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Close Ad infoworld UNITED STATES * United States * United Kingdom * App Dev * Cloud * Gen AI * Machine Learning * Analytics * IDG TECH(Talk) Community * Newsletters × search Analytics Careers Databases Cloud Computing * Amazon Web Services * Kubernetes * Microsoft Azure Generative AI Machine Learning Open Source Software Development * Agile Development * CI/CD * Devops * Java * JavaScript * Microsoft .Net Newsletters IDG Events In-Depth * Features * How-To * News * Reviews Blogs Video * Do More with R * Smart Python * IDG TECH(talk) Channel White Papers/Webcasts From Our Partners * The Latest Content from Our Sponsors More from the Foundry Network The voice of IT leadership Analytics Careers CIO Role Digital Transformation Leadership Project Management Security at the speed of business Application Security Cloud Security Identity Management Information Security Network Security Risk Management Security Software Making technology work for business Blockchain Collaboration Mobile Office Software Security Systems Management Windows From the data center to the edge Data Center Internet of Things Linux Networking SD-WAN Servers Storage Wi-Fi * About Us | * Contact | * Republication Permissions | * Privacy Policy | * Cookie Policy | * Copyright Notice | * European Privacy Settings | * Member Preferences | * Advertising | * Foundry Careers | * Ad Choices | * E-commerce Links | * California: Do Not Sell My Personal Info | * Follow Us * * * × Close * Home * Artificial Intelligence * Generative AI REINING IN THE BS IN AI LARGE LANGUAGE MODELS TRAINED ON QUESTIONABLE STUFF ONLINE WILL PRODUCE MORE OF THE SAME. RETRIEVAL AUGMENTED GENERATION IS ONE WAY TO GET CLOSER TO TRUTH. * * * * * * * By Matt Asay Contributor, InfoWorld | Nov 19, 2023 6:00 pm PST alberto clemares exposito / Shutterstock Even people not in tech seemed to have heard of Sam Altman’s ouster from OpenAI on Friday. I was with two friends the next day (one works in construction and the other in marketing) and both were talking about it. Generative AI (genAI) seems to have finally gone mainstream. What it hasn’t done, however, is escape the gravitational pull of BS, as Alan Blackwell has stressed. No, I don’t mean that AI is vacuous, long on hype, and short on substance. AI is already delivering for many enterprises across a host of industries. Even genAI, a small subset of the overall AI market, is a game-changer for software development and beyond. And yet Blackwell is correct: “AI literally produces bullshit.” It makes up stuff that sounds good based on training data. Even so, if we can “box it in,” as MIT professor of AI Rodney Brooks describes, genAI has potential to make a big difference in our lives. [ ALSO ON INFOWORLD: ZERO-SHOT LEARNING AND THE FOUNDATIONS OF GENERATIVE AI ] ‘CHATGPT IS A BULLSHIT GENERATOR’ Truth is not fundamental to how large language models function. LLMs are “deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large data sets.” Note that “truth” and “knowledge” have no place in that definition. LLMs aren’t designed to tell you the truth. As detailed in an OpenAI forum, “Large language models are probabilistic in nature and operate by generating likely outputs based on patterns they have observed in the training data. In the case of mathematical and physical problems, there may be only one correct answer, and the likelihood of generating that answer may be very low.” Companies begin exploring the AI multiverse | Ep. 70 0 seconds of 27 secondsVolume 0% Press shift question mark to access a list of keyboard shortcuts Keyboard ShortcutsEnabledDisabled Play/PauseSPACE Increase Volume↑ Decrease Volume↓ Seek Forward→ Seek Backward← Captions On/Offc Fullscreen/Exit Fullscreenf Mute/Unmutem Decrease Caption Size- Increase Caption Size+ or = Seek %0-9 Settings OffAutomated Captions - en-US Font Color White Font Opacity 100% Font Size 100% Font Family Arial Character Edge None Background Color Black Background Opacity 50% Window Color Black Window Opacity 0% Reset WhiteBlackRedGreenBlueYellowMagentaCyan 100%75%50%25% 200%175%150%125%100%75%50% ArialCourierGeorgiaImpactLucida ConsoleTahomaTimes New RomanTrebuchet MSVerdana NoneRaisedDepressedUniformDrop Shadow WhiteBlackRedGreenBlueYellowMagentaCyan 100%75%50%25%0% WhiteBlackRedGreenBlueYellowMagentaCyan 100%75%50%25%0% This ad will end in 13 Live 00:13 00:13 00:27 That’s a nice way of saying you might not want to rely on ChatGPT to do basic multiplication problems for you, but it could be great at crafting an answer on the history of algebra. In fact, channeling Geoff Hinton, Blackwell says, “One of the greatest risks is not that chatbots will become super intelligent, but that they will generate text that is super persuasive without being intelligent.” It’s like “fake news” on steroids. As Blackwell says, “We’ve automated bullshit.” This isn’t surprising, given the primary sources for the LLMs underlying ChatGPT and other GenAI systems are Twitter, Facebook, Reddit, and “other huge archives of bullshit.” However, “there is no algorithm in ChatGPT to check which parts are true,” such that the “output is literally bullshit,” says Blackwell. What to do? SponsoredPost Sponsored by the EY organization A complex threat landscape demands a dynamic approach to security Just one in five CISOs and C-suite leaders consider their security strategies effective. Learn how a modern security strategy can better protect your organization. ‘YOU HAVE TO BOX THINGS IN CAREFULLY’ The key to getting some semblance of useful knowledge out of LLMs, according to Brooks, is “boxing in.” He says, “You have to box [LLMs] in carefully so that the craziness doesn’t come out, and the making stuff up doesn’t come out.” But how does one “box an LLM in?” One critical way is through retrieval augmented generation (RAG). I love how Zachary Proser characterizes it: “RAG is like holding up a cue card containing the critical points for your LLM to see.” It’s a way to augment an LLM with proprietary data, giving the LLM more context and knowledge to improve its responses. RAG depends on vectors, which are a foundational element used in a variety of AI use cases. A vector embedding is just a long list of numbers that describe features of the data object, like a song, an image, a video, or a poem, stored in a vector database. They’re used to capture the semantic meaning of objects in relation to other objects. Similar objects are grouped together in the vector space. The closer two objects, the more similar they are. (For example, “rugby” and “football” will be closer to each other than “football” and “basketball”). You can then query for related entities that are similar based on their characteristics, without relying on synonyms or keyword matching. As Proser concludes, “Since the LLM now has access to the most pertinent and grounding facts from your vector database, it can provide an accurate answer for your user. RAG reduces the likelihood of hallucination.” Suddenly, your LLM is much more likely to give you a true response, not merely a response that sounds true. This is the sort of “boxing in” that can make LLMs actually useful and not hype. SponsoredPost Sponsored by EY It's time to get your arms around cloud cost management How a FinOps approach can help you make the right trade-offs among the cost, speed, and quality of cloud services. Otherwise, it’s just automated bullshit. Next read this: * The best open source software of 2023 * Do programming certifications still matter? * Cloud computing is no longer a slam dunk * What is generative AI? Artificial intelligence that creates * Coding with AI: Tips and best practices from developers * Why Wasm is the future of cloud computing Related: * Generative AI * Emerging Technology * Technology Industry Matt Asay runs developer relations at MongoDB. The views expressed herein are Matt’s and do not reflect those of his employer. Follow * * * * * Copyright © 2023 IDG Communications, Inc. 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