-weight: 400;\"> Chooch AI can count cells on slides with 98% accuracy and 100+ times the speed of human cell counting. <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Chooch AI Models<\/a> are currently being licensed for use in research facilities and microscopes. The main focus has been to accelerate drug discovery, but AI models have been created to detect types of cells.\u00a0<\/span>\r\n\r\n<b>Smart Operating Room Computer Vision. <\/b><span style=\"font-weight: 400;\">Collects log information at beginning and end of medical procedures, counts and tracks all surgical protocols, devices and materials. The data generated triggers alerts, actions, and messages to the appropriate parties throughout the system.\u00a0<\/span>\r\n\r\n<b>Workplace and Patient Safety. <\/b><span style=\"font-weight: 400;\">Patient monitoring in hospital settings to detect issues such as falls or other activity. PPE detection is an industry agnostic application of computer vision that protects workers and reduces risk in many industries. In healthcare, these AI models can detect that safety equipment is used and procedures such as handwashing are followed.\u00a0<\/span>\r\n\r\n<b>Imaging Analysis<\/b><span style=\"font-weight: 400;\">. The <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI platform<\/a> is being used for several different imaging analysis use cases. Our AI models are extremely accurate, and fast to train. Any type of imaging process can be used to train AI and detect features for radiology analysis.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Chooch offers complete computer vision solutions for <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare ai.<\/a><\/span>",
"post_title": "Computer Vision for Healthcare at HIMSS 2021",
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"post_date": "2023-01-18 09:53:35",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Healthcare<\/a> facilities throughout the world are suffering from critical staff shortages, and the COVID-19 pandemic has only made the situation worse. According to November 2020 statistics from the U.S. Department of Health and Human Services, 18% of U.S. hospitals said that they were critically short on medical staff. Patient monitoring AI can dramatically improve the ability of hospitals and medical facilities to monitor situations.\r\n\r\nStaffing shortages have made it difficult for hospitals to provide sufficient monitoring of patients who require immediate attention. Tragically, a wide range of patient behaviors \u2013 like sitting up, getting out of bed, coughing, falling, or gesturing for help \u2013 go unnoticed until it\u2019s too late to help the patient in need. <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Patient monitoring AI<\/a> can be life saving in these situations.\r\n\r\n<img class=\"alignnone wp-image-2884\" src=\"\/wp-content\/uploads\/2023\/07\/patient-monitoring-with-computer-vision.jpg\" alt=\"Patient Monitoring with Computer Vision\" width=\"779\" height=\"407\" \/>\r\n<h3>The Tragic Cost of Patient Monitoring Failures<\/h3>\r\nDiligent visual monitoring of patients is key to ensuring the best medical outcomes, but even a fully staffed hospital or medical facility can\u2019t keep an eye on everything. Here are some examples of the tragic cost of patient monitoring failures:\r\n<h4>Falls<\/h4>\r\nMedical patients in hospitals, nursing homes, and other health facilities are more prone to fall injuries. For example, PSNet reports that medical patients fall approximately <a href=\"https:\/\/psnet.ahrq.gov\/issue\/falls-english-and-welsh-hospitals-national-observational-study-based-retrospective-analysis\" target=\"_blank\" rel=\"noopener noreferrer\">3 to 5 times per 1,000 bed-days<\/a>. The Agency for Healthcare Research and Quality reports that an estimated <a href=\"https:\/\/www.ahrq.gov\/patient-safety\/settings\/hospital\/fall-prevention\/toolkit\/index.html\" target=\"_blank\" rel=\"noopener noreferrer\">700,000 to 1 million<\/a> hospital patients fall every year. Moreover, approximately <a href=\"https:\/\/www.ahrq.gov\/patient-safety\/settings\/long-term-care\/resource\/injuries\/fallspx\/man1.html\" target=\"_blank\" rel=\"noopener noreferrer\">50% of the 1.6 million U.S. nursing home residents<\/a> fall every year.\r\n\r\nSadly, over <a href=\"https:\/\/psnet.ahrq.gov\/issue\/preventing-falls-and-fall-related-injuries-health-care-facilities\" target=\"_blank\" rel=\"noopener noreferrer\">33% of hospital patient falls<\/a> result in an injury \u2013 and many of these injuries are serious, involving fractures and head trauma. Beyond the injuries, hospital patient falls may be classified as \u201c<a href=\"https:\/\/psnet.ahrq.gov\/primer\/never-events\" target=\"_blank\" rel=\"noopener noreferrer\">never events,<\/a>\u201d which means that the Centers for Medical and Medicaid Services will not reimburse hospitals for the additional medical costs related to the falls, representing a significant financial burden on the hospital.\r\n\r\nConsidering the risk of fall injuries at medical facilities, medical staff need to know whenever an at-risk patient gets out of bed or suffers a fall. However, it\u2019s impossible to constantly monitor all patients at all times. Considering that medical facilities are usually short on staff, it\u2019s not uncommon for a fallen patient to be left unattended, which can lead to devastating health consequences.\r\n<h4>Coughing, Hand Gestures, and Spasms<\/h4>\r\nPatients suffering from coughing fits \u2013 or various types of hand gestures and bodily spasms \u2013 could be in dire need of assistance to ensure that they are breathing properly and not experiencing a health emergency. Any delay in detecting a patient in this kind of situation could result in a worsened health condition or death.\r\n\r\nIn many cases, health facilities can be held liable for their patient monitoring failures \u2013 especially if those failures are the result of negligence and result in serious injuries or death. Aside from the tragic health consequences and negative impact on patient families, monitoring failures damage the reputations of medical facilities, increase liabilities, and elevate insurance costs.\r\n<h3>Leveraging Visual AI for Better Patient Monitoring<\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Patient monitoring AI technologies<\/a> can dramatically improve the ability of hospitals and medical facilities to detect instances of patients falling, in addition to monitoring patient behaviors and gestures. A trained computer vision system for patient monitoring can easily detect the following patient gestures, movements, and behaviors:\r\n<ul>\r\n \t<li>A hospital patient, ICU patient, post-op patient, or visitor who falls down.<\/li>\r\n \t<li>A patient who swings his or her legs over the side of a bed in preparation to get up.<\/li>\r\n \t<li>A patient who suddenly sits up in bed.<\/li>\r\n \t<li>A patient who is suffering from a coughing fit, sneezing fit, or body spasms.<\/li>\r\n \t<li>A patient who is gesturing his or her arms for help.<\/li>\r\n \t<li>A patient with a bloody nose after reacting badly to a drug protocol.<\/li>\r\n<\/ul>\r\nAt Chooch AI, we can train sophisticated computer vision models to detect all of the above and more. Deployable through the cloud \u2013 or on edge devices for maximum security and privacy \u2013 these devices can use an existing IoT camera network to gather and interpret visual data on patient and hospital visitor activities. Chooch can also install a visual AI system for patient monitoring on a rollable cart that includes (1) a monitoring camera on the top and (2) a visual AI edge server on the bottom.\r\n\r\nMedical staff can position these carts in rooms to monitor patient behavior and provide immediate updates and alerts as required. Any hospital or medical facility can develop and deploy visual AI strategies like these, and start achieving better patient outcomes in a matter of days.\r\n\r\nIn summary, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI computer vision models<\/a> for patient monitoring AI can help hospitals and medical facilities:\r\n<ul>\r\n \t<li>Monitor patient conditions with greater accuracy and attention to detail.<\/li>\r\n \t<li>Instantly respond to patient emergencies as soon as a problem arises.<\/li>\r\n \t<li>More immediately help patients who are experiencing adverse reactions to drugs.<\/li>\r\n \t<li>Receive instant alerts when a patient or anyone in the hospital falls.<\/li>\r\n \t<li>Detect unusual patient behaviors such as a patient getting ready to exit his or her bed, coughing, sneezing, or gesturing for help.<\/li>\r\n<\/ul>",
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"post_date": "2023-01-18 09:53:17",
"post_date_gmt": "2023-01-18 09:53:17",
"post_content": "In this 15 minute presentation, Emrah Gultekin, CEO of Chooch AI, presents how the <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> platform ingests visual data, trains the AI, and exports <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI models<\/a> to the edge. This allows scalable inferencing on the edge on any number of devices from any number of cameras. A transcript of the presentation is provided below the video.\r\n\r\n<iframe src=\"https:\/\/www.youtube.com\/embed\/xdnKDzUkgVY\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nHi, everybody. So today we're going to be talking a little bit about <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> and how that is performed, so let me go ahead and share my screen. So mass deployment of <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI models<\/a> on the Edge, that's what this is about today.\r\n\r\nBasically, what we do here at Chooch is there are three components that make up the entire system, and one is the dashboard, and that's the cloud account that you have. And that's really crucial because that's where you create the account, that's where you select your pre-trained models, you can actually train new models on the account, you can add devices, and so forth. So that's one part of it.\r\n\r\nThe next part is the device itself on the <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge<\/a>, which is usually an NVIDIA device. And then the third component is the camera, so any type of imaging that's coming in. So the camera's associated with the device and that's where the inferences are done, and you're able to manage all that on premise and on the Cloud.\r\n\r\nSo here's an example of what the output looks like on any of these devices and cameras that you have, so safety vests, hard hats, and whatever you basically train it for. And so these are outputs that are saved on the device, and you can create alerts, or you can create SMS messages or email messages, depending on your use case. And you could aggregate all this information and generate the reports as well.\r\n\r\n<img class=\"size-full wp-image-1777 alignright\" src=\"\/wp-content\/uploads\/2023\/06\/ai-model-deployments-for-edge.png\" alt=\"AI Model Deployments for Edge\" width=\"262\" height=\"262\" \/>So if we look at AI as a whole, in terms of the different areas and the different types of things that you need to do to make it work properly, we're looking at three main areas, and that's dataset generation, which is the first bit of it, that's the most crucial part of it in terms of starting out. And then the second part is training, over here. So that's where you create the models. And then inferencing, and that's when you have new predictions coming in, so you have new data coming in and it generates inferences which are predictions of what it sees.\r\n\r\nSo this is like the cycle of it, and then the inferencing goes back into dataset generation as well. So if you have new types of information coming in, new types of data or video streams, it's important to feed it back into dataset generation to refine the model and also update it, or basically, maybe train new classes or new models as well.\r\n\r\nSo the device is really crucial here because that's where it is on the <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge<\/a>, and you have a device and a camera that looks at the area, and then it does inferencing. So what's important here is to be able to put all these streams onto the devices. And the reason is the network load is very low, there is no network load on the device, obviously, so you don't have to send anything to the Cloud. The second issue is privacy, everything stays on the device. And a third is speed, it's two milliseconds per inference. So it's far faster than anything that you're going to do on the Cloud.\r\n\r\nWe have many, many devices and many models, and you can manage these devices and the models from your dashboard.\r\n\r\nAnd then the camera is associated with the device. So you create a device, and then you add cameras to it, and you can add multiple streams to any of these NVIDIA devices. So let's start with dataset generation AI training. That's really, really crucial over here, just how we do it.\r\n\r\nSo on the dashboard, what you do is you first, it depends what you're doing, so it's <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial image or object. Object<\/a> is the most complex, so we start with that. Here, you create a dataset. Let's do that. So it'll ask you to upload images or videos, so you can upload images or videos to create your dataset, and it asks you if you want to do bounding box or polygon annotation. Annotation is a way to label what's inside of that image or inside of that video. So we'll go into some examples for that as well.\r\n\r\nHere's let's say a raw image of what you want to train. And what you start doing is basically doing bounding boxes. And if it's polygon, then you would do segmentation. Here you would do it, and name what you're looking at, so it could be \"hardhat\", it could've been \"red hardhat\" and then security Avast and so forth. So you basically do this manually. If it's an unknown object in the dataset, it'll start giving you these.\r\n\r\nSo you upload these, you annotate them manually. If it's something new, you have to do it manually. If it's a known object that the system already knows, it provides you with suggestions, so it creates a dataset. And here you are, 141 images of a hard hat and 74 security Avast.\r\n\r\nSo this would be the raw images, so you would have raw annotations here. And then what happens in the back is this would be augmented by about x18 images in order to enrich the dataset. So it changes it, augments it in the backend.\r\n\r\nHere you then create a perception. So you go back and you say, \"Hey, We have the dataset, now let's create the perception,\" which is the model. And you name your perception, then you select the dataset, you can reuse these datasets obviously, for different types of models that you're building. And then it starts training it. And then you can see the log of what's going on. And then it's actually trained.\r\n\r\nAnd here, you can do a test via upload and test your new perception, your new model, and then basically provide feedback to the model. And it'll generate also an F1 score with it. Here, you can see the JSON response, so this is the raw JSON with the class title and the coordinates of what you're looking at, what it predicts.\r\n\r\nAnd here's the F1 score. This is an accuracy score. So the model generates automatically the accuracy of those particular classes. But that's not enough, because what you need to do is go back and check it as a human. And this is done manually, pre deployment usually, or after deployment sometimes it's done as well. And what you want to do is you want to be able to have an F1 score which is above 90%. And that's what this is about. You're able to download this and actually test many images of it.\r\n\r\nSo device deployment and camera management, this is also crucial. So let's say you're using a pre-trained model or pre-trained perception, or you've kind of trained your own thing. You want to deploy these onto the <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge<\/a> so that the inferencing is done on the edge.\r\n\r\nAnd here you have the device that you want to generate, so you go onto other devices, you create the device, this is office device, device for whatever office here, and it'll create the device, right? It'll have a device ID on it. And then what you want to do is you want to add cameras to it, right? So you have your Jetson line or your T4, and then you want to add a camera to it, or cameras, multiple cameras. You add the camera, name it, the RTSP feed as well, and then you select your perceptions that you want to put onto this device.\r\n\r\nSo let's say it's the hard hat one, or whatever, <a href=\"https:\/\/www.chooch.com\/blog\/ai-for-safety-fall-detection-with-computer-vision\/\">fall detection<\/a>. You add these to the device, and you could see that here, it's added to the device, and boom, it starts working. So what we're really doing here is training on the Cloud and then deploying the model onto the Edge, pre-trained as well, or you want to deploy something that you've trained, it doesn't really matter. But you're able to push it out onto the device.\r\n\r\nAnd the device actually syncs up with your Cloud account anytime it has connectivity. You can use it without connectivity, obviously, but you can use it with connectivity as well, and it'll sync automatically if you want it to sync automatically with the Cloud account, if you've trained, retrained, a model, or you've done something new, or just basic system updates that you might have.\r\n\r\nSo this is an example of masks and no masks.\r\n\r\nThis is an example of social distancing.\r\n\r\nSo you could put all these onto the Edge so that they work exclusively. So basically, what happens with the Edge is you don't stream anything to the Cloud and in doing so it works 24\/7 without any type of burden to the network. And it's also very, very expensive to do that on the Cloud.\r\n\r\nThis is people counting. This is <a href=\"https:\/\/www.chooch.com\/blog\/ai-for-safety-fall-detection-with-computer-vision\/\">fall detection<\/a>. These are examples of anything that you want to train, or you want to use anything that's pre-trained, you're able to do that. Fire detection.\r\n\r\nAnd you're able to select the pre-trained and deploy them immediately, if you want to use any of the pre-trained stuff. But you might have a use case where you want something specific and you would work with us so that that becomes trained. And we do the training very, very quickly, depending on the data that our clients provide.\r\n\r\nSo here you have many, many devices, and you can manage all these devices and all the models on them remotely, and it does the inferencing on those devices.\r\n\r\nSo, thank you. If you have any questions about <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a>, please out to us, hello@chooch.ai, and we look forward to keep working with with the ecosystem. Thank you.\r\n\r\nSo what's crucial here is to be able to deploy these models and perceptions on the Edge, on multiple devices and through multiple cameras. And that's what this is really about. And to be able to manage those at scale.\r\n\r\nSo to be able to do that, we built the system which is the dashboard where you have your models, and where you have your devices, and where you manage those devices and cameras. And then physically, you need to have these cameras hooked up to the devices, whether any of the Jetsons or on-prem, such as the T4s, and to be able to manage these and to be able to update these at one time. So to be able to train something new, deploy it on multiple devices, scale it, and also to be able to retrain it and to have them synced.\r\n\r\nSo AI is not about static models. It's about dynamic models, and also dynamic situations where you have these different devices out there with different types of camera angles, different types of cameras, and so forth. So be able to do this at scale and to be able to manage all of it, and that's what we've done as a company is to provide you with the Chooch <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\" target=\"_blank\" rel=\"noopener\">AI platform<\/a> in order to deploy these very, very quickly, you're able to do the Docker, download the Docker, set up in two and a half, three minutes, and then basically scale it out depending on what your use case might be.\r\n\r\nSo it's really important that we recognize what this is all about. This is all about efficiency, and to be able to do these at scale, and to be able to do it very quickly. And that's what we've done as a company.\r\n\r\nThank you for listening to this. This is all about the Edge, being able to do the inferencing on the Edge, being able to deploy these models, deploy these devices on the Edge, and to be able to provide that type of inferencing and that type of data. And we look forward to continue working with the ecosystem here.",
"post_title": "AI Model Deployments For Edge AI with the Chooch AI Platform",
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"post_date": "2023-01-18 09:51:18",
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"post_content": "Chooch AI is creating <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> that can be deployed quickly into the field with clear benefits. Ensuring that workers wear mandated safety equipment can lower insurance costs, increase productivity and save lives. Watch this video that demonstrates how detects when safety equipment isn't being worn.\r\n\r\nSeveral workers gloves are not wearing cloves, for example, and by sending an alert to a supervision, the workers can receive a message reminding them to keep their gloves on. Chooch AI can quickly and accurately detect these issues across multiple videos feeds. In fact, Chooch AI provides computer vision security\u00a0for many industries.\r\n\r\n<iframe src=\"https:\/\/www.youtube.com\/embed\/g9rnf54PDKE\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nThe ability to detect <a href=\"https:\/\/www.chooch.com\/blog\/how-to-detect-ppe-compliance-in-auto-parts-manufacturing-with-ai\/\">PPE compliance<\/a> has a lot of benefits especially reducing the risk of injury and lowering the cost of non-compliance. This means that Chooch AI brings immense value to its partners and customers. Chooch AI\u2019s PPE detection models are pre-trained and ready for deployment. Often, they can be deployed within days because of the pre-training.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">Personal Protective Equipment (PPE)<\/a> detection ensures worker safety by protecting them against health and safety risks. When workers don\u2019t wear PPE the risk of injury, contamination and financial losses due to fines go up.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> that are trained to ensure safety equipment compliance, reduce the risk of injury and lower the cost of non-compliance while ensuring environmental health and safety.\r\n\r\nChooch AI Models detect PPE compliance and <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">automate safety in the workplace<\/a> using the following method:\r\n<ol>\r\n \t<li>AI models are trained to detect PPE<\/li>\r\n \t<li>Afterward, AI models process video streams with computer vision.<\/li>\r\n \t<li>When a lack of <a href=\"https:\/\/www.chooch.com\/blog\/how-to-detect-ppe-compliance-in-auto-parts-manufacturing-with-ai\/\">PPE compliance<\/a> is detected, Chooch AI sends alerts and location data to stakeholders indicating that worker safety is at risk.<\/li>\r\n<\/ol>\r\nPPE detection and compliance can benefit different facilities such as\r\n<ul>\r\n \t<li>Warehouses, where there are safety risks such as falls and hard hats, are required.<\/li>\r\n \t<li>Factories where gloves, safety boots, safety goggles, and hairnets are required to avoid contamination or contact with hazardous material.<\/li>\r\n \t<li>Construction sites.<\/li>\r\n \t<li>Hospitals which require workers to wear gloves, masks, and gowns.<\/li>\r\n \t<li>Mining operations.<\/li>\r\n<\/ul>\r\nAfter an AI model has been deployed successfully, it also receives remote training from Chooch AI. And when a partner has special needs, a custom model can be also be deployed.\r\n\r\nLearn more about how AI models for PPE detection and compliance and about Computer Vision for Security. Or read our Environmental Health & Safety Compliance with <a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/solution-brief-chooch-readynow-models-ppe-detection.pdf\">Computer Vision Whitepaper<\/a>.",
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"post_date": "2023-01-18 09:50:49",
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"post_content": "Detecting unauthorized personnel is a crucial task for any business that needs to protect the safety of their employees and clients, or that stores valuable assets or data on-premises. Human security guards certainly have their uses, but they aren\u2019t without faults, either: they aren\u2019t available around the clock, they can only be present in a single location, and they\u2019re vulnerable to human error (just like the rest of us).\r\n\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">Computer vision<\/a> can help improve safety and security in the home and workplace by detecting unauthorized individuals. What\u2019s more, computer vision models can run 24\/7, with accuracy rates that rival or even surpass your human security personnel.\r\n\r\nDepending on the situation and use case, you may be able to deploy computer vision in multiple ways to protect the <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security of your premises<\/a>, people, and assets:\r\n<ul>\r\n \t<li><strong>Motion detection:<\/strong> In some cases, you may need to monitor remote or off-limits areas where no one should be present. A simple computer vision model for motion detection can help detect the presence of unauthorized individuals and send alerts to the appropriate authorities.<\/li>\r\n \t<li><strong>Vehicle identification:<\/strong> If unauthorized personnel are using vehicles to enter restricted areas, you can use <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> to automatically record the vehicle\u2019s license plate and identify its brand, model, and color.<\/li>\r\n \t<li><strong>Facial authentication:<\/strong> If only certain individuals should have access to a restricted area, you can use <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">facial authentication<\/a> to separate authorized from unauthorized personnel. Computer vision models for facial authentication have very high accuracy, run in a fraction of a second, and preserve user privacy by storing only mathematical hashes, rather than the images themselves.<\/li>\r\n<\/ul>\r\nOf course, this is all assuming that you have a robust system of security cameras and surveillance equipment that you can use as input to the computer vision model. The good news is that you can now run <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> models on <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge devices<\/a> that are physically located close to the data source itself, rather than having to upload images and video to the cloud. This enables you to get real-time results, keeping your business premises, people, and assets as secure as possible.",
"post_title": "How does computer vision help detect unauthorized personnel?",
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"post_date": "2023-01-18 09:50:44",
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"post_content": "Railway operators must conduct routine inspections and maintenance of tracks, trains, and other equipment to ensure the safe operation of railways. Through these inspection and maintenance activities, railway operators prevent service interruptions and, most importantly, reduce the chances of catastrophic <a href=\"https:\/\/safetydata.fra.dot.gov\/officeofsafety\/default.aspx\" target=\"_blank\" rel=\"noopener noreferrer\">railway accidents<\/a> by resolving some of the most common causes of accidents, such as train and equipment failures, track defects, and other issues.\r\n\r\nWhile trains require more maintenance than any other piece of railroad infrastructure, tracks are also highly prone to causing breakdowns and delays when neglected. Ultimately, the successful completion of detailed visual inspections of trains, tracks, and other equipment is the first line of defense against neglected maintenance issues leading to accidents.\r\n\r\nBeyond routine maintenance inspections and repairs, train conductors and engineers help prevent accidents by constantly watching for obstacles \u2013 such as vehicles, rocks, trees, livestock, and people \u2013 on the tracks ahead. If train drivers detect these obstacles early enough, there's a better chance of avoiding a disastrous accident.\r\n\r\nThe latest <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> offers railway operators tremendous ROI benefits in terms of earlier and more affordable <a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">detection of defects and obstacles<\/a>. In fact, computer vision offers dramatic efficiency improvements over traditional methods of defect and obstacle detection.\r\n<h3>Traditional Methods of Railway Defect and Obstacle Detection<\/h3>\r\nTraditional methods for detecting rail- and train-related flaws and maintenance issues include visual inspection, ultrasound, liquid penetration inspection (LPI), radiography, and more. Visual inspections are particularly costly and inconvenient to perform, as they require teams of highly trained technicians to walk along tracks and trains to look for problems in need of repair. Railways also use cameras to assist with the inspection process.\r\n\r\nDuring rail and train inspections, human technicians must visually evaluate the condition of rails, ties, track ballast, mounting systems, train wheels, train undercarriages, and other details. Human errors and oversights abound in this process, and it\u2019s not uncommon for inspectors to accidentally overlook a glaring maintenance concern that needs immediate repair to prevent train derailment or service shutdowns. As discussed in further detail below, railway operators incur massive costs when these defects go unnoticed.\r\n\r\nAs for obstacle detection, conductors and engineers must rely on their keen eyesight and focus. Unfortunately, it usually doesn't matter how early conductors can visually identify an obstacle. Trains typically cannot stop quickly enough to avoid a collision.\r\n<h3>The Challenges of Visual Defect and Obstacle Detection<\/h3>\r\nDespite spending millions of dollars each year to inspect North American railroads, railway inspection processes are fraught with problems and errors. This is mostly the result of:\r\n<ul>\r\n \t<li><strong>Staff shortages:<\/strong> Cost cuts and a lack of skilled inspectors mean that some railway operators may not have enough inspectors on hand to monitor all track assets with sufficient regularity and attention to detail.<\/li>\r\n \t<li><strong>Poor management decisions surrounding inspections:<\/strong> Railway industry managers are prone to making mistakes when it comes to balancing the limited resources they can direct toward track inspection and maintenance. This can result in the neglect of track and train assets that need more attention and care.<\/li>\r\n \t<li><strong>Inadequately performed inspections:<\/strong> Human railway inspectors are prone to missing details and making mistakes as a result of strict time constraints, distraction, fatigue, and the limitations of human capacity.<\/li>\r\n \t<li><strong>A lack of regular inspections:<\/strong> Some railway operators must divert their limited inspection resources to key pieces of infrastructure. This can lead to infrequent or inadequate inspections of less essential sections of track.<\/li>\r\n \t<li><strong>Human limitation:<\/strong> Train engineers are limited by how far ahead they can see. Plus, a curving track, trees, and buildings could obscure upcoming obstacles. This makes it difficult to detect livestock, people, and other obstacles early enough to stop the train to avert a collision. Train track suicides are also common. Tragically, many engineers remember the times they weren\u2019t able to stop the train in time to prevent someone from dying.<\/li>\r\n<\/ul>\r\n<h3>The Cost of Railway Inspection Errors and Train Accidents<\/h3>\r\nFailure to detect maintenance issues results in railroad operators finding out about problems too late \u2013 and the costs can be catastrophic. In these cases, an easily fixable defect can turn into a problem that's expensive to repair or results in a serious accident.\r\n\r\nAccording to <a href=\"https:\/\/safetydata.fra.dot.gov\/officeofsafety\/publicsite\/graphs.aspx\" target=\"_blank\" rel=\"noopener noreferrer\">the most recent data<\/a> from the U.S. Department of Transportation Federal Railroad Administration, human error, track failures, miscellaneous factors (such as collisions with obstacles, animals, and people), and equipment failures cause the majority of train accidents.\r\n\r\n<img class=\"alignnone wp-image-2627 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/train-accident-chart.png\" alt=\"Train Accident Chart\" width=\"885\" height=\"688\" \/>\r\n\r\nSome of the costs associated with failing to detect railroad defects and obstacles include:\r\n<ul>\r\n \t<li>Replacing and repairing train equipment and railroad tracks.<\/li>\r\n \t<li>Higher repair and maintenance costs.<\/li>\r\n \t<li>Personal injuries and wrongful death liabilities.<\/li>\r\n \t<li>Damage to goods and supplies the train was transporting.<\/li>\r\n \t<li>Lost customers and fewer sales from reputation damage and service delays.<\/li>\r\n \t<li>Environmental impact and cleanup of hazardous material spills.<\/li>\r\n \t<li>Psychological and emotional turmoil experienced by train drivers after witnessing a human death.<\/li>\r\n<\/ul>\r\n<h3>Leveraging Visual AI for Better Railway Defect and Obstacle Detection<\/h3>\r\nVisual AI technology can offer a cost-effective and highly efficient solution for <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-defect-detection\/\">detecting defects<\/a> and obstacles earlier, more accurately, and dramatically more affordably than railway operators can achieve with human inspectors and train engineers alone.\r\nComputer vision strategies for railroad defect and obstacle detection leverage the following features:\r\n<ul>\r\n \t<li>High-definition cameras mounted on the undercarriages, fronts, and sides of railway cars and along railway tracks.<\/li>\r\n \t<li>Servers running sophisticated <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> that interpret visual data.<\/li>\r\n \t<li>Infrared and high-definition cameras that scan railway tracks for obstacles like animals, rocks, trees, people, vehicles, and debris.<\/li>\r\n \t<li>Instant reports and alerts sent to decision-makers who can immediately trigger a repair request for further investigation.<\/li>\r\n \t<li>Instant reports and alerts sent to train conductors and engineers who can slow down or stop trains as early as possible to prevent collisions.<\/li>\r\n<\/ul>\r\nWith <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> computer vision technology, railway operators can rapidly train visual <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> to detect all types of visually perceivable defects and objects. In fact, Chooch AI can develop, train, and implement a custom visual AI strategy in only six to nine days. In this short amount of time, railway operators can start to realize the tremendous ROI benefits that come from faster, more accurate, and more affordable railway defect and obstacle detection.\r\n\r\nIn summary, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Chooch AI computer vision models<\/a> for the railroad industry can help railway operators:\r\n<ul>\r\n \t<li>Detect obstacles in the path of trains earlier than human engineers to provide additional time to prevent collisions and suicides.<\/li>\r\n \t<li>Visually detect maintenance issues on trains, wheels, and undercarriages.<\/li>\r\n \t<li>Visually identify track defects related to welds, cracks, and other maintenance issues.<\/li>\r\n \t<li>Evaluate the conditions of railway ties, track ballast, and mounting systems.<\/li>\r\n \t<li>Reduce train accidents and associated costs and damages.<\/li>\r\n \t<li>Reduce the cost of track, train, and equipment inspections.<\/li>\r\n \t<li>Reduce the cost of track, train, and equipment maintenance through earlier <a href=\"https:\/\/www.chooch.com\/blog\/manufacturing-computer-vision-for-defect-detection-and-more\/\">detection of defects and problems<\/a>.<\/li>\r\n<\/ul>",
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"post_date": "2023-01-18 09:49:31",
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"post_content": "Safety and security\u00a0must be paramount for any business that wants to protect its employees, customers, and assets from potential issues and malicious actors. Yet with more cameras and sensors than ever before, how can organizations lower their risk by quickly and efficiently analyzing the flood of images and videos at their fingertips?\r\n\r\nThe solution comes in the form of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI<\/a> for security video analytics. Below, we\u2019ll discuss computer vision and video analytics work together and why it\u2019s important to perform these analyses on the edge.\r\n<h2>Computer Vision for Security Video Analytics<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a>\u00a0is a subfield of artificial intelligence with the goal of making computers \u201csee\u201d like humans do. The domain of computer vision includes object recognition, facial recognition, event detection, motion tracking, and much more.\r\n\r\nWhile computer vision has been widely adopted in dozens of fields and industries,\u00a0safety and security\u00a0is one of the top use cases for computer vision. Here at Chooch, we\u2019ve helped many of our clients develop computer vision solutions for their safety and security AI needs. The list of examples includes:\r\n<ul>\r\n \t<li><strong>OSHA compliance and public safety:<\/strong>\u00a0Workplaces such as offices, restaurants, and construction sites all have their own set of health and public safety regulations that employees and customers must comply with. For example, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> systems can help determine whether workers are wearing protective clothing, such as hard hats or masks.<\/li>\r\n \t<li><strong>Authentication:<\/strong>\u00a0Certain restricted areas need to remain accessible to employees while remaining off-limits to the general public.\u00a0Facial identification systems with liveness detection, supported by computer vision, can help distinguish between legitimate and illegitimate access requests.<\/li>\r\n \t<li><strong>Remote sensing:<\/strong>\u00a0Many security cameras and sensors are located in remote areas, making it even more important to detect potential issues and risks. Computer vision can help identify suspicious vehicles, record license plates, detect breaches of a boundary or perimeter, and much more.<\/li>\r\n \t<li><strong>Object recognition:<\/strong>\u00a0Using cameras and thermographic sensors for infrared radiation, you can more easily identify noteworthy objects and people within just a fraction of a second, rapidly determining if they pose a security risk.<\/li>\r\n<\/ul>\r\n<h2>Security video analytics on the edge<\/h2>\r\nWhile computer vision has many possible applications, few of them are as time-sensitive as safety and security. When a <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision system<\/a> identifies a potential risk to your organization, you need to take swift and decisive action.\r\n\r\nThis means, of course, that time is of the essence when performing security video analytics. Unfortunately, many computer vision systems are too slow to perform real-time analysis: instead of processing the captured images or video themselves, they upload this data to a more powerful machine in the cloud. Latency issues (waiting for data to be uploaded and analyzed) present a barrier to large-scale adoption of <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">computer vision for safety and security AI<\/a>.\r\n\r\nThat\u2019s why\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">edge AI<\/a>\u00a0is an essential development for the field of security video analytics. Edge computing is a paradigm in which data processing occurs in \u201cedge\u201d devices that are physically located close to the original point of capture, rather than by servers in the cloud.\r\n\r\nWhile the field of edge AI is young, it\u2019s growing rapidly as more and more businesses come to realize the\u00a0benefits of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge computing<\/a>. For example, Accenture predicts that by 2025,\u00a0<a href=\"https:\/\/www.accenture.com\/_acnmedia\/pdf-94\/accenture-value-data-seeing-what-matters.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">70 percent of security surveillance cameras<\/a>\u00a0will come equipped with real-time monitoring and analytics capabilities, versus just 5 percent in 2018.\r\n\r\nSo how can you leverage the power of the edge for your own <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safety and security AI<\/a> needs? Chooch has developed an\u00a0edge AI platform\u00a0that can deploy up to 8 models with 8,000 different classes on a single edge device, letting you identify thousands of different types of objects. Powered by NVIDIA Jetson devices, Chooch\u2019s edge AI platform delivers results with greater than 90 percent accuracy within just a fraction of a second.\u00a0<a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" rel=\"noopener noreferrer\">Get in touch with us today<\/a>\u00a0for a chat about your business needs and objectives.\r\n\r\n ",
"post_title": "Edge AI: A Gamechanger for Video Analytics with Computer Vision",
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"post_date": "2023-01-18 09:48:13",
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"post_content": "Retail shrinkage is a multi-billion-dollar sore fundamental problem in the retail industry. According to the National Retail Federation, in 2019, the inventory loss due to shoplifting, employee theft, or other errors and fraud reached\u00a0$61.7 billion in the United States alone. To overcome the issue, retailers have implemented various <a href=\"https:\/\/www.chooch.com\/blog\/chooch-at-nrf-2023-lenovo-live-loss-prevention\/\">loss prevention<\/a> strategies and techniques, from electronic article surveillance, reporting systems, surveillance cameras, and plenty of policies to control the shrink.\r\n\r\nYet, they still fall victim to shrinkage, and most of these methods are reactive and tend to be inefficient, cost-wise.\r\n\r\nThe growing volumes of data have led organizations to use available data more effectively by developing systems to report, analyze, and predict shrink accurately. Thus, embracing advanced technologies such as artificial intelligence and <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI devices<\/a><a href=\"https:\/\/www.chooch.com\/blog\/\">.<\/a>\r\n<h2>Why should you consider integrating Edge AI in your Retail activity?<\/h2>\r\nRetail shrinkage can drastically impact retailers' profits and might even put them out of business as the risk gets high for businesses that already have low-profit margins. The higher it gets, the more it can impact organizations' ability to pay their employees and their business-related expenses, which eventually leads to poor customer service and experience.\r\n\r\nLoss prevention drives higher profits and more business growth for the retail industry. It is a prime priority for retailers to increase their profits and decrease losses, and <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">Retail AI Solutions<\/a>\u00a0are promising in retail loss prevention. These advanced technologies are using data patterns and insights to predict fraudulent activity in forms of shoplifting, internal theft, return fraud, vendor fraud, discount abuse, administrative errors, and so forth. Hence, providing a more proactive approach to reduce retail shrink and loss.\r\n\r\nRetailers are now shifting to AI-driven solutions that allow an extensive set of opportunities to improve the customer experience as well as enhancing retail security by preserving protection against fraudulent elements of inventory loss and delivering a more reliable shopping experience.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a>\u00a0solutions such as video analytics can run instantly and effectively respond to events and actions occurring at the store.\r\n<h2>How does Edge AI prevent Retail Loss?<\/h2>\r\nThere is a significant shift in how Edge AI approaches the <a href=\"https:\/\/www.chooch.com\/blog\/chooch-at-nrf-2023-lenovo-live-loss-prevention\/\">loss prevention<\/a> strategies from reactive to proactive, predictive prevention techniques. The process starts with collecting data from various sources, including security systems (camera, alarm records, etc.), video, payment data, store operation data, point of sales, crime data (local crime statistics), and supply chain data.\r\n\r\nThe data serves as a fundamental feed to leverage techniques such as computer vision, deep learning, behavioral analytics, predictive analytics, pattern recognition, image processing & recognition, machine learning, and correlation.\r\n\r\nIntegrating <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI in retail<\/a> loss prevention offers a set of proactive actions to stop retail loss, increase KPIs to prevent inventory loss, discount abuse, pilferage, shoplifting, theft, and return fraud and reduce shrinkage. Moreover, it shifts the strategy from \"Identifying a case\" to \"Preventing a case.\"\r\n<h2>Retail Edge AI strategies' examples:<\/h2>\r\n<h3>Video Analytics Systems<\/h3>\r\nVideo analytics powered with <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">artificial intelligence and machine learning<\/a> algorithms allow retailers to overcome the limitations of traditional video surveillance systems. Artificial intelligence makes video searchable, and actionable enabling its users to proactively investigate the retail loss and pinpoint persons susceptible to committing the retail crimes, as well as offering real-time monitoring and alerts system for suspicious behavior.\r\n<h3>Smart shelves<\/h3>\r\nSmart shelves are using technology to connect to the items they hold to monitor and secure these areas. Smart shelves are configured to provide real-time alerts and trigger calls to action for any abnormal activity detected. Beyond the <a href=\"https:\/\/www.chooch.com\/blog\/chooch-at-nrf-2023-lenovo-live-loss-prevention\/\">loss prevention benefits<\/a>, smart shelves enable retailers to track merchandise in real-time, giving insight into when to restock.\r\n<h3>RFID tags<\/h3>\r\nRFID-enabled smart tags attached to goods communicate with an electronic reader to track products. These devices can be removed at the checkout; if they're not removed, a security alarm is triggered when the customer tries to exit the store.\r\n<h3>Point of Sale Systems<\/h3>\r\nAn automated point of sale, or POS, is ideal for mitigating employee temptations to steal and help implement reliable inventory practices. Traditional systems are managed by employees. Still, failure to scan items is one of the primary ways employee theft occurs. Moreover, by not scanning a product at the checkout, employees are also discrediting inventory visibility.\r\n<h2>How Can Chooch AI help the retail industry to thrive and stop losses?<\/h2>\r\nThe future of retail loss prevention is AI-driven. When used accurately, artificial intelligence is able to limit retail loss and manage inventory to overcome shrinkage and impact the bottom line. Are you looking for a reliable partner to strengthen your shrinkage prevention strategy with AI?\r\n\r\nChooch AI offers complete <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Computer Vision Services<\/a>. It provides AI training and models for any visual data for enterprise deployments. Chooch AI is a fast, flexible, and accurate platform that can process visual data in any spectrum for many applications across many industries.\r\n\r\nThe\u00a0<a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch Visual AI platform<\/a>\u00a0offers a wide variety of brick and mortar\u00a0<a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">retail AI applications<\/a>.\u00a0From shelf space management to in-store health monitoring, from image optimization to analyzing consumer behavior, visual AI can improve consumers' shopping experience and revenues. The flexibility and efficiency of Chooch AI can deliver multiple impactful solutions to the <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">retail industry<\/a> on one platform.\r\n\r\n ",
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"post_date": "2023-01-18 09:45:46",
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"post_content": "Researching AI solutions? Recent technology breakthroughs have made edge AI a go-to method fro implementing computer vision.\u00a0 Need evidence? Market intelligence firm IDC has predicted that the number of edge AI processor shipments will soar to 1.5 billion in 2023, with a five-year annual growth rate of 65 percent. But what is <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI<\/a> exactly, and how do edge <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI platforms<\/a> work? Keep reading for all the answers.\r\n<h2>What is Edge AI? What are Edge AI Platforms?<\/h2>\r\nTo answer the question \u201c<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">What is edge AI?<\/a>\u201d, we first need to discuss the key concepts of IoT and edge computing:\r\n<ul>\r\n \t<li><strong>IoT (Internet of Things)<\/strong> is a massive network of interconnected devices that can communicate and exchange information via the Internet. These days, IoT systems can be found in everything from self-driving cars to the smart toaster in your kitchen that tells you today\u2019s weather.<\/li>\r\n \t<li><strong>Edge computing<\/strong>\u00a0is the practice of performing computation closer to the \u201cedge\u201d of the IoT network. Rather than uploading data to a remote server in the cloud for processing, edge computing seeks to do as much of this processing locally as possible, helping to reduce latency and cut costs.<\/li>\r\n<\/ul>\r\n<strong>Edge AI<\/strong>\u00a0is therefore the combination of edge computing and artificial intelligence: running AI algorithms on a local hardware device, without having to exchange data with remote servers.\r\n\r\nOne good example of an edge AI system is Apple\u2019s\u00a0<a href=\"https:\/\/www.macworld.com\/article\/230490\/face-id-iphone-x-faq.html\" target=\"_blank\" rel=\"noopener noreferrer\">iPhone facial recognition technology<\/a>, which uses a model of the owner\u2019s face to automatically unlock the device. According to Apple, this model remains on the iPhone at all times, and is never sent to the cloud. By restricting the computation to the user\u2019s device, facial recognition can continue to work even when the phone has no signal. Note that Chooch AI has a facial authentication solution.\r\n\r\nAn\u00a0<strong>edge AI platform<\/strong>\u00a0is a starter kit for rapidly prototyping and building systems that make use of edge AI. These platforms are generally purchased from third-party companies that have simplified the process of training, testing, deploying, and monitoring AI models. For example, the\u00a0<a href=\"\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener noreferrer\">Chooch Edge AI inference engine<\/a>\u00a0can deploy up to 8 models and 8,000 classes on a single edge ai device.\r\n<h2>What Are the Benefits of Edge AI Platforms?<\/h2>\r\nWithout an edge AI platform, businesses would have to build everything from scratch\u2014from the hardware itself to the AI algorithms that run on that hardware. Using an edge AI platform lets you get up and running much more quickly, innovating and iterating at the bleeding edge.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI platforms<\/a> also offer a great deal of stability and dependability. By choosing a solid, reliable third-party provider of edge AI platforms, organizations can outsource concerns such as support and maintenance, focusing on the applications of edge AI rather than the technical details of implementing it.\r\n\r\nThanks to their popularity, Edge AI platforms have been used across many different fields and industries, including:\r\n<ul>\r\n \t<li><strong><a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Healthcare AI<\/a>:\u00a0<\/strong>Edge AI systems have been successfully applied to multiple use cases in healthcare. For example, large DICOM images from MRIs and CT scans can be analyzed on a local machine, rather than incurring the cost of sending data to the cloud.<\/li>\r\n \t<li><strong>Safety & Security AI:\u00a0<\/strong>When time is of the essence, running facial recognition and image recognition systems on the edge can make all the difference. Businesses can use edge AI to enforce health and safety regulations in the workplace (e.g. detecting the absence of hard hats on a construction site).<\/li>\r\n \t<li><strong><a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Retail AI<\/a>:\u00a0<\/strong>Edge AI offers a wide range of possibilities for retail stores: analyzing customer behavioral patterns during the \u201cbuyer\u2019s journey,\u201d identifying products that need to be restocked, discovering recent purchasing trends, and much more.<\/li>\r\n<\/ul>\r\n<h2>The Essential Components of an Edge AI Platform<\/h2>\r\nThe essential components of a quality edge AI platform include:\r\n<ul>\r\n \t<li>A\u00a0<strong>camera or sensor\u00a0<\/strong>used to collect data that will be used as input to the AI algorithm.<\/li>\r\n \t<li>A\u00a0<strong>GPU (graphics processing unit)<\/strong>\u00a0used for computation. GPUs are essential for modern AI thanks to their massive parallelism, which makes them dramatically faster than CPUs.<\/li>\r\n \t<li>An<strong>\u00a0AI model\u00a0<\/strong>that takes in data and provides computation instructions to the GPU.<\/li>\r\n \t<li>An<strong>\u00a0analytics dashboard<\/strong>\u00a0to help users understand the performance of their algorithm over time.<\/li>\r\n<\/ul>\r\n<h2>Conclusion<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> platforms are powerful, robust solutions for bringing AI to a device near you, without having to offload processing to a remote cloud server. As we\u2019ve discussed, edge AI can run in nearly any location, with hundreds of possible use cases to explore.\r\n\r\nIf you\u2019re thinking about trying edge AI for yourself, check out\u00a0Chooch\u2019s Edge AI offerings, which can deliver results with more than 90 percent accuracy in just 0.2 seconds. We use industry-leading NVIDIA Jetson AI platforms that can easily integrate with your existing technical setup. What\u2019s more, the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> dashboard makes it easy to get up and running, from training AI models to extracting valuable real-time insights. Get in touch with our team today for a chat about your business needs and objectives.",
"post_title": "Edge AI Platform Essentials",
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"post_date": "2023-01-18 09:44:23",
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"post_content": "As artificial intelligence technologies continue to develop and advance with the advances in deep learning and more powerful GPUs, businesses are taking notice. But deciding to use enterprise AI solutions is just the tip of the iceberg\u2014in particular, you need to decide between a single-purpose <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI system<\/a> vs. an AI platform. In this article, we\u2019ll discuss why <a href=\"https:\/\/www.chooch.com\/platform\/\">AI platforms<\/a> are generally more flexible, expandable, and agile than alternatives such as a single-purpose AI system.\r\n\r\nAccording to a 2019 survey, <a href=\"https:\/\/martechseries.com\/analytics\/71-us-businesses-plan-use-ai-ml-2019\/\" target=\"_blank\" rel=\"noopener noreferrer\">71 percent of organizations<\/a> say that they plan to use more AI and machine learning in the near future, but we at Chooch AI believe that an AI platform is far preferable to a single-purpose AI system.\r\n\r\n<img class=\"size-full wp-image-1563\" src=\"\/wp-content\/uploads\/2023\/07\/enterprise-ai-platforms.png\" alt=\"Enterprise AI Platforms\" width=\"1200\" height=\"360\" \/>\r\n<h2>What is a single-purpose AI system?<\/h2>\r\nA single-purpose <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI system<\/a> is just what it sounds like: an AI system that has been built for a single use case. This system may have been built internally, or by a third-party team of AI experts.\r\n<h2>What is an AI platform?<\/h2>\r\nAn Enterprise AI Platform is a flexible, extensible framework. A platform makes it easier for businesses to develop solutions and applications using artificial intelligence. These platforms usually include assets such as AI algorithms, pre-trained models, datasets, and\/or simple visual interfaces. Many AI platforms have prebuilt workflows for highly common use cases such as facial recognition, object recognition, and recommender systems.\r\n\r\n<a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\">It's free to try to Chooch AI Platform.<\/a>\r\n<h2>AI platforms vs. single-purpose systems<\/h2>\r\nWhen you need a solution to a pressing business problem, developers\u2019 first thought is often building a single-purpose AI system. In the same vein, entrepreneurs often have a single motivating idea or application that compels them to launch a startup in the field of AI.\r\n\r\nIt\u2019s true that the domain expertise of a single-purpose <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI system<\/a> (e.g. visual inspection) or service (e.g. data labeling for autonomous driving) should inform your planning, training data, model, and deployment. However, if you ask the author of e.g. a facial recognition system to reapply the product to another domain, such as e.g. identifying and counting cells, it might be nearly as time-consuming as building a new AI system from scratch. This is the biggest problem with a single-purpose AI system: it can be extremely inflexible and unable to adapt to change as your organization grows and evolves.\r\n\r\nAn AI platform, on the other hand, is intended to be suitable for a wide variety of possible use cases. This means that such a platform has to be built to adapt, expand, and extend itself over time. For larger organizations, or for organizations who anticipate making changes in the future, enterprise AI platforms make far more sense.\r\n<h2>The functions of an AI platform<\/h2>\r\nIn order to be truly effective as a standalone entity, <a href=\"https:\/\/www.chooch.com\/platform\/\">AI platforms<\/a> need to wear many different hats. The various functions of a well-rounded AI platform are:\r\n<ul>\r\n \t<li><strong>Data collection: <\/strong>AI models need vast quantities of data in order to function at peak performance\u2014the more of it the better. Using an AI platform can help automate much of the data collection and organization process.<\/li>\r\n \t<li><strong>Annotation labeling:<\/strong> Most organizations use AI to perform \u201csupervised learning\u201d: learning from examples that are labeled (e.g. photographs of individuals). AI platforms can help create annotations and labels to prepare your dataset for training.<\/li>\r\n \t<li><strong>Algorithm and framework selection:<\/strong> Different AI algorithms and frameworks are better suited for different kinds of use cases. An AI platform can help advise you on the best approach to take for your situation.<\/li>\r\n \t<li><strong>Training:<\/strong> The AI training process can be long and complicated. <a href=\"https:\/\/www.chooch.com\/platform\/\">AI platforms<\/a> can provide guidance and advice on the best and most efficient way to proceed.<\/li>\r\n \t<li><strong>AI model generation:<\/strong> Even after training is complete, it can be tricky to take the generated model and start using it for real-world situations. Using an AI platform can help smooth over these bumps.<\/li>\r\n \t<li><strong>Testing:<\/strong> Before using an AI model in production, it absolutely needs to be tested on a fresh dataset that it hasn\u2019t seen before to assess its true accuracy.<\/li>\r\n \t<li><strong>Retraining:<\/strong> It\u2019s very rare that an AI model functions perfectly after just a single round of training. Rather, you need to experiment with and fine-tune the results by tweaking the model and the training hyperparameters.<\/li>\r\n \t<li><strong>Inferencing:<\/strong> Real-time inferencing is crucial for applications such as facial recognition and autonomous vehicles.<\/li>\r\n \t<li><strong>Deployment to cloud or edge:<\/strong> Finally, AI platforms can help you deploy the finished model to wherever is most convenient for you\u2014whether that\u2019s servers in the cloud or on an <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge ai device<\/a>.<\/li>\r\n<\/ul>\r\n<h2>Conclusion<\/h2>\r\nThe <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> platform has a variety of advantages\u2014most importantly, the ability to construct many possible solutions, giving you greater flexibility and agility. That\u2019s why Chooch has built its own AI platform that makes it easy for organizations of all sizes and industries to bring AI into their workflows.\r\n\r\nBusinesses use the Chooch AI platform across a wide range of AI Enterprise Solutions, whether it\u2019s for healthcare, safety and security, retail, or manufacturing. Want to try it out for yourself? Check out our Visual AI platform and get in touch to <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\">start your free trial.<\/a>",
"post_title": "Enterprise AI Platforms: More Flexible, More Expandable, More Agile",
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"post_content": "Early fire and <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">smoke detection<\/a> using AI for <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">safety and security<\/a> have massive benefits. The savings to life and property are much higher than the cost of deploying these models. Faster and more accurate AI-enabled fire detection can save lives and property which brings unparalleled value to Chooch AI customers and partners.\r\n\r\nEarly fire and smoke detection is crucial in controlling fires and preventing complete devastation. AI models can be trained to detect smoke and fire and also send alerts.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/mpw-oIvjB70\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe>\r\n\r\nHow do Chooch AI models detect fire and smoke visually?\r\n<ol>\r\n \t<li>AI training produces AI models that can \u2018see\u2019 fire and smoke.<\/li>\r\n \t<li>Next, AI models process video streams with computer vision.<\/li>\r\n \t<li>When fire or smoke is detected, Chooch\u00a0sends alerts with images and location data to first responders.<\/li>\r\n<\/ol>\r\nThese models act as smart smoke detectors. Early fire detection has huge benefits for:\r\n<ul>\r\n \t<li>Homes by catching fires early and preventing the loss of lives and property.<\/li>\r\n \t<li>In kitchens where there is a high chance\u00a0of fire.<\/li>\r\n \t<li>In industrial settings where hazardous or highly flammable materials can cause untold fire damage.<\/li>\r\n \t<li>In public spaces and buildings to avoid injury, loss of life and reduce damage. They can also support firefighting operations.<\/li>\r\n<\/ul>\r\nChooch AI\u2019s fire and <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">smoke detection<\/a> models are pre-trained and ready for deployment. These models can be deployed within days onto edge devices because of the pre-training.\r\n\r\nOnce a model has been\u00a0deployed successfully, <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a>\u00a0continues to train it remotely. Additionally, for partners with specific needs, custom models can be deployed.\r\n\r\nLearn more about how AI models can detect smoke and fire with <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a>.",
"post_title": "AI Fire Detection with Computer Vision",
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"post_date": "2023-01-18 09:41:34",
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"post_content": "The use of Digital Asset Management (DAM) software is vital for any organization managing large terabytes of visual media. Despite the usefulness of this highly sophisticated software, teams of human workers are still required to annotate, index, timestamp, and control the quality of assets like photographs and video. Not only is the process costly and time-consuming, but it\u2019s also fraught with errors and inadequacies. Now, media companies and retailers are achieving dramatic ROI benefits by adopting <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> strategies to fully automate these time-consuming annotation and indexing tasks.\r\n<h2>Why Is DAM Important?<\/h2>\r\nMany enterprises \u2014 especially media companies \u2014 are managing millions of images and billions of seconds of video. The more these companies understand media in their archives, the faster they can find \u201cXYZ,\u201d as soon as the need arises. Also, by implementing quality control standards, retailers can better align images and video with their marketing and branding standards.\r\n\r\n<img class=\"alignnone wp-image-3281 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/visual-ai-for-dam.jpg\" alt=\"Visual AI for Digital Asset Management (DAM)\" width=\"1200\" height=\"628\" \/>\r\n\r\nUnfortunately, most companies don\u2019t fully know what\u2019s available in their DAM archives and they can\u2019t control the quality of their visual data. This is not because they don\u2019t see the value of better DAM. It\u2019s because of the enormous cost, time \u2014 and in many cases impossibility \u2014 of employing human workers to accurately annotate and control the quality of their vast reserves of data\r\n<h2>Leveraging Visual AI for More Efficient Digital Media Management<\/h2>\r\nAn advanced <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision solution<\/a> can fully automate the process of analyzing and annotating both photographs and video through \u201csmart annotation.\u201d Even if the annotation process requires highly skilled and experienced professionals \u2014 such as an expert on celebrities or clothing styles \u2014 a Chooch AI strategy can achieve the job with greater accuracy, speed, and affordability.\r\n<h4>Case Study Example 1: A Television Station Needs to Annotate a Vast Media Archive<\/h4>\r\nAt Chooch AI, we develop agile, powerful, and highly affordable <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision strategies<\/a> for the widest range of use cases. Recently, one of our partners connected us with a television station managing a video reel archive consisting of millions of seconds of recorded content. Periodically, the station needs to locate a small clip of a celebrity for an advertising spot. For example, they might need to find some video of Emeril Lagasse inside their vast library of recorded video data to include in a 30-second preview of an upcoming show. Unfortunately, most of their video is completely un-indexed, so employees could spend a month scanning through content just to find the clip they need. Multiply this task by 100 for a large media company, and you can start to understand how time-consuming and expensive the process can be.\r\n\r\nWith a DAM computer vision strategy from <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a>, the television station implemented a ready-made visual <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI model<\/a> that recognizes over 400 celebrity faces. Using this system, the station was able to instantly index and timestamp all of their video archives according to where different celebrities were found \u2014 a process that would have taken a team of human workers years to complete.\r\n<h4>Case Study Example 2: An Online Used Car Retailer Needs to Implement Photograph Quality Control Standards<\/h4>\r\nIn another case, an online auto reseller contacted Chooch AI to develop a strategy that would enforce quality control standards on dealer-submitted images. The retailer found that dealer-submitted listings were more successful when they featured clear, well-framed photos with all three tires visible. They also found that distracting objects in the shots \u2014 like trash or half-empty water bottles \u2014 were damaging to sales. Despite clear guidelines for photo submission, the auto reseller dealers were submitting poor-quality photos, and they did not have enough human photo inspectors to implement strict quality control standards.\r\n\r\nWith a <a href=\"https:\/\/www.chooch.com\/\">computer vision system from Chooch AI<\/a>, the auto reseller was able to implement pre-trained visual <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> for quality control purposes. These models recognize problems related to trash, bottles, framing, blurriness, and brightness \u2014 automating the process of disqualifying images based on these criteria. This has dramatically boosted sales on the platform by improving the quality and effectiveness of images.\r\n<h2>Final Thoughts on Computer Vision for DAM<\/h2>\r\nAmong the efficiency and accuracy benefits of <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a>, one of the most impressive characteristics of the platform is speed of implementation. Digital media managers can quickly train <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a> to analyze and annotate visual media data based on nearly any criteria. In fact, Chooch AI users can design, develop, and deploy an entirely unique <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> strategy in just 6 to 9 days. In this short amount of time, you can start to reap tremendous ROI benefits by eliminating the endless hours of monotonous and expensive human labor required to index and find the clips and photographs you need.",
"post_title": "Visual AI for Digital Asset Management (DAM)",
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"post_date": "2023-01-18 09:39:40",
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"post_content": "As computer vision is becoming increasingly sophisticated, it brings business benefits to a wide variety of industries. From <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-defect-detection\/\">defect detection<\/a> to loss prevention, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is a powerful tool with the potential to improve processes and results in many contexts. But before we dive into the use cases of computer vision, let's define what it is.\r\n<h2>What are computer vision solutions?<\/h2>\r\nComputer vision is often confused with <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Artificial Intelligence (AI)<\/a>, but it is not quite the same thing. Computer vision is the ability of computers to \"see\", analyze and understand images or videos. AI, on the other hand, is when computers perform tasks that usually would require human intelligence. Simply put, computer vision is the \"human eyes\" of the computer, while AI is the \"human brain.\" One could also say that <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is AI applied to the visual world.\r\n\r\nVisual data that computer vision can process include images and videos captured by cameras, 3D scanners, and medical scanners. Today, computer vision is sophisticated enough to\u00a0<a href=\"https:\/\/arxiv.org\/pdf\/1502.01852.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">outperform the human eye.<\/a>\r\n\r\nIn this article, we'll look at the business applications of computer vision solutions and how they can improve safety and efficiency \u2013 while lowering costs \u2013 in 6 different industries.\r\n<h2>1. Retail<\/h2>\r\nComputer vision optimizes processes and improves the customer experience, both in brick and mortar stores and online.\r\n\r\n<strong>Improved operations and reduced costs:<\/strong>\u00a0<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> allows retailers to speed up processes such as inventory management, payments, and compliance. Automated\u00a0planogram analysis\u00a0can save time, reduce space wastage and suggest the most optimal shelf placement for each product.\r\n\r\n<strong>Increased security and reduced shrinkage:<\/strong>\u00a0Shoplifting and employee theft are costing retailers a staggering<a href=\"https:\/\/www.retaildive.com\/news\/shrink-cost-retailers-100b-last-year\/524460\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0$100 billion<\/a>\u00a0globally every year. Computer vision can spot suspicious activities and provide heat maps of shoppers moving around the store \u2013 real-time data that helps ensure health and safety.\r\n\r\n<strong>Improved customer experience:\u00a0<\/strong>Computer vision can improve in-store marketing in several ways. Facial recognition enables retailers to identify regular customers and provide personalized service. Coupons and offers can be personalized, and products can be suggested based on previous purchases.\r\n\r\nLearn more about Chooch's <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Retail AI\u00a0solutions<\/a>.\r\n<h2>2. Healthcare<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> helps medical professionals save time and optimize workflows. A growing number of doctors are using it to diagnose their patients and prescribe the right treatments.\r\n\r\n<strong>Increased patient safety:<\/strong>\u00a0Computer vision improves diagnostic accuracy, reducing the number of unnecessary procedures and expensive therapies. It can detect illnesses that can otherwise be difficult to identify, such as\u00a0<a href=\"https:\/\/www.mountsinai.org\/about\/newsroom\/2018\/artificial-intelligence-platform-screens-for-acute-neurological-illnesses-at-mount-sinai\" target=\"_blank\" rel=\"noopener noreferrer\">neurological illnesses<\/a>. Increased accuracy and quicker diagnosis mean lower costs, both for the patient and the care provider.\u00a0Facial authentication\u00a0prevents misidentification of patients and increases the security of medical facilities.\r\n\r\n<strong>Increased operational efficiency:<\/strong>\u00a0Doctors traditionally spend a lot of their time analyzing images and reports. Computer vision frees up their time so that they can spend it with patients instead. This means quicker care\u00a0<a href=\"https:\/\/codete.com\/blog\/computer-vision-healthcare\" target=\"_blank\" rel=\"noopener noreferrer\">at a lower cost<\/a>. Computer vision can also be used to monitor operating room procedures and provide automatic surgical logs. When activities such as anesthetization, chest closure, and instrument usage are logged, it reduces errors and increases efficiency.\r\n\r\n<strong>Medical imaging and measuring blood loss:\u00a0<\/strong>Computer vision is used in medical imaging, where a recent example is the detection of\u00a0<a href=\"https:\/\/www.winniepalmerhospital.com\/content-hub\/winnie-palmer-hospital-launches-invests-in-new-ai-technology-to\" target=\"_blank\" rel=\"noopener noreferrer\">COVID-19 in lung X-ray images with 98% accuracy<\/a>. The\u00a0<a href=\"https:\/\/www.winniepalmerhospital.com\/content-hub\/winnie-palmer-hospital-launches-invests-in-new-ai-technology-to\" target=\"_blank\" rel=\"noopener noreferrer\">Orlando Health Winnie Palmer Hospital for Women and Babies<\/a><a href=\"https:\/\/www.winniepalmerhospital.com\/content-hub\/winnie-palmer-hospital-launches-invests-in-new-ai-technology-to\" target=\"_blank\" rel=\"noopener noreferrer\">\u00a0<\/a>uses computer vision to measure blood loss during childbirth. Before, it was impossible to know how much blood a mother was loosing. But with computer vision, images of surgical sponges and suction canisters can be used to measure blood loss.\r\n\r\nContact us about <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare AI\u00a0<\/a>\r\n<h2>3. Manufacturing<\/h2>\r\nIn the industrial sector, human error can cause dangerous situations and expensive mistakes. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> efficiently improves the accuracy, quality, and speed of industrial operations.\r\n\r\n<strong>Defect Detection:<\/strong> Using computer vision for flaw detection in manufacturing. With fast inference speeds using edge AI and flexible training, computer vision can be applied across many manufacturing processes.\r\n\r\n<strong>Increased security in remote locations:<\/strong>\u00a0Unmanned and remote locations such as oil wells can be monitored with computer vision.\u00a0Facial authentication\u00a0confirms the personnel's identity to ensure only the right people have access to restricted areas.\r\n\r\n<strong>Improved workplace safety:\u00a0<\/strong><a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> can detect required gear in images and video, to ensure that employees wear protective gloves and hard hats. This reduces the risk of workplace injuries and legal costs while improving employee safety.\r\n\r\n<strong>Predictive maintenance:<\/strong>\u00a0Computer vision is increasingly employed to monitor the status and health of\u00a0<a href=\"https:\/\/algorithmxlab.com\/blog\/computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">critical infrastructure<\/a>. If a plant or tool fails, it can lead to costly delays. Computer vision enables\u00a0<a href=\"https:\/\/blog.vsoftconsulting.com\/blog\/ais-role-in-oil-and-gas-industry\" target=\"_blank\" rel=\"noopener noreferrer\">early discovery and preventive measures<\/a>\u00a0\u2013 saving time and money.\r\n\r\nLearn more about Chooch's <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">AI Vision solutions for manufacturers<\/a>.\r\n<h2>4. Safety and security<\/h2>\r\nHaving cameras monitoring <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">high-security facilities<\/a> is not enough; if there is no way to analyze the imagery, it is of no use. Computer vision solves this, enabling advanced analysis of the data from surveillance technology.\r\n\r\n<strong>Facial authentication protects restricted areas:<\/strong>\u00a0Adding computer vision and biometrics replacing passwords, badges, PINs prevents the wrong people from getting access to high-security facilities and classified information.\r\n\r\n<strong>Compliance with regulations:<\/strong>\u00a0Visual AI can monitor if workers wear protective clothing and set off alerts when non-compliance is detected. This means lower insurance, reduced costs, and less risk.\r\n\r\n<strong>Virus mitigation:\u00a0<\/strong>The risk of spreading infectious diseases in public spaces can be reduced with <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> that detects coughing, mask-wearing, and even fevers in the era of Covid-19\r\n<h2>5. Digital media and entertainment<\/h2>\r\nMedia industry players such as publishers, advertisers, and brands are increasingly using computer vision to improve their businesses.\r\n\r\n<strong>Automated image enrichment:<\/strong>\u00a0Adding metadata to images, and tagging new photos, reduces the need for manual labor. Cloud deployment of AI to identify people and objects and deep tagging also saves time while enhancing inventory value.\r\n\r\n<strong>Quality control and compliance with regulations:<\/strong>\u00a0On social media platforms and other websites, computer vision helps with image analysis and quality control. It alerts editors about blurred images, nudity, deep fakes, and banned content.\r\n\r\n<strong>Customized advertising:\u00a0<\/strong>Vendors can benefit from contextual ad placement, and images and objects in videos can be tagged to increase searchability.\r\n<h2>6. Geospatial AI<\/h2>\r\nThe amount of imagery derived from satellites and UAVs is growing at an explosive rate. From agriculture, urban planning, and disaster relief to insurance, conservation and earth science, c<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">omputer vision<\/a> provides the means to analyze and use the imagery.\r\n\r\n<strong>Increased public safety with change detection:<\/strong>\u00a0Computer vision trained to identify wildfires, water level changes, and industrial activity with high accuracy increases public safety. Alerts can be sent to decision-makers, enabling efficient and timely action.\r\n\r\n<strong>Efficient earth observation:<\/strong>\u00a0Computer vision allows for the analysis of electro-optical, infrared, synthetic aperture radar, from any source. Deep-learning computer vision can monitor everything from urban development and climate change to population dynamics and agriculture,\u00a0helping us understand\u00a0the world we live in.\r\n\r\n<strong>Improved environmental epidemiology:<\/strong>\u00a0As COVID 19 continues to affect every aspect of our lives, the study of\u00a0<a href=\"https:\/\/ehjournal.biomedcentral.com\/articles\/10.1186\/s12940-018-0386-x\" target=\"_blank\" rel=\"noopener noreferrer\">environmental and exposure factors in epidemics<\/a>\u00a0has never been more top-of-mind. Computer vision provides the tools for collecting and processing a wide range of data points relevant in research and prevention work.\r\n\r\nContact us about <a href=\"https:\/\/www.chooch.com\/solutions\/geospatial-ai-vision\/\">Geospatial AI\u00a0<\/a>\r\n<h2>Conclusion<\/h2>\r\nIn a world where the amount of visual data is exploding, computer vision has a wide range of use cases. The technology improves operations efficiency while increasing safety and revenue, helping businesses collect, analyze, and use the images and video unprecedentedly.\r\n\r\nChooch offers <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">AI Vision technology<\/a> solutions that can be customized for your specific business needs. If you would like more information on how we can help, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">contact us.<\/a>",
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"post_date": "2023-01-18 09:33:23",
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"post_content": "Far from making <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">edge AI<\/a> less critical, 5G can actually complement edge AI, with both technologies working in concert to digitally transform your business.\r\n\r\nIt\u2019s understandable why you might think that 5G removes (or at least reduces) the need for edge AI. After all, one of the motivating concerns for edge AI is the latency and slowdown when uploading data to remote cloud servers. Meanwhile, 5G is expected to be significantly faster than 4G. Estimates vary, but analysts predict that 5G could be as much as <a href=\"https:\/\/www.highspeedinternet.com\/resources\/4g-vs-5g\">10 times faster<\/a> than 4G in some locations. So if we use 5G to upload AI data, that means we don\u2019t have to worry about upload speeds anymore\u2014right?\r\n\r\n<img class=\"alignnone wp-image-2955\" src=\"\/wp-content\/uploads\/2023\/07\/learn-computer-vision-edge-ai.jpg\" alt=\"Learn Computer Vision Edge AI\" width=\"745\" height=\"390\" \/>\r\n\r\nWell, not quite. For one, 5G won\u2019t always deliver blazing-fast speeds. According to the article linked above, 5G sees the most performance improvements over 4G in densely populated areas. In more remote locations, however, 5G isn\u2019t much better than 4G\u2014barely twice as fast.\r\n\r\nIn addition, latency is only one reason why businesses are choosing edge AI. Some prefer edge AI because it keeps data on the local device, preserving security and privacy. Another motivation for <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI<\/a> is decreased costs of data transmission, and the move from 4G to 5G won\u2019t affect the amount of data that needs to be sent to the cloud.\r\n\r\nSo if 5G won\u2019t obviate the need for edge AI, what is the role of 5G in edge computing? Instead of sending data to remote servers, 5G enables you to send data to edge devices more quickly than with 4G.\r\n\r\nIn fact, according to Mark Gilmour, global head of 5G at Colt Technology Services, it\u2019s 5G that will depend on edge computing, not the other way around. More specifically, 5G will have to rely on the edge in order to justify its continued rollout; the two technologies act as \u201cforce multipliers\u201d for each other.\r\n\r\n<a href=\"https:\/\/www.rcrwireless.com\/20201211\/opinion\/readerforum\/5g-and-edge-computing-why-these-complementary-technologies-will-optimize-enterprise-businesses-reader-forum\" target=\"_blank\" rel=\"noopener noreferrer\">Gilmour writes:<\/a> \u201cIf we want to step into the full development of the 5G era, then edge computing is imperative and is a critical succeed\/fail factor, not just a standalone technology, bandwidth booster or a \u2018nice to have.\u2019 Specifically, edge really comes into play when leveraging factors like low latency and high-performance data processing over a cellular connection. You can have edge without 5G, but for these new 5G use cases, you can\u2019t have 5G without edge.\u201d",
"post_title": "Does 5G make edge AI less critical?",
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"post_content": "Certain employees will continually fail to use the <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE equipment<\/a> and managers may never even notice. Tragically, this results in worsened injuries when an accident occurs. It also causes a host of accident-related costs and operational inefficiencies for businesses. Now, the latest <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> is making PPE enforcement easier than ever -- allowing industrial businesses to achieve near-100% PPE compliance while reducing the number and severity of accidents at industrial facilities.\r\n<h2>Why PPE Compliance Is Critical<\/h2>\r\nAccording to statistics from the <a href=\"https:\/\/www.ilo.org\/moscow\/areas-of-work\/occupational-safety-and-health\/WCMS_249278\/lang--en\/index.htm\" target=\"_blank\" rel=\"noopener noreferrer\">International Labor Organization (ILO)<\/a>, there are approximately 340 million work-related accidents globally each year, and 160 million people suffer from work-related illness. Of these, approximately 2.3 million people die of their injuries and illnesses.\r\n\r\nWith specific regard to <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace accidents<\/a>, the <a href=\"https:\/\/www.nyu.edu\/content\/dam\/nyu\/environmentalHealthSafety\/documents\/PPE_Packet_FY06.PDF\" target=\"_blank\" rel=\"noopener noreferrer\">NYU protective equipment (PPE) standard<\/a> reports that the vast majority of injuries could have been prevented with the use of appropriate PPE. For example, 70.9% of hand and arm injuries could have been prevented by using safety gloves, and 99% of face injury victims were not wearing facial protection.\r\n<h2>Costs and Consequences of Inadequate PPE Enforcement<\/h2>\r\nAccidents and injuries that result from inadequate PPE enforcement cause the following costs for industrial businesses:\r\n<ul>\r\n \t<li>Additional accidents and more severe injuries when accidents occur.<\/li>\r\n \t<li>Greater dissatisfaction among workers due to a workplace culture that doesn\u2019t emphasize worker safety.<\/li>\r\n \t<li>Reduced productivity due to job site shutdowns and labor shortages following an injurious accident.<\/li>\r\n \t<li>Higher insurance premiums following serious <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace accidents<\/a> and OSHA violations.<\/li>\r\n \t<li>Increased litigation costs due to wrongful death and personal injury lawsuits, lawyer fees, settlements, and other liabilities.<\/li>\r\n \t<li>More safety citations and fees due to regulator inspections and safety infractions following injurious workplace accidents.<\/li>\r\n<\/ul>\r\n<h2>Leveraging Computer Vision for Better PPE Compliance<\/h2>\r\nThe latest <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> can monitor PPE use across every inch of a property to help companies achieve near-100% <a href=\"https:\/\/www.chooch.com\/blog\/how-to-detect-ppe-compliance-in-auto-parts-manufacturing-with-ai\/\">PPE compliance<\/a>. Most importantly, these visual AI systems radically reduce work-related injuries and their associated costs and consequences. By using a network of strategically placed, AI-equipped, high-definition cameras, industrial facilities can detect evidence of PPE non-compliance, and send a manager or foreman to educate and remind the employee on appropriate PPE use.\r\n\r\nThe Chooch AI visual AI platform includes pre-made computer vision models\u2014ready for immediate implementation\u2014that detect PPE compliance failures related to:\r\n<ul>\r\n \t<li>Hard hats<\/li>\r\n \t<li>Gloves<\/li>\r\n \t<li>Vests<\/li>\r\n \t<li>Eye protection<\/li>\r\n \t<li>Aprons<\/li>\r\n \t<li>Face shields<\/li>\r\n \t<li>Harnesses<\/li>\r\n \t<li>Virtually any type of PPE<\/li>\r\n<\/ul>\r\nWith Chooch AI, businesses can also train new visual AI models for unique PPE enforcement use cases.\r\n\r\nBeyond PPE enforcement alone, computer vision for PPE compliance provides a visual record of employees using required PPE. This data may be important in the event of a safety inspection or lawsuit following a serious workplace accident.\r\n<h2>Case Study: Theft and Safety Glove Inspections<\/h2>\r\nRecently, Chooch AI developed a computer vision solution for a nationwide automobile disassembly enterprise with over 400 industrial facilities throughout the United States. Hand-related accidents were the biggest source of workers\u2019 compensation claims for the auto disassembler. Most of these injuries involved cuts, lacerations, and broken bones\u2014and the vast majority could have been avoided by using safety gloves.\r\n\r\nThe auto disassembler was experiencing the following costs and consequences related to hand injuries:\r\n<ul>\r\n \t<li>Absenteeism and staffing shortages: Employees with hand injuries typically needed to miss 1 to 4 weeks of work.<\/li>\r\n \t<li>Financial losses: A typical hand injury claim could cost the company between $2,000 and $10,000.<\/li>\r\n \t<li>A culture of PPE non-compliance: The inability to enforce safety glove use had solidified into a culture of PPE non-compliance that was difficult to break.<\/li>\r\n<\/ul>\r\nChooch AI helped the auto disassembler train and implement a new AI model for PPE safety glove detection. With a simple update to their existing visual AI system for theft detection, Chooch uploaded a patch for safety glove detection across the company\u2019s entire network of 400-plus facilities.\r\n\r\nArmed with the \u201ceyes-on-the-backs-of-their-heads\u201d solution they were looking for, the auto disassembler started to receive instant alerts of PPE non-compliance pertaining to safety gloves. This allowed managers and foremen to provide the appropriate safety training and coaching to employees.\r\n<h2>Chooch AI: Computer Vision for PPE Compliance<\/h2>\r\nChooch AI offers enterprises the flexibility to deploy visual AI models for nearly any <a href=\"https:\/\/www.chooch.com\/blog\/how-to-detect-ppe-compliance-in-auto-parts-manufacturing-with-ai\/\">PPE compliance<\/a> challenge. With its vast library of pre-built computer vision models, <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> can identify employees that fail to wear gloves, hard hats, aprons, safety glasses, harnesses, and more. Best of all, Chooch AI is so fast and easy to use that businesses can train and deploy unique AI models in just 6 to 9 days.\r\n<ul>\r\n \t<li>Faster response times: By simultaneously monitoring hundreds of surveillance camera feeds covering every inch of a property, computer vision offers real-time alerts for faster response times as soon as a problem occurs.<\/li>\r\n \t<li>Dramatically less expensive and more efficient: Working in conjunction with human security personnel, Chooch AI empowers security teams to boost the speed, accuracy, coverage, and cost-efficiency of security-related tasks.<\/li>\r\n<\/ul>\r\nBy partnering with a robotics provider, Chooch AI developed a computer vision strategy to augment the logistic company\u2019s security coverage while reducing their staffing, training, and management burdens. By seamlessly integrating <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> with drones and robotic \u201cdogs,\u201d the logistics company achieved more accurate monitoring for fence breaches, unauthorized personnel, and a host of premises-related security concerns \u2013 without the use of additional human capital and without incurring additional security expenditures.",
"post_title": "Computer Vision for PPE Compliance at Industrial Facilities",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Retail industry<\/a> businesses face the constant threat of theft-related inventory shrinkage, but traditional retail theft prevention methods leave a lot to be desired. In fact, despite the best efforts of retailers, <a href=\"https:\/\/bluewatercredit.com\/five-finger-discount-35-facts-shoplifting-america\/\" target=\"_blank\" rel=\"noopener noreferrer\">one study<\/a> indicates that 33% of retail shrinkage is from shoplifting and 33.1% is from employee theft. Now, some retailers are adopting computer vision technology for <a href=\"https:\/\/www.chooch.com\/blog\/loss-prevention-retail-ai-can-make-dramatic-improvements-with-edge-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">retail loss prevention<\/a>. By leveraging AI-enabled cameras and sophisticated computer vision algorithms, retailers are reducing theft-related inventory shrinkage like never before.\r\n<h2>The challenges of traditional retail theft control strategies<\/h2>\r\nIt\u2019s important for retailers to use traditional theft control strategies -- like human security personnel, security tags, and POS software -- but these strategies don\u2019t prevent all instances of theft. In fact, shoplifting results in <a href=\"https:\/\/www.forbes.com\/sites\/tjmccue\/2019\/01\/31\/inventory-shrink-cost-the-us-retail-industry-46-8-billion\/?sh=7fa4b1e16b70\" target=\"_blank\" rel=\"noopener noreferrer\">over $15 billion<\/a> in U.S. retail losses each year, and this doesn\u2019t account for the enormous costs of employee theft.\r\n\r\nDespite the use of common theft control strategies, retailers still face the following challenges:\r\n<ul>\r\n \t<li><strong>Stockrooms and supply rooms are vulnerable:<\/strong> It\u2019s difficult for retailers to track what\u2019s happening in restricted areas like stockrooms, offices, and breakrooms. Dishonest customers, sales employees, and cleaning staff sneak into these unattended areas and steal items without being noticed.<\/li>\r\n \t<li><strong>Employees are often seasonal:<\/strong> Most retailers perform background security checks on new employees. However, numerous retail employees are temporary and seasonal, making it difficult to know which ones can be trusted not to steal.<\/li>\r\n \t<li><strong>The dangers of \u201csweethearting\u201d:<\/strong> Sweethearting happens when a cashier employee adds additional discounts or purposefully doesn\u2019t scan or charge customers for items.<\/li>\r\n \t<li><strong>Organized crime:<\/strong> Instances of organized shoplifting crimes are growing more common. In these instances, a number of shoplifters will into the store and overwhelm the staff by asking for help with different products. While staff members are engaged, the other shoplifters will steal items without being noticed.<\/li>\r\n \t<li><strong>Backdoor theft and trash can theft:<\/strong> Employees may steal items by placing them in boxes and taking them out of the backdoor of stores. They could also throw the items they want to steal in the trash and recoup them at a later time.<\/li>\r\n<\/ul>\r\n<h2>Leveraging computer vision to stop retail theft<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Computer vision systems for retail<\/a> theft prevention use a network of AI-enabled cameras, machine learning models, and advanced analytics to detect instances of theft and immediately notify managers to investigate. These solutions operate throughout the day, never get distracted, and monitor all store areas simultaneously. They can run in the cloud for easy scalability or on edge servers for faster processing and maximum data security.\r\n\r\nChooch\u00a0computer vision systems for <a href=\"https:\/\/www.chooch.com\/blog\/loss-prevention-retail-ai-can-make-dramatic-improvements-with-edge-ai\/\">retail loss prevention<\/a> can provide the following features and more:\r\n<ul>\r\n \t<li><strong>Tracking product locations and customer behavior:<\/strong> Computer vision can detect when a customer is about to leave the store without paying for an item. These systems can also trigger alerts to security personnel when a product disappears into a customer\u2019s pocket or bag.<\/li>\r\n \t<li><strong>Tracking products and boxes in supply rooms:<\/strong> <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> can monitor and track the status and locations of boxes and products in supply rooms for better theft prevention and organization.<\/li>\r\n \t<li><strong>Tracking for organized shoplifting:<\/strong> Visual AI can watch for the signs of an organized shoplifting attack by tracking the number of customers in a store and their behaviors at all times.<\/li>\r\n \t<li><strong>Detection of known shoplifters and criminals:<\/strong> <a href=\"https:\/\/www.chooch.com\/\">Visual AI<\/a> can immediately detect the faces of known shoplifters and criminals as soon as they walk into a retail store.<\/li>\r\n \t<li><strong>Access control for restricted areas:<\/strong> Facial detection can control access to restricted areas like supply rooms. These systems use <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition technology<\/a> that ensures only authorized employees enter restricted areas.<\/li>\r\n \t<li><strong>Better checkout security:<\/strong> Computer vision technology can monitor cash registers to ensure that human cashiers charge customers appropriately for all items. This technology can also detect when customers try to steal items at self-checkout stations.<\/li>\r\n \t<li><strong>Backdoor alerts:<\/strong> AI Vision models can immediately notify managers whenever an employee walks out the backdoor with items or boxes.<\/li>\r\n<\/ul>\r\nIn addition to these advantages, <a href=\"https:\/\/www.chooch.com\/platform\/\">Chooch AI Vision platform<\/a> delivers solutions for analyzing customer behavior and demographics and <a href=\"https:\/\/www.chooch.com\/blog\/artificial-intelligence-is-transforming-retail-shelf-management\/\">tracking and monitoring shelf space<\/a> for out-of-stock items.",
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"post_date": "2023-01-18 09:25:52",
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"post_content": "Traditionally, companies depend on human security personnel to safeguard their premises and assets. Thanks to computer vision security and drone AI, companies can improve and supplement the services that human security personnel provide.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/1C7YwDzlElM\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener\">Computer vision<\/a> solves the challenges that come with human security personnel such as error and lack of round-the-clock availability. <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> customers enjoy the following benefits:\r\n<ul>\r\n \t<li>The ability to monitor feeds from numerous cameras, drones, and sensors at the same time.<\/li>\r\n \t<li>Biometrics such as fingerprints and <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial authentication<\/a> with liveness detection for access control.<\/li>\r\n \t<li>Vehicle identification.<\/li>\r\n<\/ul>\r\nAI security works by:\r\n<ul>\r\n \t<li>Training computer vision models to identify security threats, vehicles, and biometric data.<\/li>\r\n \t<li>Running video feed from security cameras and drone computer vision on edge devices that identify objects, actions, vehicles, people and so on.<\/li>\r\n \t<li>Sending an alert to decision makers if the model identifies a security breach.<\/li>\r\n<\/ul>\r\nOrganizations can protect their assets efficiently and cost-effectively using computer vision for security. Chooch AI has pre-trained <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener\">artificial intelligence models<\/a> that we can deploy immediately for your security needs. If an organization has special use-cases, we can train custom models with the same fast deployment. Let\u2019s discuss your computer vision security project.",
"post_title": "Computer Vision Security: Robotics and Drone AI for the Security Industry",
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"post_date": "2023-01-18 09:21:57",
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"post_content": "Chooch AI\u2019s <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a> can monitor every aspect of a retail operation, be it a minimart or a restaurant \u2014 including all front of house customer touchpoints and back of house locations from kitchen to food preparation and storage areas, as well as employee break rooms and office space.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/4bu-lGBGPxI\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\" target=\"_blank\" rel=\"noopener\">Retail computer vision<\/a> can be in places that a human manager or supervisor can\u2019t easily go, and it can see and analyze all of these locations simultaneously. It would be impossible for human managers or supervisors to detect adherence to proper masking and social distancing guidelines in every location of a restaurant at the same time. Chooch\u2019s <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision AI platform<\/a> can do all of this \u2014 and more \u2014 in real-time.\r\n\r\nChooch AI\u2019s superior <a href=\"https:\/\/www.chooch.com\/blog\/whats-the-difference-between-object-recognition-and-image-recognition\/\">image recognition<\/a> can detect face masks and differentiate between masked and unmasked faces through many simultaneous inputs. This helps keep your guests and staff healthy \u2014 and your business compliant with <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">health guidelines<\/a> or regulations.\r\n\r\nPrevent potentially costly lapses in regulatory compliance by deploying Chooch AI\u2019s powerful, pre-trained mask detection abilities. The use of a pre-trained computer vision <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener\">AI model<\/a> means these systems can be deployed within days to help you keep your guests and staff safe. As health guidelines and requirements change, the <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision AI<\/a> can receive updated AI training remotely and computer vision consulting from Chooch AI, resulting in no downtime and always-on monitoring. Computer vision for security may also be a topic of interest.\r\n\r\nLearn more about <a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\" target=\"_blank\" rel=\"noopener\">computer vision in retail<\/a> specifically, keeping employees and customers safe while reducing business risks for your restaurant, bar, or other public food service establishment or get a demo of our <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a>.",
"post_title": "Retail Computer Vision Platform: Mask Detection and Safety Compliance At Restaurants",
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"post_date": "2023-01-18 09:20:34",
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"post_content": "Mask detection using AI increases public safety by reducing the spread of infectious diseases, such as COVID-19, and accelerating the reopening of the world. These <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">artificial intelligence models<\/a> provide a lot of value to <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> customers because they increase safety and ensure legal compliance.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/GGNSV1qok1w\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe>\r\n\r\nThese benefits far outweigh the cost of deploying these models.\r\n\r\nMask detection AI models work by:\r\n<ul>\r\n \t<li>Using PPE detection protocols to detect mask-wearing through computer vision.<\/li>\r\n \t<li>Processing video streams using our <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge AI platform<\/a> and intelligent video analytics, detecting faces with masks and those without masks. If a mask isn't detected, an alert is sent to relevant authorities who ensure that all public safety guidelines are followed.<\/li>\r\n \t<li>These <a href=\"https:\/\/chooch.ai\/wp-content\/uploads\/2021\/03\/chooch-ai-computer-vision-case-studies-2021.pdf\" target=\"_blank\" rel=\"noopener\">visual AI models<\/a> do not have facial recognition capabilities which ensures privacy.<\/li>\r\n<\/ul>\r\nMask detection can benefit organizations such as:\r\n<ul>\r\n \t<li>Offices and factories to ensure that employees maintain safety standards at all times.<\/li>\r\n \t<li>Hospitals that require <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\" target=\"_blank\" rel=\"noopener\">healthcare AI<\/a>, so that health providers protect themselves and their patients by wearing masks.<\/li>\r\n \t<li>Airports and any other public spaces to promote <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">public health and safety<\/a>.<\/li>\r\n<\/ul>\r\nAfter we've successfully deployed an AI model, we provide remote training. If one of our partners has specific needs, we can deploy a custom edge AI mode and tell you more about <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-edge-device\/\" target=\"_blank\" rel=\"noopener\">examples of edge devices<\/a>. Ready to learn more about how AI models detect masks? <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">Contact us<\/a> to launch your mask detection project.",
"post_title": "AI Mask Detection: Putting Edge AI, Computer Vision, and Intelligent Video Analytics to Good Use",
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"post_date": "2023-01-18 09:20:20",
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"post_content": "If your organization is in search of better, more robust, and reliable security to keep your personnel and assets safe, Chooch <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">AI computer vision<\/a> is all the solution you need. Computer vision provides broad, full-time, always-on security AI wherever and whenever you need it, utilizing your existing cameras, and running on edge devices to minimize operating costs.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/754ht7UoqnI\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nHuman security guards can only be in one place at a time and can only watch so many screens at once. AI with a <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a> can check any number of locations simultaneously for intrusion, vandalism, unauthorized access, and many more security criteria and concerns.\r\n\r\nChooch <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">computer vision with safety and security<\/a> pre-trained AI models with object recognition can identify assets, people, or heat signatures in less than 0.02 seconds, ensure compliance with safety and health regulations, detect and track vehicle movement, and analyze criteria like make, model, color, and license plate numbers, and much more.\r\n\r\nChooch\u2019s innovative <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision AI platform<\/a> can expand your security team\u2019s capabilities by enabling tasks like:\r\n<ul>\r\n \t<li>Multiple point-of-access, egress\/ingress, and Soft Target-Crowded Place (ST-CP) monitoring<\/li>\r\n \t<li>Facial identification for area access and control via <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition<\/a><\/li>\r\n \t<li>Real-time intruder detection, monitoring, and tracking<\/li>\r\n \t<li>Simultaneous monitoring of many different locations or assets<\/li>\r\n<\/ul>\r\nComputer vision never blinks, <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener\">AI models<\/a> never sleep, and Chooch combines them to help your organization meet its unique safety and security requirements. Click here to learn more about computer vision for security and <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">contact Chooch<\/a> to learn more about how computer vision can benefit you.",
"post_title": "Computer Vision for Security: AI Models for Break-Ins",
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"post_date": "2023-01-18 09:18:51",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> supports public safety by using <a href=\"https:\/\/chooch.ai\/wp-content\/uploads\/2021\/03\/chooch-ai-computer-vision-case-studies-2021.pdf\" target=\"_blank\" rel=\"noopener\">Visual AI<\/a> to detect safety equipment, such as masks and other personal protective gear, and signs of illness, such as coughing, in public places. Using <a href=\"https:\/\/www.chooch.com\/blog\/safety-ai-model-ppe-detection-video\/\">PPE detection<\/a>, our cough and mask detection model lowers the risk of spreading COVID-19 which can help reduce costs and save lives. Chooch AI customers enjoy the benefit of protecting their employees and customers from COVID-19 using a fast, accurate, and cost-effective method.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/C0QIOmGRVzU\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe>\r\n\r\nThis <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\" target=\"_blank\" rel=\"noopener\">healthcare AI<\/a> model detects coughs and mask-wearing using the following procedure:\r\n<ul>\r\n \t<li><a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">Artificial intelligence models<\/a> are trained to detect instances of coughing and mask-wearing.<\/li>\r\n \t<li>Chooch AI processes video streams to identify instances of coughing and to detect mask-wearing.<\/li>\r\n \t<li>If our <a href=\"https:\/\/chooch.ai\/wp-content\/uploads\/2021\/03\/chooch-ai-computer-vision-case-studies-2021.pdf\" target=\"_blank\" rel=\"noopener\">visual AI<\/a> detects instances of coughing or no mask is detected, alerts are sent out for follow-up.<\/li>\r\n \t<li>This model has no <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a> which ensures privacy.<\/li>\r\n<\/ul>\r\nYou can deploy cough and mask detection models in offices, factories, airports, and schools. Once we've successfully deployed this model, <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> will provide remote training. If one of our partners has specific needs, we can also deploy a custom model. You can learn more about how AI models detect coughs and masks, and then contact Chooch AI to discuss your cough and mask detection project.",
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"post_date": "2023-01-18 09:17:11",
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"post_content": "<span style=\"font-weight: 400;\">An AI (artificial intelligence) model is a program that has been trained on a set of data (called the <\/span><i><span style=\"font-weight: 400;\">training set<\/span><\/i><span style=\"font-weight: 400;\">) to recognize certain types of patterns. <a href=\"https:\/\/www.chooch.com\/blog\/4-ways-generative-ai-is-improving-computer-vision\/\">AI models<\/a> use various types of algorithms to reason over and learn from this data, with the overarching goal of solving business problems. There are many different fi<\/span><span style=\"font-weight: 400;\">elds that use AI models with different levels of complexity and purposes, including <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>, robotics, and natural language processing.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">As mentioned above, a <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning algorithm<\/a> is a procedure that learns from data to perform pattern recognition and creates a machine learning model. Below is a sampling of just a few simple machine learning algorithms:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\">-nearest neighbors: The <\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\">-nearest neighbors algorithm is used to classify data points based on the classification of their <\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\">nearest neighbors (where<\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\"> is some integer). For example, if we have <\/span><i><span style=\"font-weight: 400;\">k <\/span><\/i><span style=\"font-weight: 400;\">= 5, then for each new data point, we will give it the same classification as the majority(or the plurality) of its closest neighbors in the data set.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Linear regression: Linear regression attempts to define the relationship between multiple variables by fitting a linear equation to a dataset. The output of a linear regression model can then be used to estimate the value of missing points in the dataset.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\">-means: The <\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\">-means algorithm is used to separate a dataset into <\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\"> different clusters (where <\/span><i><span style=\"font-weight: 400;\">k<\/span><\/i><span style=\"font-weight: 400;\"> is some integer). We start by randomly choosing <\/span><i><span style=\"font-weight: 400;\">k <\/span><\/i><span style=\"font-weight: 400;\">points (called centroids) in space, and assigning each point to the closest centroid. Next, we calculate the mean of all the points that have been assigned to the same centroid. This mean value then becomes the cluster's new centroid. We repeat the algorithm until it converges, i.e. the position of the centroids does not change.<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-weight: 400;\">AI and <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning algorithms<\/a> are fundamentally mathematical entities, but can also be described using <\/span><i><span style=\"font-weight: 400;\">pseudocode<\/span><\/i><span style=\"font-weight: 400;\">, i.e. an informal high-level language that looks somewhat like computer code. In practice, of course, <a href=\"https:\/\/www.chooch.com\/blog\/4-ways-generative-ai-is-improving-computer-vision\/\">AI models<\/a> can be implemented with any one of a range of modern programming languages. Today, various open-source libraries (such as scikit-learn, TensorFlow, and Pytorch) make AI algorithms available through their standard application programming interface (API).<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Finally, an <a href=\"https:\/\/www.chooch.com\/blog\/4-ways-generative-ai-is-improving-computer-vision\/\">AI model<\/a> is the output of an AI algorithm run on your training data. It represents the rules, numbers, and any other algorithm-specific data structures required to make predictions about unseen test data.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">The decision tree algorithm, for example, creates a model consisting of a tree of if-then statements, each one predicated on specific values. Meanwhile, deep neural network algorithms create a model consisting of a graph structure that contains many different vectors or weights with particular values.<\/span>\r\n\r\nPlease visit these pages to <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">learn more about AI models or how AI models are used<\/a> as <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a>.",
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"post_date": "2023-01-18 09:15:43",
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"post_content": "The <a href=\"https:\/\/www.chooch.com\/platform\/\">computer vision platform<\/a> at Chooch AI is scaling with our customer and partner engagement. In this webinar, we introduce an updated dashboard offering analytics, alerts, and insights. Read the entire Chooch AI Product Update webinar below.\r\n\r\nLearn more about our computer vision platform from the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Product<\/a> page.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/zuNDkRAD-GM\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe>\r\n\r\nHere's the transcript.\r\n\r\nVina:\r\n\r\nOur API is organized around REST API. This allows our developers to programmatically communicate back and forth with our platform, but it's just as easy to use as our UI. And also our API is compatible with live streams and live tagging, which is done through the user's edge device, which brings up our next slide. When we talk about edge device, we refer to a locally placed server that you can run <a href=\"https:\/\/www.chooch.com\/\">AI models<\/a> on, which is OnPrem, not in the cloud and close to a video feed. On the dashboard view, there's a list of devices ready for you to access. Within edge devices, you can manage your own camera streams, set up new devices, and deploy AI models. With edge devices, this is very useful when it comes to increasing privacy and security, since it is locally stored and it's not in the cloud.\r\n\r\nSo what are some of these models, you're asking? Our next slide for public models, Chooch has developed these models that allow you to quickly detect any object that you were looking for. Some examples of these <a href=\"https:\/\/www.chooch.com\/solutions\/public-sector-ai-vision\/\">public models<\/a> is fire detection. Here in California, as we all know, we do have a lot of fires, especially detecting those small fires before it becomes massive fires. And then also we do <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">smoke detection<\/a>, which goes hand in hand with fire detection, as you can probably detect smoke before you can detect the fire. PPE detection, I think that's really valuable, right now, especially in the workplace, making sure that your employees are wearing hard hats or safety vests or gloves, or even at the airports wearing a mask.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\">PPE detection<\/a> is very valuable right now during this pandemic. We also have <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">human fall detection<\/a>, right? Making sure that you're detecting, if your employee has fallen in the workplace, what type of actions do you need to take and also how to prevent that from happening again. So now that you've seen our public models, let me show you some of our dataset tools in our next slide. With dataset you can create your own custom models. As you can see here, we have some airplanes, which is part of our object detection model.\r\n\r\n<img class=\"alignright wp-image-3387\" src=\"\/wp-content\/uploads\/2023\/06\/chooch-ai-product-features-review.jpg\" alt=\"Chooch AI product update features review\" width=\"541\" height=\"315\" \/>\r\n\r\nSo 2D and 3D synthetic data. This is really helpful in helping generate different image viewpoints, object poses, backgrounds, and lighting conditions.\u00a0As you can see in that screenshot, the airplane is seen in the daytime and also at night. So that's different lighting conditions, and for generating different angles, such as like tools and machinery to identify different components and different parts of those tools or machinery. 2D and 3D synthetic data can also be really helpful in enhancing your dataset with images that are less common or hard to find. With smart annotations, you can use custom or <a href=\"https:\/\/www.chooch.com\/solutions\/public-sector-ai-vision\/\">public models<\/a> to annotate additional objects within a dataset. Let's just say you have a thousand images within your dataset, smart annotation will make it easier to detect those additional objects. With augmentation, you can make slight modifications to your images in a dataset by rotating, scaling, or cropping, as you can see the image of that airplane, there's different angles.\r\n\r\nAlso with data augmentation <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a> provides the ability to copy and modify 2D objects, hundreds of times, to train the AI faster. Lastly, our next slide we'll do a high level overview of some of our version four updates. So I'll just highlight that now we have Kubernetes deployment, that's supported. Our average inference speed has increased up to two times, and so this means that for a basic AI model, you can do 10 predictions within one second. We also support live streams from YouTube and M3U playlists, and we've also improve multiple streaming and inference features. So I'll go ahead and pass this on to Peter who will now discuss more about our new analytics feature. Thank you.\r\n\r\nPeter:\r\n\r\nThank you very much, Vina for that. Again, this is Peter now. Again, we're going to go and talk a little bit more about the new analytics interface as well as our alerting configuration options, being able to change or manipulate, or provide a workflow on intelligence on what is and is not alarming from the system. From here we have on our analytic interface, we have the ability to view different predictions in the system. This again is a way to look at them in different graph or graphing interfaces, filtering them based on time or feed or model, and then also being able to manipulate based on time.\r\n\r\nOkay, so here we see some examples of the different interface. On the top, we have our different filtering options. Again, we can choose based on device, so different edge component, which we'll go in a little bit later. Per stream or camera stream, that's our terminology for that. Different zones, which we'll go out in a second again. Hey, different geofences within a video feed. Hey, loading dock, or another part of the interface, and then also events. So hey, I want to see only certain events. On the top right we can also see the different filtering on time. So instead of looking at it for all of time, maybe the last month or the last week, again, being able to filter and dial in this information within our interface. As you make these changes, you can see on the bottom an example of the line graph, but again, this can be changed. We can also see pie graphs or other manipulations like that.\r\n\r\nAnd as part of this interface, we also are able to see the actual event or alert image as well. So for example, the first three pictures there, we see a geofence or zone, where a forklift or a person triggered that specific event in the system. And again, we grab a snapshot from that video feed and store it in the solution, and then for the other pictures there, we see an example of a retail situation where maybe a geofence or zone is drawn around a specific kiosk that we want to monitor activities there. So hey, a person came there, maybe they dwelled more than 60 seconds, again, we're able to data mine that as part of our analytics here.\r\n\r\nOkay, the next thing here is we're going to look and talk about our rules interface. So now we're able to provide a workflow or filtering methodology to define not only when do we get predictions, but then based on certain criteria. And so the first event here is, hey, from a model that we saw going to talk about our earlier, where we have some models that have upwards of a thousand different predictions in it, or again, if you create your own model yourself, you can go ahead and define that. But hey, within this interface, I only want to go ahead and look for the packages as part of a certain model. And so, hey, the first tab here is we define what annotation or label we want to grab from them.\r\n\r\nAnd then the next tab... We see, hey, not only do we want to see its physic model triggered based on this prediction, but also a geofence. So in this example here, there's this zone that was drawn around a loading dock, so when we see in this example here again, not packages, but hey, when a forklift comes into that zone, we want to get an alarm. And then on the last one we have rules.\r\n\r\nAnd so in here within these different components, we're able to also define temporal presence, proximity, and zones, which we saw a second ago. So temporal is time, presence is the amount of people or objects in that area, proximity is distance between, so like social distancing, like what we're dealing with right now, and then lastly zones. So zone again, we saw it earlier. So what this ultimately means is all the different criteria between events, zones, and rules, would then trigger an event in our system. So we're trying to filter out the noise, making things only pertinent based on what is actionable, based on the custom use case, things like that. And then we'll pass on over to Omid to do a live demo of the system. Thank you very much.\r\n\r\nOmid:\r\n\r\nAwesome, thank you for that. Let me go ahead, share my screen. All right. All right, so we're going to begin off with the dashboard. So this is the landing page when you first log into your <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a> dashboard. You're going to see all of your <a href=\"https:\/\/www.chooch.com\/api\/\">API documentations<\/a>, our <a href=\"https:\/\/www.chooch.com\/platform\/\">platform guides<\/a>, and our how-to videos right here on the right-hand side, and then you'll also see quick navigational access on the top-hand side. If you scroll down to the bottom, you'll see all of our latest updates, so what's changed on the platform, and anytime we make new updates, you'll be able to see that there as well as our API key. Now just kind of re-highlighting some of the points that our team has talked about. We want to touch on some of the public models real quick, so I can come here and we can go to our object detection models.\r\n\r\nAnd here again is where you can quickly leverage our pre-built models out of the box. So there's no customized development needed. These are ready to go. You can push these to your devices and be able to use them rapidly. You can note that some are very lightweight, they'll have two classifications and I'll get into what that means, but basically fall down or standing, and then we have some models that have over 11,000 classifications, like our general deep detection. So this is leveraging and detecting everything from phones to cells, to different actions like swimming, biking, climbing, it incorporates all these all into this one large model. Now there's a lot of instances where users or customers may need their own custom developed model. So what we allow users to do is to develop those models, and our own team actually leverages as these tools in-house.\r\n\r\nSo if we go to our datasets tab here and we go to my object datasets... Here, you can see some models that we have started, some datasets that we have started annotating ourselves. And if you already have previously annotated datasets, what you can do is just hit upload dataset and upload that dataset as long as it's in the COCO JSON format, with all the supporting <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images<\/a> with it. Now I'm going to touch on this dataset here. This is one that we can all easily relate to, and it's just on airplanes. Now, what you can see here is we have over 1,700 different images already loaded and annotated on this platform. Now, if you're doing this manually image per image, that can take a long time and sometimes you may not have all of those images readily available.\r\n\r\nSo what our platform allows you to do is to upload limited amount of data, and then use the additional tools to generate larger amounts of data to be used for model training. And just to give you a reference with these 1,700 images that are already annotated and produced here, we only use 30 real images, one 35 second video clip, and one CAD file. And with that, we're able to do things like generating synthetic data. So 3D synthetic data, you would upload your CAD file and then a material file with it, and then <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a> would be able to apply either your specific backgrounds or we have generic backgrounds that we could apply to it, to then generate vast amounts of data. 2D Synthetic data is basically once you annotate an object, you can then apply your own backgrounds and themes, and then Chooch starts randomizing and making additional <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images<\/a> from your data already.\r\n\r\nAnd then we have things like smart annotation, so that's going through and automatically labeling a lot of these. So if you already have, let's say 30 aircrafts, you can go through smart annotation and be able to automatically let the system annotate all those aircrafts for you. And then the last thing is I call it the knockout punch, is using augmentation. What augmentation will do, it'll take all those images and then apply either rotational or horizontal flips, then start adding additional, basically shifting scaling rotation, noise, blur, and brightness, and contrast to the images that are being generated, to allow for different environmental considerations. Now, just to show you what our annotation tool looks like, I'll go into this image real quick... And here you can see this is going to be a no-code approach to labeling and annotating. So all we're doing is taking this tool and actually drawing our bounding box around this aircraft.\r\n\r\n<img class=\"alignright wp-image-3386\" src=\"\/wp-content\/uploads\/2023\/06\/chooch-ai-platform-identify-aircraft.jpg\" alt=\"Chooch AI product update webinar no code platform identify aircraft\" width=\"528\" height=\"308\" \/>\r\n\r\nSo we can just click drag and draw, and then we can label it as a 747, and once we hit save, you'll see this number actually increase. Now we have 1,772 <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images<\/a>, as well as annotations. And just to give you an idea of what the 2D synthetic looks like, so this aircraft is labeled and facing the left. Now, if I go to the next image, you'll see the same aircraft is just resized facing the opposite direction with a different background now applied to it. So these are the ways and methods that we're able to now generate larger amounts of data, so you can create a strong model. Now, once your data is annotated and ready to go, what you can do is just hit, create model, give it a name and then hit create.\r\n\r\nAnd once we hit create, <a href=\"https:\/\/www.chooch.com\/\">Chooch<\/a> is going to take this internally and start running through and building a model for us. So now what we can do is once we have these models developed and they're built in-house with your annotations and your maybe data scientists applying their knowledge into it... And again, this can be anything from detecting objects or detecting anything from, let's say, cancer or bacteria cells on X-ray visions. So it's basically taking the expertise of an individual, like a doctor, and applying it to the AI. And once we build the model, then you can go to your devices.\r\n\r\nSo in creating the devices, very simple and easy, as long as you meet some of our basic criteria, you'll see here, you'll be able to run on Ubuntu and Red Hat, you'll be a select your GPU, and then if you leverage MQTT for machine-to-machine communication, you can also do that, so when we generate any detections, we can send it to that MQTT broker to then distribute as you have defined it. So in this case, we already have a device created and we're running on a T4 GPU. And when we come in, we can now apply different streams. So adding a stream is fairly simple, you'd hit add stream, give it a name, and let's call this... Airport runway, and then you would give it an IP. We'll just give it a blank IP here, and we'll hit add string.\r\n\r\n<img class=\"alignright wp-image-3388\" src=\"\/wp-content\/uploads\/2023\/06\/chooch-ai-product-webinar-forklift-and-people-detection.jpg\" alt=\"Chooch AI product webinar forklift and people detection\" width=\"523\" height=\"305\" \/>\r\n\r\nSo now this new stream data has been added. And then you can go into this stream and apply a model. So if we go into one, like our smart loading example, we already have a forklift custom model built, but if we wanted to, we can add additional models to it, so we're not limiting you on the amount of models that you can add per stream. So let's see what this actually looks like. So this new screen I just came into is our edge dashboard. This edge capability can be in your private cloud, it can be on premise, or you can also leverage our cloud as well. Now here, you're seeing all the different people, forklifts, boxes, and packages being annotated and detected here, and you're seeing everything in red. Those are all the current objects and people that are being detected. Now we want to take it to another level and apply some additional analytics to this. So if we come back to our dashboard here, we can go to our analytics settings...\r\n\r\nAnd here's where we start defining our different classes and groups. So now we can additionally group person and forklift together. So you can create a group like we have done here, and then we can go to our zones tab. So these are all like Peter was mentioning earlier, we can annotate different zones. So in this case, on our zone map, you can see we have an unloading zone and then we have a loading zone. So once you use, and it's the same tooling that you saw before for doing the annotation, it's just a no-code approach. We can then go to rules. So in this rule, we have defined danger zone. So it's a person, forklift, and the unloading zone together for more than one second, then we will generate an alert. So if we go back to our devices, what users can additionally do is be alerted either in real time, or select a different frequency.\r\n\r\nSo here you can see that I've applied my email to this alert and reports email, and frequency, you can do either real time, you can do end of hour, end of day, end of week, or end of month, and then you can include the alert <a href=\"https:\/\/www.chooch.com\/imagechat\/\">images<\/a> in those as reports as well. So if we go to our analytics tab now... Navigate here... What the system will do now is gather all of the alerts that it has detected, and be able to show us in a graph format or in pie charts, right? And as Peter mentioned, and you can start toggling and applying different filters, so if you have an incident and you want to drill down to say, \"Hey, I need to go back to this day, at this hour, on this specific camera,\" you can now filter that and quickly access some of the analytics behind it.\r\n\r\nSo here you can see the pie charts with all of our forklifts, our smart loading... And then if we actually go to alert images, you can actually get a snapshot view of some of those alerts that have occurred. So in this case, in our forklift danger, we have a person that's very close to the forklift and one of the zones. So we're able to highlight that as well. And what this would look like in the email, if I go back here, you go to our emails, you'll see that in the email, you can get an alert for the specific device, the stream, the date and time, as well as some of the rules that were triggered here. And then you'll get again, the snapshot of that incident or that alert or that rule being broken. So with that, I will stop sharing and hand it back over to Andrew.\r\n\r\nAndrew:\r\n\r\nThanks Omid, and thanks to our entire Chooch team for the efforts on those updates. Now, we will open it up for Q&A. We'll take a brief 30 seconds and let everyone enter any questions on the chat below, and we actually had a couple that just came in and we'll kick it off and as questions come in we will tackle those. So our first question is, how long does a typical model take to develop?\r\n\r\nOmid:\r\n\r\nYes, I can go ahead and answer that one, Andrew. So typical models can vary on timelines. That variation can be on the amount of data being supplied and the complexity in which we're tackling. Now, traditionally, a typical model can take between six months to a year to develop, but with the tools that we show today, you can rapidly develop models and being very conservative in a matter of a couple of weeks. And that's with a lot of testing being done and making sure that the accuracy is where we would like it to be. That's a good question.\r\n\r\nAndrew:\r\n\r\nYeah, great question. Great question. Another question we have regarding models is, do you all build models or provide just professional services?\r\n\r\nPeter:\r\n\r\nI can go ahead and take that one. So great question as well. So we provide either two options here, either we can provide the services for you to do the development and training of a model. We can also train the trainer where we can help provide mentoring or consulting services to use the platform, or the platform as itself, as you saw from Omid's demonstration is with a little guidance and documentation and self explanatory, where you can potentially do the model development yourself as well. So depending upon the business model and the needs and how complex use cases, we can enter either all three possible pursuits. Thank you.\r\n\r\nAndrew:\r\n\r\nAwesome, thanks Peter. Another question regarding the private cloud, can you be installed on my private cloud?\r\n\r\nPeter:\r\n\r\nI'll take that one as well. Our solution is very flexible in that it can be deployed a hundred percent in the cloud, or there could be also what we call the edge component OnPrem, but the edge component can also deployed in your cloud as well, so we're very dynamic to what can deployed and where it is. We can try and explore that further based on exactly what would be the required compute and things like that.\r\n\r\nAndrew:\r\n\r\nExactly. Thanks Peter. This one's regarding alerts. Funny timing, right after you Peter, does our platform have the ability to send out SMS alerts, not just email alerts?\r\n\r\nPeter:\r\n\r\nYep, and I'll grab that one as well. So in addition to email and SMS, be it reports or alerts, we do support MQTT integration as well. And so that's a standard within the IoT or OnPrem interface and industry, so we can integrate to an MQTT broker and then provide our predictions via JSON or things like that. Thank you.\r\n\r\nAndrew:\r\n\r\nAwesome, thanks Peter. Another one that we have here is how many public models does Chooch currently have available to customers? So I can kind of tag this one, we have over 250,000 models and it really comes down to, with our team, with the rapid model development. That's how we have come to this number. I think we got time for maybe a couple more here as we keep going through here. Oh, someone's done their homework. You did not mention anything about the IC2 app. Can you talk a little bit more about the IC2 app and how it connects with public models?\r\n\r\nPeter:\r\n\r\nOne minor correction to what you just said, Andrew, it actually classifications or annotations. So that means, hey, we're able to look for smoke, car, wave, boat, et cetera, but we have upwards about 80 different models, varying from image, object detection, facial detection, and also text detection. We have up to 250,000 different type of classes for those detections. Going to your second question there, Andrew, thank you for that in regards to IC2. So the IC2 mobile app, be it for Android or Apple is a way to demonstrate and test out our general deep detection, analytic, or model we have in our solution. And so what this does is you're providing a camera interface, looking at different things. My picture on my wall here, myself, or another person next to me, it's providing images up to our cloud and we're providing general detection looking at a person's gender or age or sentiment or things like that. So again, it's a way to test out and demonstrate the power of our product.\r\n\r\nOmid:\r\n\r\nI just wanted to add to that with the <a href=\"https:\/\/apps.apple.com\/us\/app\/ic2-chooch-ai-computer-vision\/id1304120928\" target=\"_blank\" rel=\"noopener\">IC2<\/a>, we're constantly developing and iterating on it. And now what we're also able to do is annotate on the fly. So you could be out in the field somewhere and you need to make an annotation. You can grab an image of it, annotate it, and it'll actually get pushed up to your cloud dataset. So in your dataset, you'll have an actual tag there for your <a href=\"https:\/\/apps.apple.com\/us\/app\/ic2-chooch-ai-computer-vision\/id1304120928\" target=\"_blank\" rel=\"noopener\">IC2 app<\/a>, and then all the images that you annotate on your phone through the IC2 app will get generated there, so you can incorporate that into your larger datasets.\r\n\r\nAndrew:\r\n\r\nAbsolutely. And thanks Peter, for the clarification on that one. We got time for one more question here. Does Chooch AI offer pilots to companies, and how long is a typical pilot? We can kind of tag team this one. A pilot or a kickstart can vary anywhere from weeks to months, but the short answer is yes, we do offer that, and would love to talk to you more about how this works with you and scope this process with you. Absolutely, we do. Any team members have anything else to add on that?\r\n\r\nPeter:\r\n\r\nYeah, and I'll jump in there as well. And again, the pilot is dependent upon the use case, the model to be developed, and just also how ROI or return on investment is measured on that. But ultimately it's on a case by case basis, and within the timeframe kind of what Andrew budgeted there.\r\n\r\nAndrew:\r\n\r\nAbsolutely. Well, we are our running short of time here. We try to get to all the questions. Those that we did not, we'll try to get back to you via this chat. Thank you all for joining today, and please feel to reach out to us at chooch.ai, and we look forward to communicating with you and answering more of your questions. Thanks so much for joining us today, have a great rest of your day.\r\n\r\nPeter:\r\n\r\nThank you very much, everyone.\r\n\r\n ",
"post_title": "Chooch AI Product Update 2022: Updated Dashboard, Analytics, Alerts and more.",
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"post_content": "Object recognition is a subfield of <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">computer vision<\/a>, artificial intelligence, and machine learning that seeks to recognize and identify the most prominent objects (i.e., people or things) in a digital image or video with <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI models<\/a>. Image recognition is also a subfield of AI and computer vision that seeks to recognize the high level contents of an image.\r\n\r\n<img class=\"alignnone wp-image-2912 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/object-recognition-and-image-recognition.jpg\" alt=\"Object Recognition and Image Recognition\" width=\"1000\" height=\"666\" \/>\r\n<h2>How Is Object Recognition Different from Image Recognition?<\/h2>\r\n<span style=\"font-weight: 400;\">If you\u2019re familiar with the domain of computer vision, you might think that object recognition sounds very similar to a related task: <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition<\/a>. However, there\u2019s a subtle yet important difference between image recognition and object recognition:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><span style=\"font-weight: 400;\">image<\/span> <span style=\"font-weight: 400;\">recognition<\/span><span style=\"font-weight: 400;\">, the AI model assigns a single high-level label to an image or video.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><span style=\"font-weight: 400;\">object recognition<\/span><span style=\"font-weight: 400;\">, the AI model identifies each and every noteworthy object in the image or video.<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-weight: 400;\">The best way to illustrate the difference between object recognition and image recognition is through an example. Given a photograph of a soccer game, an <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition model<\/a> would return a single label such as \u201csoccer game.\u201d An object recognition model, on the other hand, would return many different labels corresponding to the different objects (e.g., the players, the soccer ball, the goal, etc.), as well as their positions in the image.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Object recognition is also not quite the same as another computer vision task called object detection:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Object recognition models are given an image or video, with the task of identifying all the relevant objects in it.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.chooch.com\/blog\/what-is-object-detection\/\">Object detection<\/a> models are given an image or video as well as an object class, with the task of identifying all the occurrences of that object (and only that object).<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-weight: 400;\">For example, suppose you have an image of a street scene:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An object detection model would take this image as input as well as an object class such as \u201cpedestrian\u201d or \u201ccar,\u201d and then return all the detected locations in the image where that object occurs.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An object recognition model, on the other hand, would return the locations of both pedestrians and cars, as well as all other objects it recognizes in the image (buildings, street signs, etc.).<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-weight: 400;\">You can therefore think of <a href=\"https:\/\/www.chooch.com\/blog\/what-is-object-detection\/\">object detection<\/a> as a \u201cfilter\u201d on the output of general object recognition models, looking only for a specific type of object.<\/span>\r\n<h2>How Are Object Recognition Models Trained?<\/h2>\r\n<span style=\"font-weight: 400;\">To perform object recognition, machine learning experts train <\/span><a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\"><span style=\"font-weight: 400;\">AI models<\/span><\/a><span style=\"font-weight: 400;\"> on extremely large datasets of labeled data. Each member of the dataset includes the source image or video, together with a list of the objects it contains and their positions (in terms of their pixel coordinates).<\/span>\r\n\r\n<span style=\"font-weight: 400;\">By \u201cstudying\u201d this dataset and learning from its mistakes, the AI model gradually improves its capability to recognize different classes of objects during <\/span><span style=\"font-weight: 400;\">AI training<\/span><span style=\"font-weight: 400;\">, just as humans learn to recognize different visual concepts.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Once the model has been trained on a preexisting dataset, it can start analyzing fresh real-world input. For each image or video frame, the model creates a list of predictions for the objects it contains and their locations. Each prediction is assigned a confidence level\u2014i.e., how much the model believes the prediction represents a real-world object. Predictions that are above a given threshold are classified as objects, and they become the final output of the system.<\/span>\r\n<h2>How Are Image Recognition Models Trained?<\/h2>\r\n<span style=\"font-weight: 400;\">The AI model training process for <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition<\/a> is similar to that of object recognition. However, there\u2019s one crucial difference: the labels for the input dataset.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Object recognition datasets bundle together an image or video with a list of objects it contains and their locations. Image recognition datasets, however, bundle together an image or video with its high-level description.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Before training an <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition model<\/a>, machine learning experts need to decide which categories they would like the AI model to recognize. For example, a simple weather recognition model might classify images as \u201csunny,\u201d \u201ccloudy,\u201d \u201crainy,\u201d or \u201csnowy.\u201d Each image or video in the training dataset needs to be associated with one of these labels, so that the model can learn it during the training process.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Once the <a href=\"https:\/\/www.chooch.com\/imagechat\/\">image recognition model<\/a> is trained, it can start analyzing real-world data. The model accepts an image as input, and returns a list of predictions for the image\u2019s label. As with object recognition, each prediction has a confidence level. The prediction with the highest confidence level is selected as the system\u2019s final output.<\/span>\r\n<h2>What Is Object Recognition Used for?<\/h2>\r\n<span style=\"font-weight: 400;\">Object recognition has many practical use cases. Below are just a few applications of object recognition:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\"><span style=\"font-weight: 400;\">retail AI<\/span><\/a><span style=\"font-weight: 400;\">, object recognition models can identify different products and brands on the shelves to analyze how customers interact with and purchase them.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/www.chooch.com\/solutions\/geospatial-ai-vision\/\"><span style=\"font-weight: 400;\">geospatial AI<\/span><\/a><span style=\"font-weight: 400;\">, wildlife researchers can use object recognition on drone footage to analyze how animal populations change in an area over time.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><span style=\"font-weight: 400;\">media AI<\/span><span style=\"font-weight: 400;\">, sales and marketing professionals can use object recognition to identify \u201cobjects\u201d such as logos, brands, and products to better understand the contents of an image.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Autonomous vehicles require object recognition to identify the most relevant parts of the world around them (e.g., pedestrians, road signs, or other cars).<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-weight: 400;\">Facial authentication<\/span><span style=\"font-weight: 400;\"> can also be considered a special case of object recognition in which a person\u2019s face is the \u201cobject\u201d that must be detected. Modern facial recognition systems can detect thousands of different faces with extremely high accuracy in just a fraction of a second.<\/span>\r\n<h2>What is Image Recognition Used For?<\/h2>\r\n<span style=\"font-weight: 400;\">Like object recognition, image recognition is used in a wide variety of industries and applications. Below are some examples:<\/span>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\"><span style=\"font-weight: 400;\">manufacturing AI<\/span><\/a><span style=\"font-weight: 400;\">, image recognition models can examine products and classify them as \u201cdefective\u201d or \u201cnon-defective.\u201d<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><span style=\"font-weight: 400;\">security AI<\/span><span style=\"font-weight: 400;\">, construction sites can use image recognition to make sure that workers are wearing their personal protective equipment (PPE), classifying surveillance images as \u201ccompliant\u201d or \u201cnon-compliant.\u201d (<\/span><a href=\"https:\/\/www.chooch.com\/blog\/safety-ai-model-ppe-detection-video\/\"><span style=\"font-weight: 400;\">Click here<\/span><\/a><span style=\"font-weight: 400;\"> to see a video of a PPE detection model in action.)<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\"><span style=\"font-weight: 400;\">healthcare AI<\/span><\/a><span style=\"font-weight: 400;\">, physicians can use image recognition models to analyze the output of medical imaging devices. For example, an AI trained on mammogram images can classify the machine\u2019s output as \u201cbenign\u201d or \u201cpotentially cancerous,\u201d flagging it for review by a human expert.<\/span><\/li>\r\n<\/ul>\r\n<h2>Why Use Chooch for Object Recognition?<\/h2>\r\n<span style=\"font-weight: 400;\">Chooch is a powerful, feature-rich computer vision platform for building <a href=\"https:\/\/www.chooch.com\/blog\/what-is-object-detection\/\">object recognition and image recognition models<\/a>. We\u2019ve helped businesses of all sizes, industries, and technical levels deploy and manage visual AI and <\/span><span style=\"font-weight: 400;\">computer vision solutions<\/span><span style=\"font-weight: 400;\">.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Thanks to Chooch, there\u2019s no need to hire your own in-house team of AI and machine learning experts. Instead, you can hit the ground running with one of our dozens of pre-trained object recognition models that have been designed to fit a wide range of business use cases. You can also leverage the Chooch AI platform to train your own highly accurate object recognition model using a custom dataset, and then deploy it in the cloud or with an <\/span><a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\"><span style=\"font-weight: 400;\">edge AI platform<\/span><\/a><span style=\"font-weight: 400;\">.<\/span>\r\n<h2>Why Use Chooch for Image Recognition?<\/h2>\r\n<span style=\"font-weight: 400;\">The Chooch AI platform makes it simple to get started creating your own robust, production-ready image recognition and object recognition models. From within the Chooch dashboard, you can select one of our 100+ pre-trained AI models, or create a custom model based on a specific dataset. Our user-friendly AI platform lets you easily label and annotate dataset images and dramatically shorten the training process.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">Ready to start building sophisticated, highly accurate image recognition and object recognition AI models? So are we. If you\u2019re comfortable delving into the technical details, feel free to check out our <\/span><a href=\"https:\/\/www.chooch.com\/api\/\"><span style=\"font-weight: 400;\">computer vision API<\/span><\/a><span style=\"font-weight: 400;\">. Otherwise, you can <\/span><a href=\"https:\/\/www.chooch.com\/contact-us\/\"><span style=\"font-weight: 400;\">schedule a call with our team of AI experts<\/span><\/a><span style=\"font-weight: 400;\"> for a chat about your business needs and objectives, or <\/span><a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\"><span style=\"font-weight: 400;\">create your free account<\/span><\/a><span style=\"font-weight: 400;\"> on the Chooch computer vision platform.<\/span>\r\n<h2>Why Use Chooch for Object Recognition?<\/h2>\r\nChooch is a powerful, feature-rich computer vision platform for building <a href=\"https:\/\/www.chooch.com\/blog\/what-is-object-detection\/\">object recognition and image recognition models<\/a>. We\u2019ve helped businesses of all sizes, industries, and technical levels deploy and manage visual AI and computer vision solutions.\r\n\r\nThanks to Chooch, there's no need to hire your own in-house team of AI and machine learning experts. Instead, you can hit the ground running with one of our dozens of pre-trained object recognition models that have been designed to fit a wide range of business use cases. You can also leverage the Chooch AI platform to train your own highly accurate object recognition model using a custom dataset, and then deploy it in the cloud or with <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">edge AI platform<\/a>.\r\n\r\nReady to start building sophisticated, highly accurate object recognition AI models? So are we. If you\u2019re comfortable delving into the technical details, feel free to check out our <a href=\"https:\/\/www.chooch.com\/api\/\" target=\"_blank\" rel=\"noopener noreferrer\">computer vision API<\/a>. Otherwise, you can <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener noreferrer\">schedule a call with our team of AI experts<\/a> for a chat about your business needs and objectives, or <a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\" target=\"_blank\" rel=\"noopener noreferrer\">create your free account<\/a> on the Chooch computer vision platform.",
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"post_content": "Chooch AI has created a suite of <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">AI solutions<\/a> with its visual <a href=\"https:\/\/www.chooch.com\/platform\/\">artificial intelligence platform<\/a> to detect lung injury, coughs, masks and fevers.\r\n\r\nHere are two video demos, but please\u00a0contact us for more information.\r\n<p style=\"text-align: center;\"><strong>Hand Washing Detection<\/strong><\/p>\r\n<p style=\"text-align: center;\"><iframe src=\"https:\/\/www.youtube.com\/embed\/PbemCcCvN8I\" width=\"100%\" height=\"425\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/p>\r\n<p style=\"text-align: center;\"><strong>Cough and Mask Detection<\/strong><\/p>\r\n<p style=\"text-align: center;\"><iframe src=\"https:\/\/www.youtube.com\/embed\/C0QIOmGRVzU\" width=\"100%\" height=\"425\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/p>\r\n<p style=\"text-align: center;\">Thanks for watch - please visit our <a href=\"\/see-how-it-works\/\">Reopening the World<\/a> page.<\/p>",
"post_title": "Covid-19 Visual AI Solution Videos: Hand Washing & Cough and Mask Detection",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Healthcare<\/a> providers are increasingly challenged as they are tasked with doing more, faster, especially in times of crisis. Now, the confluence of GPUs and AI has generated a solution to meet these challenges. Video streams connected to <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> on the NVIDIA Jetson platform can act as eyes, alongside microphones that can act as AI-enabled ears thanks to the <a href=\"https:\/\/www.nvidia.com\/en-us\/clara\/smart-hospitals\/\">NVIDIA Clara Guardian platform<\/a>. The benefits include improved public safety, better patient care, and more operational efficiency at healthcare facilities.\r\n\r\nWorking with NVIDIA, Chooch AI has made it possible to automatically monitor health safety without bias by detecting, for example, that hands have been scrubbed, masks are being worn, and that everyone is fever free. In medical procedures, visual AI ensures that all actions during a surgical procedure can be tracked and recorded. This can range from counting gauzes to minimize the risk that one is left in a surgical cavity to the recording the exact time anesthesia was applied and ended.\r\n\r\n<strong>Chooch AI Platform and NVIDIA Clara Guardian<\/strong>\r\n\r\nDelivering AI at the edge not only improves response times but also minimizes privacy concerns. Chooch runs <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a> on NVIDIA Jetson Nano, NVIDIA Jetson Xavier NX and NVIDIA T4 inference GPUs, generating response times as fast as 0.02 seconds for high accuracy image, action, and object recognition. Whether tracking actions in surgical procedures or mask compliance, NVIDIA and Chooch AI are bringing real-time AI to the edge of <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-in-healthcare\/\">healthcare<\/a>. While Chooch AI focuses on interpreting video in real time, NVIDIA Clara Guardian includes the NVIDIA DeepStream SDK as well as NVIDIA NeMo and Jarvis for AI-enabled speech and language processing.\r\n\r\nNVIDIA Clara is a healthcare-specific set of SDKs and application frameworks that run on the <a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/products\/egx\/\">NVIDIA EGX platform<\/a> for AI computing on edge servers and embedded devices. NVIDIA Clara Guardian is an application framework that simplifies the development and deployment of smart sensors with multi-modal AI anywhere in a hospital.\r\n\r\nFrom fever detection and mask detection to medical imaging analysis to action logging in surgical theaters, <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> on NVIDIA edge devices improves healthcare outcomes, powering new efficiencies in AI-enabled hospitals and beyond.\r\n\r\nLearn more about Chooch <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Healthcare AI<\/a>.",
"post_title": "Chooch AI Helps Improve Safety, Care and Efficiency in Healthcare with NVIDIA Clara Guardian",
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"post_content": "Speed, safety, and accuracy are crucial in the healthcare industry. <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\" target=\"_blank\" rel=\"noopener\">Computer vision in healthcare<\/a> applications are revolutionizing the healthcare industry by making it easier for healthcare organizations to improve patient care and streamline their internal processes.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/nezqrfAP-g8\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nThe uses for computer vision in healthcare include:\r\n<ul>\r\n \t<li>Medical Imaging Analysis to read x-rays and identify cancer cells. Already, AI models have a better track record than humans in making accurate diagnoses.<\/li>\r\n \t<li>Facial authentication to identify patients correctly and prevent wrong procedures. Facial authentication helps secure medical facilities by only allowing authorized personnel into restricted areas.<\/li>\r\n \t<li>Automatic surgery logs detect operating room procedures and log actions enhance efficiency and safety.<\/li>\r\n \t<li>Cough, mask-wearing, and handwashing detection ensures that healthcare workers follow safety and hygiene standards via <a href=\"https:\/\/www.chooch.com\/blog\/save-lives-and-lower-costs-ai-ppe-detection-with-computer-vision\/\" target=\"_blank\" rel=\"noopener\">PPE detection<\/a>.<\/li>\r\n<\/ul>\r\nHealthcare AI works by:\r\n<ul>\r\n \t<li>Training <a href=\"https:\/\/chooch.ai\/wp-content\/uploads\/2021\/03\/chooch-ai-computer-vision-case-studies-2021.pdf\" target=\"_blank\" rel=\"noopener\">visual AI<\/a> models to \u2018see\u2019 actions, such as handwashing, or recognize anomalies, such as tumors.<\/li>\r\n \t<li>Analyzing data in video streams from cameras according to predetermined standards.<\/li>\r\n \t<li>If it detects a breach or non-compliance, it sends an alert to decision-makers for remedial action.<\/li>\r\n<\/ul>\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\" target=\"_blank\" rel=\"noopener\">Computer vision for healthcare<\/a> is suitable for various healthcare organizations that want to streamline their processes. Chooch AI can deploy pre-trained <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\" target=\"_blank\" rel=\"noopener\">artificial intelligence models<\/a> immediately. If an organization has special use-cases, we can train custom models and quickly deploy them. If you want to learn more about healthcare AI, <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">contact us<\/a> today.",
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"post_date": "2023-01-18 09:03:28",
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"post_content": "<a href=\"https:\/\/open.spotify.com\/episode\/5Wl7nIlH5y8CZ0LHW25jl4?si=yRy3zYGsSzeXg4e9HoPviw\" target=\"_blank\" rel=\"noopener\">Listen on Spotify<\/a>\r\n\r\n\"We're gonna go pro-AI now. And, when we think about it, you know, we've been talking about all the status security, erasing the data, but what happens when your car is stolen, your child is kidnapped. Now, all of a sudden, artificial intelligence comes in handy. Right? It's a catch 22, it's a yin and yang of the world that we're gonna have to figure out. We're lucky we have Emrah on the phone with us right now. He's the co-founder and CEO of <a href=\"https:\/\/www.chooch.com\/\">Chooch Technologies<\/a> and let me give you a quick synapsis before he's on to explain it himself. This is computer vision that processes any visual data -- microscopic to satellite, the CCTVs to medical imaging, drones, etc. And we're really lucky to have him. I know he's a really busy guy so let's get right to it.\"\r\n\r\nCorey Morgan: Hey Emrah, it's Corey Morgan, co-host, how are you tonight?\r\n\r\nEmrah: Good. Good. How are you guys?\r\n\r\nJohnny Irish: Good. Good.\r\n\r\nCorey Morgan: Yeah, we're literally having such a good conversation but it really has on being, you know, on the protection side of all this. And, uh, on the flip side, you're gonna come in and say, \"You know what, this is gonna change our world and it's for the best.\" So, please tell us what's going on in your world and how you got involved with this really quickly.\r\n\r\nEmrah: Yeah, so privacy is definitely a concern and we've been talking about this for many, many years now and it keeps us up at night. But the fact is, AI is becoming part of our lives as we move forward. And when we do this Chooch AI, we're a <a href=\"https:\/\/www.chooch.com\/\">computer vision company<\/a>. So what we do is we clone human-built intelligence into machines. So if you're a biomedical expert and you're looking at cells all day and counting them and trying to identify them.\r\n\r\nWhat we do is we take their capability of doing that and we put it into machines so we don't have to do it anymore. And then we proliferate it so one person becomes, you know, a thousand or a million. We can basically proliferate it to infinity. And, similarly, with other things like aircraft engine parts. Basically, anything visual that humans do today, we can take and we can put into a machine. So that the machine tags the same way that a human would. So basically, this is what we're doing. It's real AI and we work with some of the major themes, either spacial or <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>. We're doing a lot of <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security and safety<\/a> as well.\r\n\r\nJohnny Irish: So how would that work, like, say to combat wildfires?\r\n\r\n[caption id=\"attachment_706\" align=\"alignright\" width=\"336\"]<img class=\"size-full wp-image-706\" src=\"\/wp-content\/uploads\/2023\/06\/ai-detection-wildfires.png\" alt=\"AI Detection of Wildfires\" width=\"336\" height=\"280\" \/> AI Detection of Wildfires[\/caption]\r\n\r\nEmrah: That's one of our projects, actually. So today, <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">wildfires<\/a> are detected by humans. Basically, okay there seems to be fire somewhere and they see some smoke and it's too late by that time, usually.\r\n\r\nSo what we're doing is we're processing 30 million images every 15 minutes from satellites and drones so that we can send back that information to first responders and send them email alerts or text alerts. So, humanly impossible to do, you'd need thousands and thousands of people looking at this imagery to detect <a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">wildfires from space<\/a>. And that's basically what we're doing to one of our major projects that we've been doing since 2018.\r\n\r\nJohnny Irish: Alright, you also mentioned <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>. You know sometimes I feel like our audience are, you know, just the general public and doesn't really know what's going on behind the scenes. So it's healthcare, it's wildfires. What are the main AI technologies currently being developed to make us safer and help us move on a daily basis throughout our lives that we might not know about?\r\n\r\nEmrah: Yeah, so it's really early in AI development to be perfectly straight-forward. It's sort of akin to internet in 1993-1994. It's just starting and you can browse two things but nothing substantial as it is today. AI is the same way.\r\n\r\nIt's very, very early. A lot of these components don't work out of the box. So it's not like, \"Oh, AI is here.\u201d It\u2019s a new computational tool, basically, and it\u2019s kind of horizontal today. So you take <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a>, for example, it\u2019s kind of horizontal across verticals. And then, you also have other things like NLU, NLP which is Natural Language Processing and Natural Language Understanding. And then, you have audio as well. You have speech. These are things that are being developed across as vertical as horizontally. And what we\u2019re seeing is, you know, we\u2019re seeing incremental increase in these. I think what we\u2019re doing is this will be 20 to 30 years in development. And we\u2019ll see it basically come into our lives very, very quickly in many different fields. We\u2019re seeing it today in, for example, the home pods like Alexa and Google and whatever. We\u2019re seeing it in self-driving cars.\r\n\r\nWe\u2019re seeing across some enterprise as well. So it\u2019s not very <strong><em>consumer-y <\/em><\/strong>right now. And that\u2019s why people, I think, are very confused about this. It\u2019s more <strong><em>enterprise-y. <\/em><\/strong>And it\u2019s across enterprises, their back ends using this technology to do better data analytics, data crunching across those different verticals.\r\n\r\nJohnny Irish: Uh, real quick here. Let\u2019s just take a step backwards. Just a little one on one before we jump into some of the details. What is artificial intelligence exactly, and how does it differ from, like, <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a>?\r\n\r\nEmrah: Great question. So, artificial intelligence is the entirety of a new computational method. Machine learning is one of the methods underneath artificial intelligence. So artificial intelligence is, it encompasses the entire gamut of this new computational method but machine learning is part of it. So if you\u2019re talking technically, yes machine learning is the best way to talk about it.\r\n\r\nThere are two components to machine learning: one is training, so you need to train an AI; and then, the second component are the predictions that are called <strong><em>inferencing<\/em><\/strong>. So if you don\u2019t train the AI, you don\u2019t get any <strong><em>inferencing<\/em><\/strong> predictions. So <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a>, it kinda branched out into two things there. To train it, you need to provide labeled images. And that\u2019s what we do with <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a>, we provide, we labeled images saying, \u201cOkay, this is a certain type of cell, or this is a certain type of acid on the ground.\u201d And then, the AI learns it and when it receives new information, it can tag those things.\r\n\r\nJohnny Irish: Okay.\r\n\r\nEmrah: That\u2019s really what AI is about today. It\u2019s a regression tool. It\u2019s not really artificial intelligence. That\u2019s kind of a misnomer.\r\n\r\nJohnny Irish: Mhhm.\r\n\r\nEmrah: It\u2019s a new computational tool. I wish they would have called it that because artificial intelligence gets people kind of edgy.\r\n\r\nJohnny Irish: Yeah, it\u2019s like does artificial intelligence even exist?\r\n\r\nCorey Morgan: When you say artificial intelligence, I think Terminator, you know, literally.\r\n\r\nEmrah: Yeah, and that\u2019s not where we are and I don\u2019t think we\u2019re gonna go there at all.\r\n\r\nJohnny Irish: Yeah, Skynet you know. *laughs*\r\n\r\nEmrah: Yeah, yeah. It\u2019s a new computational tool and the computation is stronger than old computational tools which are like, you know, normal algorithms. What this does is, it does multiple algorithms in a linear regression so it can make predictions on certain things. And the prediction is just a tag, but it\u2019s just a computation. It\u2019s a computer. \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 And so, personally we\u2019re practitioners in AI and we\u2019re saying there\u2019s nothing to be afraid of here at this stage because this is just a new way of computing and, actually, we have no choice. It\u2019s like rejecting electricity or rejecting a car, like, \u201cI\u2019m not gonna drive a car.\u201d\r\n\r\nJohnny Irish: Yeah, right. And that happened back in the day, by the way folks. *laughs*\r\n\r\nEmrah: Yeah. Yeah.\r\n\r\nJohnny Irish: People were against electricity. I mean, people are against cars and they wanted to keep the horse and buggy.\r\n\r\nCorey Morgan: Yeah, and to be honest with you, it\u2019s like rejecting the internet, as you said, in \u201993, \u201994.\r\n\r\nJohnny Irish: Exactly.\r\n\r\nEmrah: Yeah, it is. We have no choice as humans, as, like, in this community, this country, the world. Because if we don\u2019t take this on, like, there\u2019s competition. This is all about efficiency, and if we don\u2019t become more efficient, others will. And we\u2019ll lose.\r\n\r\nJohnny Irish: And when you say that, you\u2019re not talking about corporations. You\u2019re talking about continents and countries.\r\n\r\nCorey Morgan: We\u2019re trying to be the AI superpower. A lot of people think they are and they\u2019re gonna take our technology and move on.\r\n\r\nEmrah: Exactly. It\u2019s between countries. It\u2019s between communities. It\u2019s between organizations, companies. Companies, especially, they need to be much more efficient.\r\n\r\nJohnny Irish: Mhhm.\r\n\r\nEmrah: Those who become efficient will take over the market. And that\u2019s why you see this race with self-driving cars. Right?\r\n\r\nJohnny Irish: Mhmm.\r\n\r\nEmrah: Where you have, like, people pouring billions into self-driving because the moment you have a self-driving car, you\u2019ve taken over that market and you\u2019ve taken over the entire market as well.\r\n\r\nJohnny Irish: Yeah, so we have touched on this and, not to take you off topic, we have touched on this a couple of shows ago regarding the self-driving cars and the technology, the infrastructure on the actual highways where the cars can talk to each other, you know, like, \u201cthis car just switched lanes.\u201d It becomes much more than the car, in my opinion, it becomes the environment that the car is driving in. And that has to be \u201csmart\u201d, for lack of a better word, as well. Like a smart highway. Am I wrong in that?\r\n\r\nEmrah: It\u2019s correct. So what we\u2019re doing on the self-driving car, part of this, a lot of this is usually AI as well. So remember, you have to teach the car what\u2019s allowed. What do the pedestrian look like? What\u2019s the \u201cStop\u201d sign? What\u2019s a tree? Is it a rabbit or is it a cat? I mean, it may not make a difference, but the more detail you have, the better it can discern between things.\r\n\r\n[caption id=\"attachment_707\" align=\"alignright\" width=\"336\"]<img class=\"size-full wp-image-707\" src=\"\/wp-content\/uploads\/2023\/06\/smarter-cars-with-ai.png\" alt=\"Smarter Cars with AI\" width=\"336\" height=\"280\" \/> Smarter Cars with AI[\/caption]\r\n\r\nSo these cars that you see, they\u2019re collecting information, visual data. And that visual data are annotated, labeled, and it goes into a training system. And then the training system goes back and it\u2019s deployed into the cars. So it\u2019s like this circular thing. And right, the highways need to be smarter but it\u2019s really the cars that need to be a lot smarter and need to be talking to each other. And that, that exists today. The science of that exists. The problem is, how do you get that to the engineering? And then, how do you make a product out of it and how do you distribute it?\r\n\r\nJohnny Irish: Yeah.\r\n\r\nEmrah: So the science of this had existed for about 34 years but it\u2019s nothing near. But how do you really deploy it? How do you engineer it and then you create a product?\r\n\r\nJohnny Irish: Yeah. How do we perfect it at this point?\r\n\r\nEmrah: Exactly. Exactly.\r\n\r\nJohnny Irish: You know, one thing we\u2019ve been, you know, and this is how, why we were introduced. And the point of tonight\u2019s show and the individual segments is, we came into this as, you know, everyone\u2019s collecting our data and how do we do. But, one thing that we had a discussion, you know, Matt and I, my board up here, we were just talking about it and we mentioned it on the earlier segment is in regards to Alexa. Even though she\u2019s listening to you, she\u2019s not listening because it\u2019s an evil thing. She\u2019s listening because she need to learn those words. It\u2019s more educational for her than data sharing. Am I correct with that?\r\n\r\nEmrah: It is. It is. And unfortunately, we have this stigma against AI because we called AI and that\u2019s not what\u2019s happening here. Of course what happens is, it\u2019s a very powerful tool, right? And so what people are really afraid of here is some people having access to it and others don\u2019t. So it\u2019s kinda like the \u201chave\u201d and the \u201chave not\u201d, and the have\u2019s trying to take over the world and do evil things.\r\n\r\nAnd what we really need to do is create equality on the availability of AI to everybody. And that you need some sort of checks and balances there so that okay I have this tool but okay my neighbor also has it.\r\n\r\nJohnny Irish: Yeah.\r\n\r\nEmrah: There\u2019s a certain check there. And I think that, honestly, it\u2019s totally, totally for efficiency. We are highly inefficient right now. We need to become more efficient, which means creating more welfare. It creates more welfare for everybody.\r\n\r\nJohnny Irish: Mhhm.\r\n\r\nEmrah: I\u2019ll go back to this thing where London has 500,000 CCTVs.\r\n\r\nJohnny Irish: Yeah. Yeah.\r\n\r\nEmrah: 500,000 cameras. New York has 9000. I\u2019m like, \u201cWhy did that happen?\u201d Well, it\u2019s because of the IRA.\r\n\r\n[caption id=\"attachment_708\" align=\"alignright\" width=\"336\"]<img class=\"size-full wp-image-708\" src=\"\/wp-content\/uploads\/2023\/06\/safety-with-facial-authentication.png\" alt=\"Safety with Facial Authentication\" width=\"336\" height=\"280\" \/> Safety with Facial Authentication[\/caption]\r\n\r\nJohnny Irish: Ugh. You\u2019ve read my mind. You\u2019ve read my mind.\r\n\r\nEmrah: Right. And they go, \u201cHey listen, I\u2019m gonna put up these cameras.\u201d They put up the cameras, the bombing stopped. I\u2019d rather have a camera than a bomb in my neighborhood.\r\n\r\nThat\u2019s really the reality here. And whether they are viewing us, or, like, listening that\u2019s a different case but I think, those types of stories we need to put out there so that people feel safer and adapt.\r\n\r\nJohnny Irish: Yeah, I don\u2019t really want to get into the CCTV thing because that where we\u2019ll get into <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a> and that brings us back on the negative side, too much information and lack of privacy. But on the pro side, and this is my opinion and what I would see would be valuable for the world and our country and municipalities. When you have the likes of a Tesla, doing what they\u2019re doing. Now, everyone else is trying to catch up \u2013 Ford, Hyundai. I think that information, even though these companies are competing with each other for consumer, for self-driving car, or that I can park itself, or, you know, it\u2019s gonna automatically slow down. I think, all that data that it collects should be open source because, guess what, we just collected all that data and it\u2019s good for the consumers so Ford can use it too, you know?\r\n\r\nEmrah: Yeah, and a lot of these companies are doing that. So you have OpenAI, for example, and that is like a consolidation of all these different tools and information and data that people collect from different companies and Tesla\u2019s actually a part of it. At Chooch, we\u2019re also opening up our dataset so we have 200,000 pre-training qualifications, we have over 150 models running and that\u2019s also open to the public. So you\u2019re right about that, that it should be public. I think we should go back to its equity.\r\n\r\nJohnny Irish: Yeah. I mean, it should be mandatory from the government, you know what, you collected all this data with a consumers\u2019 car that you\u2019re just accessing. It\u2019s not even your own car anymore, you sold it. And that should just be, you know, I\u2019m not saying to reveal trade secrets but if there\u2019s a pothole on the 836, share it to the Ford and Hyundai people.\r\n\r\nEmrah: Everyone should know. Everyone should know\r\n\r\nJohnny Irish: Yeah.\r\n\r\nEmrah: There\u2019s positive externalities on that and that creates welfare for everybody.\r\n\r\nJohnny Irish: I have a quick question,\r\n\r\nEmrah: Yeah.\r\n\r\nJohnny Irish: Why Chooch <em>(kh-ootsh)<\/em>? How did you come up with the name Chooch <em>(kh-ootsh)<\/em>?\r\n\r\nEmrah: It\u2019s Chooch <em>(tsh-ootsh) <\/em>actually.\r\n\r\nJohnny Irish: I\u2019m sorry. I\u2019m sorry.\r\n\r\nEmrah: *laughs* Khooch is something else but Chooch is a mixture of choose and search.\r\n\r\nJohnny Irish: Aaahh.\r\n\r\nEmrah: It\u2019s actually the future of search and because we\u2019re gonna have these glasses on the future and that would be able to understand what\u2019s happening around you in more detail so you look at something you\u2019re eating and it\u2019ll tell you how many calories there is in it and what\u2019s in it. And you\u2019ll look at, like, a car and it\u2019ll tell you where you can buy it or, like, all the details of it.\r\n\r\nJohnny Irish: Yeah. Yeah.\r\n\r\nEmrah: So it\u2019s kind of like the future of search and it also means dummy, idiot in Italian dialect.\r\n\r\nJohnny Irish: Those are the people that say it wrong, though, right? *laughs*\r\n\r\n*everyone laughs*\r\n\r\nEmrah: And AI is a dummy. It\u2019s a poor reflection of humanity and there\u2019s also that kind of play on words on that so that\u2019s why we called it Chooch.\r\n\r\nJohnny Irish: So let me ask you a question, I hate to say artificial intelligence, can computational intelligence compete with human intelligence and\/or what are the ways that they can\u2019t? What are we finding out?\r\n\r\nEmrah: Yeah, great question. So what we\u2019re looking at here is very basic computational capability that\u2019s akin to human understanding but very, very light. So to your nose, \u201cOh, this is an apple\u201d, \u201cThis is a pear.\u201d That\u2019s all it does is it tags. The way it becomes more intelligent than a human is because it works 24\/7 and you can proliferate it.\r\n\r\nSo that\u2019s where the whole thing is. It\u2019s not smarter than a human but it works 24\/7 and you can just basically, like, it scales to infinity. That\u2019s where it\u2019s better than a human, better than human. It\u2019s like a calculator, you know. Humans know how to calculate to but it takes us some time to do it and, you know, one person at a time, well we can do one topic at a time. Here, you have these can basically put in 0.01 seconds and, like, it\u2019s all over the place. So in that sense, much better than a human. On the other hand, it\u2019s not really that deep and intelligent because, I mean, we\u2019re very, very complex beings. What the machine is doing is basically one or two layers of computational understanding and we\u2019re nowhere near that, probably not for the next 70,70, hundred years.\r\n\r\nJohnny Irish: It only knows what you feed it, for the most part. Correct? So behind it, there\u2019s always somebody putting data into it?\r\n\r\nEmrah: Exactly. There\u2019s a human putting data into it or some stream of data coming in and it\u2019s not intelligent in the way it assesses it. It just gives you tags or, like, alerts. It doesn\u2019t really understand context that well. There isn\u2019t that type of understanding. So, you don\u2019t have to be afraid of that. What we have to be concerned about is, okay well, you can do this but this AI can be like, you can basically scale it too infinity now.\r\n\r\nJohnny Irish: How do you think quantum computing is gonna change that?\r\n\r\nEmrah: Quantum computing is interesting and we\u2019re in the very early stages of that too. Remember, AI, all these deep learning frameworks, <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a>, they require a lot of compute power and this compute power didn\u2019t exist 5 year ago. This is very, very new. And it came on with the GPU servers, which are graphic processing units of NVIDIA. They\u2019re basically used for gaming. They develop these chips for gaming.\r\n\r\nAnd suddenly, they saw, well this is interesting, we can also use it for these types of computations. So that kind of increased the onboarding of AI, so that one with the GPUs and the next stage of this would the quantum computing. So if I want to run millions of models at the same time, then, you know, you\u2019ll need that type of compute power to do that. That\u2019s one of the limiting factors today. Many, many layers of computation that for you to do it, <a href=\"https:\/\/www.chooch.com\/\">Chooch AI<\/a> would do image quantification, object detection, segmentation, <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a>, authentication and all that kind of stuff. You put these in layers and you put it on a machine, it\u2019s very heavy for the machine. So to be able to get to the next level of that, yes quantum computers are gonna be crucial for better AI.\r\n\r\nJohnny Irish: Now, tell us about your company a little. Who is your target audience? Are you government? Main industry? I mean, I doubt, you know, the regular guy would call you up. Or are you more of research and development? How does it work?\r\n\r\nEmrah: Yeah. We\u2019re a B-B enterprise. So we\u2019re based of Silicon Valley, what we do is we clone human visual intelligence into machine. And that\u2019s across many, many verticals so we do have government clients, but we also have many, many commercial clients as well. And a lot of our clients are looking for these types of solutions to increase the efficiency of what they\u2019re doing. So let\u2019s say, you know, checking and understanding movements in operating room, basically for compliance for safety. Does everyone have their hard hats on? Does everyone have their gloves on? And it\u2019s stuff like that where you have compliance issues, so we\u2019re working on a lot with that and also researches for client discovery as well. So basically being able to understand the different cells.\r\n\r\nWhat\u2019s happening in these biomedical labs and understanding the interaction between different elements over there. So, it\u2019s really B to B, <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a>, government, spacial, and <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security and safety<\/a>, it\u2019s really what we do.\r\n\r\nJohnny Irish: So you guys do a lot of reaching out to these organizations saying, \u201cYou know what guys, here\u2019s the technology that you need.\u201d Compared to them saying, \u201cHey, you have anything new today?\u201d Am I correct with that?\r\n\r\nEmrah: Well, it\u2019s usually people approach us with their problems already. Saying, \u201cUh we have a defined issue.\u201d Like, \u201cHey, I wanna speed up the checking process,\u201d \u201cHey, I need my OSHA compliance.\u201d Or, you know, \u201cPeople, I need to make sure are wearing their hard hats.\u201d\r\n\r\n[caption id=\"attachment_709\" align=\"alignright\" width=\"336\"]<img class=\"size-full wp-image-709\" src=\"\/wp-content\/uploads\/2023\/06\/surgical-outcomes-with-ai.png\" alt=\"Surgical Outcomes with AI\" width=\"336\" height=\"280\" \/> Better Surgical Outcomes with AI[\/caption]\r\n\r\nJohnny Irish: Let\u2019s take a step back on that note. Sorry, I just want to take a step back because one thing you briefly mentioned that I was intrigued about was movements in surgery room.\r\n\r\nEmrah: Yeah. Yeah. So when does the surgeon walk in? When does the patient come in? When does anesthesia start? Stop?\r\n\r\nWhat goes into the surgical cavity? What comes out? So all that kind of stuff is what we\u2019re doing as well. Yeah. And it\u2019s more compliant and also assuring best practice so you might say, okay, we collect all these data, and then they do number crunching on them and then basically say, \u201cHey there\u2019s something going on here which can be practiced all over the place.\u201d So, we\u2019re able to understand those points and to bring it to the market.\r\n\r\nJohnny Irish: Yeah, and I know a lot are already into this, you know, our guys in the North East, as soon as you mentioned OSHA, it\u2019s Ah. Gotyah. *laughs*\r\n\r\nCorey Morgan: *laughs* Yeah. \u201cNow we need you, how do we get hold of you?\u201d\r\n\r\n*all laugh*\r\n\r\nJohnny Irish: Fellas, we\u2019re running out of time here. We definitely want to have you back on as all our clients, Corey, I and Matt and this station itself were very lucky to get very educated individuals and successful people like yourself. But for the audience, our OSHA customers, and listeners, how do they get a hold of you? And you know, tell us, the floor is yours for a minute to just tell everyone who you are, what you do and how to find you.\r\n\r\n \r\n\r\nEmrah: Yeah. So, uh, thank you guys for this. Basically, we\u2019re Chooch AI. We\u2019re a visual AI company. We clone visual human intelligence into machines so that humans don\u2019t need to do it anymore and basically get more work done doing it. We\u2019re Silicon Valley based. We\u2019re in San Mateo. Follow at <a href=\"https:\/\/www.chooch.com\/\">www.chooch.com<\/a> is where you can find us. Reach out. You know, we\u2019re very open people. We\u2019re trying to educate you about what AI is all about and see how it can help you with your enterprises and make you a more efficient, productive company because you need to get on the train now. The reason is if you don\u2019t, then your competitors are. And it\u2019s a hundred, a thousand x more efficient depending on your use cases.\r\n\r\nJohnny Irish: Now if they mention 880thebiz.com, do they get a 20% off discount?\r\n\r\nEmrah:\u00a0 Whatever you want.\r\n\r\n*Everyone laughs*\r\n\r\nJohnny Irish: No, I\u2019m just kidding. *laughs continue*\r\n\r\nEmrah: Anything for you guys. *laughs*\r\n\r\nCorey Morgan: Alright.\r\n\r\nJohnny Irish: Alright. Emrah, thank you so much for taking the time with us. And I know, I\u2019ve been speaking to your people, spoke to you, obviously, earlier in the week. I know you\u2019re a busy guy, and it\u2019s valuable time. Cory, myself, and Matt, we, again, we can\u2019t thank you enough for this, really fun and educational and we\u2019d definitely have you back.\r\n\r\nCorey Morgan: Have a great evening.\r\n\r\nEmrah: Thank you, Corey. Thank you, Johnny. Thank you, Matt. Really appreciate it. Thank you.\r\n\r\nJohnny Irish: The pleasure was ours. Have a great evening. Bye bye.\r\n\r\nEmrah: You, too. Thank you. Bye bye.\r\n\r\nJohnny Irish: Alright, everybody. As we told you, we were gonna bring you segments and, you know, one thing we loved doing, we\u2019ve discussed it ourselves, having four guests come in with different topics, the same industry but different sides of it. I mean, we went from data security to data erasure, cyber defense globally, and then back to pro-AI. I mean that couldn\u2019t be any more perfect show.\r\n\r\nCorey Morgan: And folks, if you have any ideas for the show or there\u2019s something that you wanna hear go to <a href=\"mailto:inquiries@cjradioshow.com\">inquiries@cjradioshow.com<\/a>.\r\n\r\nJohnny Irish: And don\u2019t forget our <a href=\"https:\/\/twitter.com\/thecjradioshow\" target=\"_blank\" rel=\"noopener noreferrer\">Twitter Account @thecjradioshow<\/a>\r\n\r\n ",
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"post_content": "<a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">Health and safety<\/a> compliance has many benefits for both employees and employers. It increases the safety of employees from workplace hazards and reduces the cost of accidents and non-compliance for employers. Chooch AI customers enjoy these benefits when they use the <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a> on computer vision AI models.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/J_gPM32KZnI\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nHow AI models for health and safety work are:\r\n<ul>\r\n \t<li>Chooch ensures our AI models are trained to detect hazardous actions.<\/li>\r\n \t<li>Chooch runs on the <a href=\"https:\/\/www.chooch.com\/blog\/edge-ai-platform-essentials\/\">edge AI platform<\/a> of existing video streams.<\/li>\r\n \t<li>It detects hazardous actions such as smoking, falling, and unauthorized people entering a building. When it detects unauthorized action, it sends an alert to managers for remedial action.<\/li>\r\n<\/ul>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-in-healthcare\/\">Health and safety detection<\/a> models are useful in:\r\n<ul>\r\n \t<li>Industries that handle hazardous materials.<\/li>\r\n \t<li>Any other organization that prohibits smoking or where it can cause serious damage.<\/li>\r\n<\/ul>\r\nChooch\u2019s health and safety detection <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">artificial intelligence models<\/a> are pre-trained and ready for deployment. We continue training models remotely after we have successfully deployed them. We can also deploy custom models for partners with specific needs, along with in-depth computer vision consulting. Are you interested in learning more about how AI models detect health and safety compliance? <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">Contact<\/a> Chooch AI today.",
"post_title": "AI Action Detection: Computer Vision Demo for Health and Safety",
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"post_date": "2023-01-18 08:47:05",
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"post_content": "Deploying IoT involves the installation of infrastructure, specific sensors, computers and cameras. However, to successfully deploy IoT this must also include real-time analytics, and introducing <a href=\"https:\/\/www.chooch.com\/blog\/the-value-of-edge-ai\/\">AI technologies<\/a> into the mix creates AIoT.\r\n\r\nAI on the network edge detects and reports on events detected by sensors and results in four distinct benefits.\r\n<ul>\r\n \t<li>Real Time Analytics<\/li>\r\n \t<li>Fully Deployed Intelligence<\/li>\r\n \t<li>Combining AI Technologies<\/li>\r\n \t<li>Completing the Lifecycle<\/li>\r\n<\/ul>\r\n<img class=\"size-full wp-image-1611\" src=\"\/wp-content\/uploads\/2023\/06\/improved-computer-vision-ai.png\" alt=\"Radically Improved Computer Vision with AI\" width=\"1200\" height=\"360\" \/>\r\n<h2>Real-Time Analytics<\/h2>\r\nEvent stream processing analyzes different types of data and is able to successfully identify which data is relevant. To handle such data, event stream processing can:\r\n<ol>\r\n \t<li><strong>Identify important events and prompt the necessary action:\u00a0<\/strong>Detection of all relevant events or events that are of some interest is possible through event stream processing. These include unusual activities during bank transactions or actions on mobile.<\/li>\r\n \t<li><strong>Constantly monitor the information that is being gathered:\u00a0<\/strong>Event stream processing can quickly detect any irregularities that can become potential problems. If such situations arise, smart devices can immediately alert the concerned operator and use corrective measures.<\/li>\r\n \t<li><strong>Make sure that the sensor data is clean and authentic:\u00a0<\/strong>You might find certain inconsistencies in the sensor data. This can be due to network errors or even dirty data. Data streams have techniques to check for discrepancies and troubleshoot if required.<\/li>\r\n \t<li><strong>Improve operations in real-time:\u00a0<\/strong>Optimization of operations in real-time is possible with advanced algorithms. For example, the arrival time of a train can be constantly updated, especially if there is a delay in any particular station.<\/li>\r\n<\/ol>\r\n<h2>Using Intelligence Where the Application Requires It<\/h2>\r\nData is constantly being generated by AIoT devices. Therefore analytics should be applied in different ways to get the best possible outcome. These different methods of deploying intelligence include:\r\n<ol>\r\n \t<li><strong>High-performance analytics:\u00a0<\/strong>Heavy performance analytics can be deployed on data that is not moving or is in the storage. It can be also used when the data is in the cloud.<\/li>\r\n \t<li><strong>Streaming analytics:\u00a0<\/strong>When large amounts of moving data need to be analyzed for a few items of interest, streaming analytics should be used. Streaming analytics can also be used if the speed is critical and alerts for an imminent crash or component failure that needs to be sent.<\/li>\r\n \t<li><strong>Edge computing:\u00a0<\/strong><a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge computing<\/a> immediately triggers necessary action on any data. It does not wait to ingest, store or move data anywhere without acting on it first.<\/li>\r\n<\/ol>\r\n<h2>Combining AI Technologies<\/h2>\r\nA combination of <a href=\"https:\/\/www.chooch.com\/blog\/the-value-of-edge-ai\/\">AI technologies<\/a> can provide many opportunities and the best outcome. For example, machine learning, language processing, and computer vision can happen simultaneously.\r\n\r\nHere\u2019s an example\/ <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Deep learning and computer vision<\/a> can be used by clinics or hospitals for accurate radiographs, CT scans, and MRIs. To build patient profiles, detailing the family history of medical issues, natural language processing can be easily used along with computer vision to make the data far more accessible and accurate.\r\n<h2>Unifying the Complete Analytics Life Cycle<\/h2>\r\nTo predict what will happen and to analyze what is happening in real-time, <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI systems<\/a> should have access to various kinds of data. If IoT is successfully implemented, the AI systems will be able to link all the following capabilities:\r\n<ol>\r\n \t<li><strong>Data analysis on the fly:\u00a0<\/strong>This is event stream processing where large amounts of data are analyzed to find any relevant information.<\/li>\r\n \t<li><strong>Real-time decision making:\u00a0<\/strong>In case of data in motion or streaming data, if an event of interest occurs then immediately the necessary action should be triggered.<\/li>\r\n \t<li><strong>Big data analytics:\u00a0<\/strong>Large amounts of data can be ingested and processed when intelligence is obtained from IoT. This usually happens in a computing environment and running more iterations and using all of the data can also improve the precision of the model.<\/li>\r\n \t<li><strong>Data management:\u00a0<\/strong>Proper data management can clean and validate all kinds of data even when it is available in different formats.<\/li>\r\n \t<li><strong>Analytical model management:<\/strong>\u00a0Analytical model management is consistent and covers everything, from registration to retirement. The evolution of models can also be tracked and the performances are constantly improved.<\/li>\r\n<\/ol>\r\n<img class=\"size-full wp-image-1579\" src=\"\/wp-content\/uploads\/2023\/06\/visual-ai-solutions-case-studies.png\" alt=\"Visual AI Solutions Case Studies\" width=\"1200\" height=\"420\" \/>\r\n<h2>Ready to deploy AIoT?<\/h2>\r\nChooch exports inference engines and installs computer vision on devices for\u00a0<a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">Edge AI<\/a>. We can provide a complete solution for your use case. Please get in touch or\u00a0<a href=\"https:\/\/app.chooch.ai\/feed\/sign_up\">Start Now<\/a>.",
"post_title": "AIoT: Four Keys to Edge AI + IoT Deployments",
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"post_date": "2023-01-18 08:45:24",
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"post_content": "Facial authentication\u00a0is a great way to protect virtual content.But like all security features, it too has some dangerous loopholes.\r\n\r\nWith enhanced camera features, 2D or 3D printers, or animation, it is very easy to create fake images that can pass for an actual face. These are what are known as presentation attacks. Preventing presentation attacks is where\u00a0liveness detection\u00a0comes in, and now it\u2019s easier than ever to implement with the\u00a0<a href=\"https:\/\/www.chooch.com\/api\/\">Facial Recognition API\u00a0from\u00a0Chooch<\/a>.\r\n\r\nWhile facial recognition is a good tool for authentication, liveness detection algorithms take care of its vulnerabilities and make sure that these biometric modalities are not compromised in any way.\r\n<h2>Presentation Attacks<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">Facial recognition<\/a>, while being a very useful biometric modality, is susceptible to attacks and attempts by fraudsters to destroy its security measures. These attacks are known as presentation attacks or \u201cspoofs\u201d. To get past the biometric security measures and protection systems placed, a fraudster will provide a non-live image, false printed or digital photograph. Videos or masks are also used to impersonate a particular person and assume a fake identity.\r\n\r\nPresentation attacks are usually of two types and these types depend upon the kind of result the fraudster wishes to cultivate.\r\n<ol>\r\n \t<li>False Match<\/li>\r\n<\/ol>\r\nIn a one-to-one biometric comparison, if the fraudster is successful, it is a false match as the fraudster has been able to avoid detection by providing a sample image of the targeted victim. Once this happens, the fraudster will be able to go through the victim\u2019s account, having access to all the applications.\r\n<ol start=\"2\">\r\n \t<li>False Non-Match<\/li>\r\n<\/ol>\r\nIf the fraudster creates one account or multiple new accounts of the victim by using an image that will not work in a biometric watch list (especially if the facial features are somehow masked) or duplicate search, it is a false non-match. These false non-matches are very difficult to identify and track as there are too many such accounts.\r\n\r\nTo avoid these attacks, liveness detection is very important. In mobile onboarding, the risk of such attacks or attempts of fraudsters will remain, but this can be prevented if liveness detection algorithms are used.\r\n\r\nThese algorithms can easily select false, non-live images, even if they cannot be used in biometric watch list searches. Liveness detection algorithms can be easily combined with multimodal biometrics, for example, voice recognition, and this strengthens the <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">security measures<\/a>. If these precautions are not taken, then <a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">facial recognition<\/a> will not be secure from spoofs or presentation attacks.\r\n<h2>Different Techniques for Liveness Detection<\/h2>\r\nThe primary job of liveness detection techniques is to provide the maximum security possible and to prevent fraudsters from taking advantage of the existing biometric modalities.\r\n\r\nLiveness detection techniques are not meant to compromise the user\u2019s experience of using an application, in any way. The different techniques implemented try to minimize interaction with the user so that there are no interruptions that can affect the usage of the application.\r\n<ol>\r\n \t<li>Active Liveness Detection<\/li>\r\n<\/ol>\r\nAs the name suggests, this form of liveness detection algorithm requires the user to be active in some. The user might need to wink, smile or shake his or her head. While this prompts a certain level of interaction, the advantage of active liveness detection is that the user will be completely aware during the process.\r\n<ol start=\"2\">\r\n \t<li>Passive Liveness Detection<\/li>\r\n<\/ol>\r\nThe technique depends upon algorithms that can detect if any part of the image is false. These algorithms check discrepancies in the image, for example, masks, any kind of distortion, or different textures. Passive liveness detection happens in the background and as it is not even visible to the user, fraudsters find it difficult to get through.\r\n<ol start=\"3\">\r\n \t<li>Hybrid<\/li>\r\n<\/ol>\r\nA hybrid liveness detection technique, while not interacting with the user is not exactly opaque. Therefore, it can still be detected by fraudsters and evaded.\r\n<h2>How are Liveness Detection Products Certified Against Presentation Attacks?<\/h2>\r\nTo certify a liveness detection product, its ability to perform is tested. These tests usually involve the use of spoofs to try and get across the biometric security measures.\r\n\r\nIf the liveness detection product is successful in outing these spoofs, and if its performance is according to international standards, then the product is certified. However, these tests might have different settings that might change after production or the spoofed content used might not cover the range of attacks that the product may have to face.\r\n\r\nThere are also some tests that are not of much use in mobile onboarding and the performance of the product in such cases becomes rather irrelevant.\r\n\r\nNow, if one tries hard enough, it is possible to overcome any security measures, even those of liveness detection. Therefore, before a liveness detection product is certified, it has to go through rigorous evaluation procedures. This will ensure that all possible vulnerabilities are covered.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/blog\/facial-recognition-in-business-5-amazing-applications\/\">Facial Authentication\u00a0from\u00a0Chooch AI<\/a> has been tested by partners and was found to be spoofproof with liveness detection. If you\u2019d like to do your own testing, please create an account at\u00a0<a href=\"https:\/\/www.chooch.com\/api\/\">Chooch\u00a0and install our API<\/a>. More about Liveness Detection for presentation attacks is in our\u00a0<a href=\"https:\/\/www.chooch.com\/api\/#custom-facial-recognition-and-authentication-api\">API Documentation<\/a>.",
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"post_date": "2023-01-18 08:33:15",
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"post_content": "Proper social distancing increases public and <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">workplace safety<\/a> by reducing the risk of spreading COVID-19. Our AI model associated with social distancing has several benefits. It reduces the costs associated with COVID-19 infection, lowers risk for people in the area, ensures legal compliance, and helps save lives.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/M3jCu5PPZII\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nSocial distancing AI uses the following methods:\r\n<ul>\r\n \t<li>Chooch AI trains AI models to measure the distance between two people.<\/li>\r\n \t<li>We set parameters to determine whether the distance is safe.<\/li>\r\n \t<li>Intelligent video analytics work with existing video systems to detect the distance between two or more people in the video feed. It\u2019s easy to add Chooch AI to <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge devices<\/a> and cameras.<\/li>\r\n \t<li>The visual AI from Chooch can accurately detect if there is social distancing between the people in the video feed. If not, we send an alert to relevant personnel.<\/li>\r\n<\/ul>\r\nSocial distancing AI is crucial in ensuring public health safety in areas that have the potential to be crowded, such as offices, malls, train stations, busy streets, schools, and airports. After we have deployed a social distancing <a href=\"https:\/\/www.chooch.com\/blog\/what-is-an-ai-model\/\">AI model<\/a>, Chooch AI provides remote training and computer vision consulting. If a partner has specific needs, we can deploy a custom model. <a href=\"https:\/\/www.chooch.com\/contact-us\/\">Contact Chooch AI<\/a> to discuss your social distancing AI project.",
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"post_date": "2023-01-18 08:31:19",
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"post_content": "Fires can cause devastating damage within a short time. <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">Computer vision<\/a> from Chooch can save buildings, billions of dollars, and countless lives. It allows for a faster response which reduces the damage to property or loss of life. These benefits far outweigh the minimal cost of AI model deployment.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/6Ky8VDbq540\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nWe can deploy fire detection models on:\r\n<ul>\r\n \t<li>Satellites<\/li>\r\n \t<li>Drones<\/li>\r\n \t<li>Land cameras<\/li>\r\n<\/ul>\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/wildfire-detection\/\">AI fire detection<\/a> uses pre-trained AI models that can \"see\" fire using <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">computer vision<\/a>. These AI models process live video feeds and can detect a fire instantly. Once the artificial intelligence model detects a fire, it sends an instant alert to the necessary authorities with the exact coordinates. This helps local personnel quickly find and extinguish a fire, drastically reducing the amount of damage caused.\r\n\r\nWe can deploy <a href=\"https:\/\/www.chooch.com\/\">computer vision AI models<\/a> in factories, offices, forests, and any other area where the risk of fire is high. After Chooch AI deploys an AI model successfully, we provide remote training. If a partner has specific needs, we can deploy a custom model. Ready to <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">learn more<\/a> about how AI models detect fire?",
"post_title": "AI Models for Wildfire Detection with Computer Vision: Deployable Now",
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"post_date": "2023-01-18 08:29:54",
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"post_content": "Trying to count a large number of assets spread over a large area can be a challenge \u2014 and prone to errors. That\u2019s why Drone AI is useful for <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> customers, as it helps them get an accurate count of their most important assets. Chooch AI models are pre-trained and ready for immediate deployment. <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-security-robotics-and-drone-ai-for-the-security-industry\/\">Drone AI<\/a> can identify any object that we have trained it to identify, whether it's animals, people, or objects. It is cheaper, safer, and more accurate than traditional methods of counting numerous assets.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/9tLCFbupeOI\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nThese benefits outweigh the costs of deployment.\r\n\r\nDrone AI processes feeds from:\r\n<ul>\r\n \t<li>Security cameras<\/li>\r\n \t<li>Drones<\/li>\r\n \t<li>Satellites<\/li>\r\n \t<li>Microscopes<\/li>\r\n<\/ul>\r\nOnce our drone <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> has captured the data, it provides an accurate count of the people, animals, or objects in the feed by:\r\n<ul>\r\n \t<li>Identifying the object or animal it's trained to identify.<\/li>\r\n \t<li>Counting the object.<\/li>\r\n \t<li>Sending the data back to decision makers.<\/li>\r\n<\/ul>\r\nThanks to its <a href=\"https:\/\/www.chooch.com\/platform\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a>, the Drone AI model is useful in the following settings:\r\n<ul>\r\n \t<li>Large-scale farms need to keep track of large herds in a large area.<\/li>\r\n \t<li>National parks, wildlife reserves, and wildlife conservancies need to have an accurate count of the animals that they have.<\/li>\r\n \t<li>Large warehouses that must keep track of thousands of boxes and products.<\/li>\r\n \t<li>Laboratories for cell counting.<\/li>\r\n<\/ul>\r\nAfter Chooch AI deploys a <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-security-robotics-and-drone-ai-for-the-security-industry\/\">drone AI<\/a> model successfully, we provide remote training. If a partner has specific needs, we can deploy a custom model. Our drone AI models can be trained within days and deployed within hours. <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">Contact us<\/a> today to learn more about Drone AI models for animal counting.",
"post_title": "Drone AI: Counting Animals With Computer Vision Demo",
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"post_date": "2023-01-18 08:26:36",
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"post_content": "Increase operational efficiency, reduce work stoppages and downtime, and enhance <a href=\"https:\/\/www.chooch.com\/solutions\/workplace-safety\/\">worker safety<\/a> with the power of computer vision AI. Chooch\u2019s powerful <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\" target=\"_blank\" rel=\"noopener\">computer vision platform<\/a> can enable manufacturers and consumer-packaged goods companies to streamline and enhance visual inspection tasks in new and powerful ways.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/ld56DGJQhnc\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\nVisual inspection has always been critical in manufacturing. This intensive, demanding, and time-consuming task can tax even the most highly trained and conscientious human observer. Computer vision AI doesn\u2019t blink, get bored or distracted, will never fail at <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-defect-detection\/\">defect detection<\/a> or overlook other critical issues, protecting your operation from costly accidents or errors.\r\n\r\nPartnering with Chooch AI not only provides outstanding value in helping you overcome manufacturing challenges and preventing damaged or poor-quality products from being shipped, but it also helps protect your workers from injury.\r\n\r\n<a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">Computer vision for manufacturing<\/a> can not only detect the presence of hard hats, eye and ear protection, gloves, and other safety equipment, but it can also monitor worker movement and location and alert workers who may have overlooked safe operating distance guidelines.\r\n\r\nChooch AI can help you rapidly deploy and implement a solution for the unique needs of your organization by implementing computer vision for <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-defect-detection\/\">defect detection<\/a> and designed to monitor, analyze, and detect any number of criteria through any number of cameras.\r\n\r\nLearn more about <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">computer vision in manufacturing<\/a> or more specifically read this case study about defect detection in bottling. Or read the<a href=\"https:\/\/info.chooch.com\/hubfs\/pdfs\/ai-in-manufacturing-ebook.pdf?__hstc=113074139.f77b90cfb712429f39082a80be0e8412.1671140693943.1674588135002.1674593019654.56&__hssc=113074139.67.1674593019654&__hsfp=1855668024\"> defect detection whitepaper<\/a>.\r\n\r\nIf you have any other questions about how this powerful and transformative technology can help you, <a href=\"https:\/\/www.chooch.com\/contact-us\/\" target=\"_blank\" rel=\"noopener\">contact Chooch today<\/a>.",
"post_title": "Manufacturing Computer Vision for Defect Detection and More",
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"post_date": "2023-01-18 08:23:28",
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"post_content": "Industrial companies can detect and monitor leaks accurately using <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\" target=\"_blank\" rel=\"noopener\">Computer Vision<\/a> from Chooch AI by deploying AI onto an edge device. These systems are useful in detecting potential environmental hazards, safety threats, and remote assets that require maintenance. <a href=\"https:\/\/www.chooch.com\/blog\/computer-vision-defect-detection\/\">Leak detection<\/a> provides industrial companies with the ability to monitor key infrastructure in remote areas safely, accurately, and cost-effectively.\r\n\r\n<iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/eSBTE9G8-P4\" width=\"100%\" height=\"470\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe>\r\n\r\n<a href=\"https:\/\/www.chooch.com\/imagechat\/\">Chooch AI\u2019s Leak detection models<\/a> can catch the spillage or leakage of hazardous materials fast, preventing contamination and environmental pollution. If not monitored properly, hazardous leaks can cause pollution, loss of key assets, and fines from regulatory authorities. Deploying leak detection AI saves companies the time, cost, and risks associated with sending human inspectors to remote sites. Chooch AI models can detect leaks using computer vision through the following methods:\r\n<ul>\r\n \t<li>Smart cameras installed in remote locations and connected to the Internet of Things (IoT).<\/li>\r\n \t<li>Drones that collect visual data in the air.<\/li>\r\n \t<li>Satellite images.<\/li>\r\n \t<li>PPE detection<\/li>\r\n \t<li>Object recognition<\/li>\r\n \t<li>Image recognition<\/li>\r\n<\/ul>\r\nOnce the drone captures the image, Chooch AI:\r\n<ul>\r\n \t<li>Detects leaks and spillages.<\/li>\r\n \t<li>Counts every drop.<\/li>\r\n \t<li>Counts how many times a leak\/spill happens in a 24-hour period.<\/li>\r\n \t<li>Distinguishes between hazardous and non-hazardous spills.<\/li>\r\n \t<li>Sends alerts, reports, and metrics back to decision-makers.<\/li>\r\n<\/ul>\r\nCompanies in the utility, energy, construction, and other sectors that have remote assets requiring constant monitoring and maintenance can deploy leak detection and remote site monitoring.\r\n\r\nChooch AI\u2019s <a href=\"https:\/\/www.chooch.com\/imagechat\/\">leak detection AI models<\/a> ready for deployment. After successful deployment of an AI model onto <a href=\"https:\/\/www.chooch.com\/blog\/what-is-edge-ai\/\">edge devices<\/a>, the remote devices can send alerts when a leak is detected.\r\n\r\nLearn more about how AI models are used for infrastructure inspection by reviewing one of our computer vision case studies. <a href=\"https:\/\/www.chooch.com\/contact-us\/\">Contact Chooch<\/a>\u00a0to discuss your leak detection and remote site monitoring project.",
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"post_date": "2023-01-18 08:23:05",
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"post_content": "AI is nothing new to anyone reading this\u00a0<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision blog<\/a>. Siri, Alexa, and web chatbots have made AI commonplace.\u00a0Yet, computer vision gives AI a pair of eyes that can be taught with machine learning.\r\n<h3 dir=\"ltr\"><strong>What is machine learning?<\/strong><\/h3>\r\n<p dir=\"ltr\"><a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">Machine learning<\/a> is the application of statistical models and algorithms to perform tasks without the need to introduce explicit instructions. It relies on inference and pattern recognition using existing data sets. It requires minimal assistance from programmers in making decisions.<\/p>\r\n\r\n<h3 dir=\"ltr\"><strong>What is computer vision?<\/strong><\/h3>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision<\/a> refers to the ability of a machine to understand images and videos. It mimics the capability of human vision by acquiring, processing, and analyzing real-world data and synthesizing them into useful information. It uses a camera to capture images and videos to analyze, which can then be purposed for object recognition, motion estimation, and video tracking.\r\n<h3>6 applications of computer vision<\/h3>\r\nMachine learning and computer vision are often used together to effectively acquire, analyze, and interpret captured visual data. Here are six applications of these technologies to help illustrate some of the benefits Chooch is seeing in the marketplace.\r\n\r\n<strong>1. Automotive<\/strong>\r\n<p dir=\"ltr\">Self-driving cars are slowly making their way into the market, with more companies looking for innovative ways to bring more electric vehicles onto the road. <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision technology<\/a> helps these self-driving vehicles \u2018see\u2019 the environment while <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> algorithms create the \u201cbrains\u201d that help that computer vision interpret the objects around the car.<\/p>\r\n<p dir=\"ltr\">Self-driving cars are equipped with multiple cameras to provide a complete 360-degree view of the environment within a range of hundreds of meters.\u00a0<a href=\"https:\/\/www.tesla.com\/autopilot\" target=\"_blank\" rel=\"noopener\">Tesla cars, for instance, uses up to 8 surround cameras to achieve this feat<\/a>. Twelve ultrasonic sensors for detecting hard and soft objects on the road and a forward-facing radar that enables the detection of other vehicles even through rain or fog are also installed to complement the cameras.<\/p>\r\nWith large amounts of data being fed into the vehicle, a simple computer won\u2019t be enough to handle the influx of information. This is why all self-driving cars have an onboard computer with computer vision features created through machine learning.\r\n\r\nThe cameras and sensors are tasked to both detect and classify objects in the environment - like pedestrians. The location, density, shape, and depth of the objects have to be considered instantaneously to enable the rest of the driving system to make appropriate decisions. All these computations are only possible through the integration of <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> and deep neural networks which results in features like pedestrian detection.\r\n\r\n<img class=\"alignnone wp-image-5821\" src=\"\/wp-content\/uploads\/2023\/01\/tesla-car-vision-view.jpg\" alt=\"A Tesla car\u2019s vision\" width=\"812\" height=\"457\" \/>\r\n<p style=\"text-align: center;\">Fig. 1. A Tesla car\u2019s vision (Source:\u00a0<a href=\"https:\/\/www.tesla.com\/autopilot\">Tesla<\/a>)<\/p>\r\n<p dir=\"ltr\">Road conditions, traffic situations, and other environmental factors don\u2019t remain the same every time you get in the car. Having a computer simply memorize what it sees won\u2019t be useful when changes are suddenly introduced into the environment. Machine learning helps the computer \u201cunderstand\u201d what it sees, allowing the system to quickly adapt to whichever environment it\u2019s brought into. That\u2019s artificial intelligence.<\/p>\r\n<p dir=\"ltr\"><strong>2. Banking<\/strong><\/p>\r\n<p dir=\"ltr\">Banks are also using computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> to quickly authenticate documents such as IDs, checks, and passports. A customer can just take a photo of themselves or their ID using a mobile device to authorize transactions, but liveliness detection and anti-spoofing can be acquired through machine learning and then detected by computer vision. Chooch features\u00a0facial authentication\u00a0for banking.<\/p>\r\n<p dir=\"ltr\">Some banks are starting to implement online deposit of checks through a mobile phone app. Using computer vision and machine learning, the system is designed to read the important details on an uploaded photo of a check for deposit. The algorithm can automatically correct distortions, skews, warps, and poor lighting conditions present on the image.<\/p>\r\n<p dir=\"ltr\">There\u2019s no need to go to the bank to deposit checks or process other transactions that used to be done over-the-counter.\u00a0<a href=\"https:\/\/emerj.com\/ai-sector-overviews\/computer-vision-applications-shopping-driving-and-more\/\" target=\"_blank\" rel=\"noopener\">The Mercantile Bank of Michigan which adopted this system was able to realize a 20% increase in its online bank users<\/a>.<\/p>\r\n<p dir=\"ltr\"><strong>3. Industrial facilities management<\/strong><\/p>\r\n<p dir=\"ltr\">The industrial sector has critical infrastructure which must always be monitored, secured, and regulated to avoid any kind of loss or damage. In the oil industry, for example, remote oil wells must be monitored regularly to ensure smooth operation. However, with sites deployed in several regions, it would be very costly to do site visits every so often.<\/p>\r\nUsing machine learning and computer vision, oil companies can monitor sites 24\/7 without having to deploy employees. The system can be programmed to read tank levels, spot leaks, and ensure the security of the facilities. Alerts are raised whenever an anomaly is detected in any of the sites, enabling a quick response from the management team.\r\n\r\nThe way computer vision is used in the scenario above can be adopted by chemical factories, refineries, and even nuclear power plants. Sensors and camera feed must all be connected and handled by a powerful AI fully capable of utilizing computer vision and <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> to detect pedestrians and vehicles approaching or entering the facilities.\r\n<p dir=\"ltr\"><strong>4. Healthcare<\/strong><\/p>\r\n<p dir=\"ltr\">There are several applications for machine learning and <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">computer vision in healthcare<\/a>.<\/p>\r\nAccurately classifying illnesses is becoming better now, thanks to computer vision technology. With machine learning training, AI can \u201clearn\u201d what diseases look like in medical imaging. It is now even possible to diagnose patients using a mobile phone, eliminating the need to line up in hospitals for an appointment.\r\n<p dir=\"ltr\"><a href=\"http:\/\/www.gausssurgical.com\/\" target=\"_blank\" rel=\"noopener\">Gauss Surgical<\/a>, a medical technology company, is using cloud-based computer vision technology and machine learning algorithms to estimate blood loss during surgical operations. Using an iPad-based app and a camera, the captured images of suction canisters and surgical sponges are analyzed to predict the possibility of hemorrhage. They\u2019re found to be more accurate than the visual estimates of doctors during medical procedures.<\/p>\r\nChooch is developing several initiatives in the computer vision for <a href=\"https:\/\/www.chooch.com\/solutions\/healthcare-ai-vision\/\">healthcare<\/a> space. For more information please contact us about use cases.\r\n<p dir=\"ltr\"><strong>5. Retail<\/strong><\/p>\r\n<p dir=\"ltr\">Chooch is powering identification and recommendation engines for several high traffic sites, and we are also working on inventory systems, but <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision<\/a> is also being used in the physical world by other companies.<\/p>\r\nAmazon, notably, recently opened their Amazon Go store where shoppers can just pick up any item and leave the store without having to go through a checkout counter. Automatic electronic payments are made possible by equipping the Go store with cameras with computer vision capabilities.\r\n\r\n<img class=\"aligncenter size-full wp-image-456\" src=\"\/wp-content\/uploads\/2023\/06\/amazon-branch.jpg\" alt=\"Amazon Go branch\" width=\"680\" height=\"379\" \/>\r\n<p style=\"text-align: center;\">Fig. 2. An Amazon Go branch (Source:\u00a0<a href=\"https:\/\/www.cnet.com\/pictures\/photos-inside-amazon-go-store-no-cashiers-seattle\/18\/?source=post_page---------------------------\" target=\"_blank\" rel=\"noopener\">CNET<\/a>)<\/p>\r\n \r\n\r\nCameras are placed on aisles and shelves to monitor when a customer picks up or returns an item. Each customer is assigned a virtual basket that gets filled according to the item they take from the shelves. When done, customers can freely walk out of the store and the cost will be charged to their Amazon account.\r\n<p dir=\"ltr\">Cashiers have been eliminated through this program, a personal cost savings, allowing for a faster and more convenient checkout process. Security won\u2019t be an issue also since the system can track multiple individuals simultaneously without using facial recognition.<\/p>\r\nAmazon has also applied for a patent for a virtual mirror. This technology makes use of computer vision to project the image of the individual looking at the mirror. Various superimpositions like clothes and accessories can then be placed over the reflection, allowing the shopper to try different items without needing to physically put them on.\r\n<p dir=\"ltr\"><strong>6. Security<\/strong><\/p>\r\n<p dir=\"ltr\">The security sector benefits greatly the most from the perfect unison between <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> and computer vision. For instance, airports, stadiums, and even streets are installed with facial recognition systems to identify terrorists and wanted criminals. Cameras can quickly match an individual\u2019s face against a database and prompt authorities on the presence of known threats in the facility.<\/p>\r\n<p dir=\"ltr\">Offices are also installing CCTV cameras to identify who enters and exits the premises. Some rooms accessible only to authorized personnel can be set with an automatic alarm when an unrecognized individual is identified by the camera linked to a computer vision system.<\/p>\r\n<p dir=\"ltr\"><a href=\"https:\/\/www.chooch.com\/solutions\/retail-ai-vision\/\">Retail<\/a> security has also been quick to take up computer vision and machine learning to improve the safety of business assets. Retailers have been using computer technology to reduce theft and losses at their branches by installing intelligent cameras in the vicinity.<\/p>\r\n<p dir=\"ltr\">Checkout can also be monitored. Using <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a>, cameras can be placed over checkout counters to monitor product scans. Any item that crosses the scanner without being tagged as a sale is labeled by the software as a loss. The report is then sent to the management to handle the issue and prevent similar incidents from happening.<\/p>\r\n\r\n<h3 dir=\"ltr\"><strong>Computer vision possibilities<\/strong><\/h3>\r\n<p dir=\"ltr\">Computer vision has a wide variety of applications in different industries. From banking and automotive to sports and security, the power of cameras combined with <a href=\"https:\/\/www.chooch.com\/blog\/comparison-guide-to-deep-learning-vs-machine-learning\/\">machine learning<\/a> poses endless possibilities for improving business performance.<\/p>\r\n<p dir=\"ltr\">Chooch offers different forms of AI Vision technology that can improve the productivity and efficiency of your marketing and security efforts. See how <a href=\"https:\/\/www.chooch.com\/\">Chooch AI Vision<\/a> platform can benefit your organization. <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Schedule a demo<\/a><\/p>",
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"post_date": "2023-01-18 07:59:17",
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"post_content": "Large-scale industrial companies rely heavily on experienced human employees to routinely inspect vast infrastructure networks. However, these inspection activities are expensive, time-consuming, dangerous, and highly prone to mistakes and errors. Now, industrial companies are adopting computer vision to radically improve the speed, safety, accuracy, and cost-efficiency of infrastructure inspection.\r\n\r\nLarge-scale industrial operations manage <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">infrastructure networks<\/a> spanning hundreds of miles. An energy company, for example, needs to maintain vast networks of electrical wires, distribution poles, transmission towers, electrical substations, and other critical assets. Because these assets are often located in remote and dangerous locations, inspecting them for maintenance issues demands high-risk expeditions, skilled labor, and an enormous amount of time and financial resources.\r\n\r\nToday, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> empowers industrial companies to achieve safer, more accurate, and more cost-effective inspections of key infrastructure components.\r\n<h2>Traditional Methods of Infrastructure Inspection<\/h2>\r\nThe inspection and maintenance of critical infrastructure components is a necessary part of nearly every large-scale industry. Some common infrastructure types requiring inspections include:\r\n<ul>\r\n \t<li>Power lines<\/li>\r\n \t<li>Utility infrastructure<\/li>\r\n \t<li>Construction sites<\/li>\r\n \t<li>Cell towers<\/li>\r\n \t<li>Roads and interstates<\/li>\r\n \t<li>Wind turbines<\/li>\r\n \t<li>Industrial agriculture<\/li>\r\n \t<li>Wastewater and effluent<\/li>\r\n<\/ul>\r\nAside from using advanced technology, trained and experienced professionals are an integral part of most inspection processes. These inspectors frequently endure dangerous conditions to perform in-person evaluations using the human eye alone with no special instrumentation. Often traveling to remote areas by helicopter, working out of bucket trucks \u2013 or climbing up towers, wind turbines, bridges, and electrical distribution poles \u2013 human inspectors need to evaluate the condition of key assets to answer a host of questions like:\r\n<ul>\r\n \t<li>Is it rusty?<\/li>\r\n \t<li>Is it structurally sound?<\/li>\r\n \t<li>Are trees growing over a transformer box?<\/li>\r\n \t<li>Are guy-wires intact?<\/li>\r\n \t<li>Is it leaking?<\/li>\r\n \t<li>And a great deal more<\/li>\r\n<\/ul>\r\n<img class=\"alignnone wp-image-3294 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/computer-vision-for-infrastructure-inspections.jpg\" alt=\"Computer Vision for Better Infrastructure Inspections\" width=\"1200\" height=\"628\" \/>\r\n<h2>Leveraging Computer Vision for Better Infrastructure Inspections<\/h2>\r\n<a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">Computer vision technology<\/a> provides a cost-effective solution for conducting accurate and timely inspections of <a href=\"https:\/\/www.chooch.com\/solutions\/manufacturing-ai-vision\/\">industrial infrastructure<\/a> assets. In addition to achieving more accurate and consistent results than human-led inspections, visual AI for infrastructure inspection is dramatically safer and more affordable.\r\n\r\nComputer vision strategies for infrastructure inspection leverage the following features:\r\n<ul>\r\n \t<li>High-definition IoT-connected cameras \u2013 including infrared cameras \u2013 mounted in remote locations that observe site conditions.<\/li>\r\n \t<li>Deployment of drones for aerial footage, visual measurements, and automatic identification of potential problems.<\/li>\r\n \t<li>High-resolution satellite topography imagery to show the current condition and status of assets on the ground.<\/li>\r\n \t<li>Edge servers running sophisticated AI models that analyze and interpret visual data, identify maintenance issues, detect environmental hazards, and spot instances of fire and overheating.<\/li>\r\n \t<li>Instant alerts, reports, and metrics sent to decision-makers for immediate action on potential problems.<\/li>\r\n<\/ul>\r\nWith <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI Vision<\/a>, industrial companies gain immediate access to a wide library of pre-built visual AI models for the most common inspection use cases. Armed with these tools, customers can develop visual AI models that instantly detect the following concerns:\r\n<ul>\r\n \t<li>Tree overgrowth<\/li>\r\n \t<li>Rusty, damaged, or defective structures<\/li>\r\n \t<li>Overheating, smoke, flares, and fire<\/li>\r\n \t<li>Leaks in pipes<\/li>\r\n \t<li>Wastewater effluent and discharges<\/li>\r\n \t<li>Retention pond and drainage problems<\/li>\r\n \t<li>Low-hanging or broken power lines<\/li>\r\n \t<li>Virtually any other visually detectable inspection issue<\/li>\r\n<\/ul>\r\n<img class=\"alignnone wp-image-3293 size-full\" src=\"\/wp-content\/uploads\/2023\/07\/industrial-computer-vision-for-infrastructure-inspections.jpg\" alt=\"Industrial Computer Vision for Infrastructure Inspections\" width=\"1200\" height=\"628\" \/>\r\n\r\nAt the end of the day, the ROI benefit of computer vision for industrial computer vision inspections is clear. Whether it\u2019s a large-scale industrial operation, utility company, or governmental organization, <a href=\"https:\/\/www.chooch.com\/what-is-computer-vision\/\">computer vision technology<\/a> empowers faster and more accurate detection of maintenance and environmental concerns \u2013 orders of magnitude more affordable than relying on human inspectors alone. Even better, <a href=\"https:\/\/www.chooch.com\/see-how-it-works\/\">Chooch AI<\/a> can design and deploy a custom visual AI inspection strategy in only 6 to 9 days.",
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RETAIL
LOSS PREVENTION: RETAIL AI CAN MAKE DRAMATIC IMPROVEMENTS WITH EDGE AI
Retail shrinkage is a multi-billion-dollar sore fundamental problem in the
retail industry. According to the National Retail Federation, in 2019, the
inventory loss due to shoplifting, employee theft, or other errors and fraud
reached $61.7 billion in the United States alone. To overcome the issue,
retailers have implemented various loss prevention strategies and techniques,
from electronic article surveillance, reporting systems, surveillance cameras,
and plenty of policies to control the shrink.
Yet, they still fall victim to shrinkage, and most of these methods are reactive
and tend to be inefficient, cost-wise.
The growing volumes of data have led organizations to use available data more
effectively by developing systems to report, analyze, and predict shrink
accurately. Thus, embracing advanced technologies such as artificial
intelligence and edge AI devices.
WHY SHOULD YOU CONSIDER INTEGRATING EDGE AI IN YOUR RETAIL ACTIVITY?
Retail shrinkage can drastically impact retailers’ profits and might even put
them out of business as the risk gets high for businesses that already have
low-profit margins. The higher it gets, the more it can impact organizations’
ability to pay their employees and their business-related expenses, which
eventually leads to poor customer service and experience.
Loss prevention drives higher profits and more business growth for the retail
industry. It is a prime priority for retailers to increase their profits and
decrease losses, and Retail AI Solutions are promising in retail loss
prevention. These advanced technologies are using data patterns and insights to
predict fraudulent activity in forms of shoplifting, internal theft, return
fraud, vendor fraud, discount abuse, administrative errors, and so forth. Hence,
providing a more proactive approach to reduce retail shrink and loss.
Retailers are now shifting to AI-driven solutions that allow an extensive set of
opportunities to improve the customer experience as well as enhancing retail
security by preserving protection against fraudulent elements of inventory loss
and delivering a more reliable shopping experience.
Edge AI solutions such as video analytics can run instantly and effectively
respond to events and actions occurring at the store.
HOW DOES EDGE AI PREVENT RETAIL LOSS?
There is a significant shift in how Edge AI approaches the loss prevention
strategies from reactive to proactive, predictive prevention techniques. The
process starts with collecting data from various sources, including security
systems (camera, alarm records, etc.), video, payment data, store operation
data, point of sales, crime data (local crime statistics), and supply chain
data.
The data serves as a fundamental feed to leverage techniques such as computer
vision, deep learning, behavioral analytics, predictive analytics, pattern
recognition, image processing & recognition, machine learning, and correlation.
Integrating Edge AI in retail loss prevention offers a set of proactive actions
to stop retail loss, increase KPIs to prevent inventory loss, discount abuse,
pilferage, shoplifting, theft, and return fraud and reduce shrinkage. Moreover,
it shifts the strategy from “Identifying a case” to “Preventing a case.”
RETAIL EDGE AI STRATEGIES’ EXAMPLES:
VIDEO ANALYTICS SYSTEMS
Video analytics powered with artificial intelligence and machine learning
algorithms allow retailers to overcome the limitations of traditional video
surveillance systems. Artificial intelligence makes video searchable, and
actionable enabling its users to proactively investigate the retail loss and
pinpoint persons susceptible to committing the retail crimes, as well as
offering real-time monitoring and alerts system for suspicious behavior.
SMART SHELVES
Smart shelves are using technology to connect to the items they hold to monitor
and secure these areas. Smart shelves are configured to provide real-time alerts
and trigger calls to action for any abnormal activity detected. Beyond the loss
prevention benefits, smart shelves enable retailers to track merchandise in
real-time, giving insight into when to restock.
RFID TAGS
RFID-enabled smart tags attached to goods communicate with an electronic reader
to track products. These devices can be removed at the checkout; if they’re not
removed, a security alarm is triggered when the customer tries to exit the
store.
POINT OF SALE SYSTEMS
An automated point of sale, or POS, is ideal for mitigating employee temptations
to steal and help implement reliable inventory practices. Traditional systems
are managed by employees. Still, failure to scan items is one of the primary
ways employee theft occurs. Moreover, by not scanning a product at the checkout,
employees are also discrediting inventory visibility.
HOW CAN CHOOCH AI HELP THE RETAIL INDUSTRY TO THRIVE AND STOP LOSSES?
The future of retail loss prevention is AI-driven. When used accurately,
artificial intelligence is able to limit retail loss and manage inventory to
overcome shrinkage and impact the bottom line. Are you looking for a reliable
partner to strengthen your shrinkage prevention strategy with AI?
Chooch AI offers complete Computer Vision Services. It provides AI training and
models for any visual data for enterprise deployments. Chooch AI is a fast,
flexible, and accurate platform that can process visual data in any spectrum for
many applications across many industries.
The Chooch Visual AI platform offers a wide variety of brick and mortar retail
AI applications. From shelf space management to in-store health monitoring, from
image optimization to analyzing consumer behavior, visual AI can improve
consumers’ shopping experience and revenues. The flexibility and efficiency of
Chooch AI can deliver multiple impactful solutions to the retail industry on one
platform.
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