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Error-Free Foundation Models: How to Adapt Foundation Models For Your Own ML Tasks To reach 99% Accuracy Webinar Lead source Conversion Type Hubspot Recent Touch Point Last Webinar Registered Date Last Webinar Name From time to time, we would like to contact you about our products and services, as well as other content that may be of interest to you. You may unsubscribe from these communications at any time. * I agree to receive content communications from Kili Technology. * I agree to allow Kili Technology to store and process my personal data.* Foundation models are transforming the way companies think about building AI. But foundation models, however powerful, have one drawback: they make mistakes. To be able to leverage them in real-life applications, it’s essential to improve our ability to control their outputs. In this webinar, we'll explore common mistakes and limitations of models like GPT-4 and SAM, such as low-contrast challenges, limited understanding of proficient data, lack of relevance, and logical consistency. During this session, you will discover the causes of these errors, including data issues, objective functions, reasoning limitations, and prompt engineering, as well as learn effective strategies to correct these errors and improve the performance of language models in your specific tasks. Don't miss out on this opportunity to enhance the reliability and accuracy of your models. Register now! Edouard d'Archimbaud CTO at Kili Technology Jean Latapy Solution Engineer at Kili Technology More and more products are powered by machine learning. That’s why it's [capital] to think about ethics and to make sure [its] impact [is] positive.“ Clément Delangue, Co-founder & CTO @Hugging Face in Why Ethics are important in ML “Our Data-centric approach to AI has helped us achieve a categorization of our own categories that is accurate in more than ninety-five percents of the videos.” Andrea Colonna, VP AI @Jellysmack in How Jellysmack leverages AI to analyze in real-time billions of videos on social networks and identify market trends “But the real-world experience of those who put them into production shows that (...) it's often the quality of data (...) that makes your AI project succeed or fail.” Edouard D'Archimbaud Co-founder & CTO @Kili Technology in Data labeling - Best practices for project management & collaboration Labeling Platform for High-quality Data One tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI Book a demo This website uses cookies Hey! At Kili Technology, we are committed to ensuring your privacy and providing you with the best possible experience. Now, before you jump into exploring our fantastic content, we'd like to get your permission to use these cookies. Don't worry; we've got your privacy covered! 😊 Our cookies serve two primary purposes: 1️⃣ Enhancing Your Experience: These cookies allow us to remember your preferences so you don't have to set them every time you visit. 2️⃣ Analyzing and Improving: We use cookies to enhance our content, features, and overall user experience. But here's the best part: we respect your choices! You have full control over which types of cookies you want to enable or disable. If you accept, we will use cookies for both the aforementioned purposes. However, if you prefer not to, we will only use the necessary cookies required for the site's basic functionality. Read more Save & Close Yes, it's Ok for me No thanks Let me choose Hide details