Gary Marcus, professor emeritus at NYU, explains the differences between large language models and "world models" — and why he thinks the latter are key to achieving artificial general intelligence.
What if the most powerful technology in human history is being built by people who openly admit they don't trust each other? In this explosive 2-hour debate, three AI experts pull back the curtain on the shocking psychology driving the race to Artificial General Intelligence—and why the people building it might be the biggest threat of all. Kokotajlo predicts AGI by 2028 based on compute scaling trends. Marcus argues we haven't solved basic cognitive problems from his 2001 research. The stakes? If Kokotajlo is right and Marcus is wrong about safety progress, humanity may have already lost control. Sponsor messages: ======== Google Gemini: Google Gemini features Veo3, a state-of-the-art AI video generation model in the Gemini app. Sign up at https://gemini.google.com Tufa AI Labs are hiring for ML Engineers and a Chief Scientist in Zurich/SF. They are top of the ARCv2 leaderboard! https://tufalabs.ai/ ======== Guest Powerhouse Gary Marcus - Cognitive scientist, author of "Taming Silicon Valley," and AI's most prominent skeptic who's been warning about the same fundamental problems for 25 years (https://garymarcus.substack.com/) Daniel Kokotajlo - Former OpenAI insider turned whistleblower who reveals the disturbing rationalizations of AI lab leaders in his viral "AI 2027" scenario (https://ai-2027.com/) Dan Hendrycks - Director of the Center for AI Safety who created the benchmarks used to measure AI progress and argues we have only years, not decades, to prevent catastrophe (https://danhendrycks.com/) Transcript: http://app.rescript.info/public/share/tEcx4UkToi-2jwS1cN51CW70A4Eh6QulBRxDILoXOno TOC: Introduction: The AI Arms Race 00:00:04 - The Danger of Automated AI R&D 00:00:43 - The Rationalization: "If we don't, someone else will" 00:01:56 - Sponsor Reads (Tufa AI Labs & Google Gemini) 00:02:55 - Guest Introductions The Philosophical Stakes 00:04:13 - What is the Positive Vision for AGI? 00:07:00 - The Abundance Scenario: Superintelligent Economy 00:09:06 - Differentiating AGI and Superintelligence (ASI) 00:11:41 - Sam Altman: "A Decade in a Month" 00:14:47 - Economic Inequality & The UBI Problem Policy and Red Lines 00:17:13 - The Pause Letter: Stopping vs. Delaying AI 00:20:03 - Defining Three Concrete Red Lines for AI Development 00:25:24 - Racing Towards Red Lines & The Myth of "Durable Advantage" 00:31:15 - Transparency and Public Perception 00:35:16 - The Rationalization Cascade: Why AI Labs Race to "Win" Forecasting AGI: Timelines and Methodologies 00:42:29 - The Case for Short Timelines (Median 2028) 00:47:00 - Scaling Limits: Compute, Data, and Money 00:49:36 - Forecasting Models: Bio-Anchors and Agentic Coding 00:53:15 - The 10^45 FLOP Thought Experiment The Great Debate: Cognitive Gaps vs. Scaling 00:58:41 - Gary Marcus's Counterpoint: The Unsolved Problems of Cognition 01:00:46 - Current AI Can't Play Chess Reliably 01:08:23 - Can Tools and Neurosymbolic AI Fill the Gaps? 01:16:13 - The Multi-Dimensional Nature of Intelligence 01:24:26 - The Benchmark Debate: Data Contamination and Reliability 01:31:15 - The Superhuman Coder Milestone Debate 01:37:45 - The Driverless Car Analogy The Alignment Problem 01:39:45 - Has Any Progress Been Made on Alignment? 01:42:43 - "Fairly Reasonably Scares the Sh*t Out of Me" 01:46:30 - Distinguishing Model vs. Process Alignment Scenarios and Conclusions 01:49:26 - Gary's Alternative Scenario: The Neurosymbolic Shift 01:53:35 - Will AI Become Jeff Dean? 01:58:41 - Takeoff Speeds and Exceeding Human Intelligence 02:03:19 - Final Disagreements and Closing Remarks REFS: Gary Marcus (2001) - The Algebraic Mind https://mitpress.mit.edu/9780262632683/the-algebraic-mind/ 00:59:00 Gary Marcus & Ernest Davis (2019) - Rebooting AI https://www.penguinrandomhouse.com/books/566677/rebooting-ai-by-gary-marcus-and-ernest-davis/ 01:31:59 Gary Marcus (2024) - Taming SV https://www.hachettebookgroup.com/titles/gary-marcus/taming-silicon-valley/9781541704091/ 00:03:01
Gary Marcus is a cognitive scientist, author, and longtime AI skeptic. Marcus joins Big Technology to discuss whether large‑language‑model scaling is running into a wall. Tune in to hear a frank debate on the limits of “just add GPUs" and what that means for the next wave of AI. We also cover data‑privacy fallout from ad‑driven assistants, open‑source bio‑risk fears, and the quest for interpretability. Hit play for a reality check on AI’s future — and the insight you need to follow where the industry heads next. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices
AI expert Prof. Gary Marcus doesn't mince words about today's artificial intelligence. He argues that despite the buzz, chatbots like ChatGPT aren't as smart as they seem and could cause real problems if we're not careful. Marcus is worried about tech companies putting profits before people. He thinks AI could make fake news and privacy issues even worse. He's also concerned that a few big tech companies have too much power. Looking ahead, Marcus believes the AI hype will die down as reality sets in. He wants to see AI developed in smarter, more responsible ways. His message to the public? We need to speak up and demand better AI before it's too late. Buy Taming Silicon Valley: https://amzn.to/3XTlC5s Gary Marcus: https://garymarcus.substack.com/ https://x.com/GaryMarcus Interviewer: Dr. Tim Scarfe (Refs in top comment) TOC [00:00:00] AI Flaws, Improvements & Industry Critique [00:16:29] AI Safety Theater & Image Generation Issues [00:23:49] AI's Lack of World Models & Human-like Understanding [00:31:09] LLMs: Superficial Intelligence vs. True Reasoning [00:34:45] AI in Specialized Domains: Chess, Coding & Limitations [00:42:10] AI-Generated Code: Capabilities & Human-AI Interaction [00:48:10] AI Regulation: Industry Resistance & Oversight Challenges [00:54:55] Copyright Issues in AI & Tech Business Models [00:57:26] AI's Societal Impact: Risks, Misinformation & Ethics [01:23:14] AI X-risk, Alignment & Moral Principles Implementation [01:37:10] Persistent AI Flaws: System Limitations & Architecture Challenges [01:44:33] AI Future: Surveillance Concerns, Economic Challenges & Neuro-Symbolic AI YT version with refs: https://youtu.be/o9MfuUoGlSw
Prof Gary Marcus revisited his keynote from AGI-21, noting that many of the issues he highlighted then are still relevant today despite significant advances in AI. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Gary Marcus criticized current large language models (LLMs) and generative AI for their unreliability, tendency to hallucinate, and inability to truly understand concepts. Marcus argued that the AI field is experiencing diminishing returns with current approaches, particularly the "scaling hypothesis" that simply adding more data and compute will lead to AGI. He advocated for a hybrid approach to AI that combines deep learning with symbolic AI, emphasizing the need for systems with deeper conceptual understanding. Marcus highlighted the importance of developing AI with innate understanding of concepts like space, time, and causality. He expressed concern about the moral decline in Silicon Valley and the rush to deploy potentially harmful AI technologies without adequate safeguards. Marcus predicted a possible upcoming "AI winter" due to inflated valuations, lack of profitability, and overhyped promises in the industry. He stressed the need for better regulation of AI, including transparency in training data, full disclosure of testing, and independent auditing of AI systems. Marcus proposed the creation of national and global AI agencies to oversee the development and deployment of AI technologies. He concluded by emphasizing the importance of interdisciplinary collaboration, focusing on robust AI with deep understanding, and implementing smart, agile governance for AI and AGI. YT Version (very high quality filmed) https://youtu.be/91SK90SahHc Pre-order Gary's new book here: Taming Silicon Valley: How We Can Ensure That AI Works for Us https://amzn.to/4fO46pY Filmed at the AGI-24 conference: https://agi-conf.org/2024/ TOC: 00:00:00 Introduction 00:02:34 Introduction by Ben G 00:05:17 Gary Marcus begins talk 00:07:38 Critiquing current state of AI 00:12:21 Lack of progress on key AI challenges 00:16:05 Continued reliability issues with AI 00:19:54 Economic challenges for AI industry 00:25:11 Need for hybrid AI approaches 00:29:58 Moral decline in Silicon Valley 00:34:59 Risks of current generative AI 00:40:43 Need for AI regulation and governance 00:49:21 Concluding thoughts 00:54:38 Q&A: Cycles of AI hype and winters 01:00:10 Predicting a potential AI winter 01:02:46 Discussion on interdisciplinary approach 01:05:46 Question on regulating AI 01:07:27 Ben G's perspective on AI winter
AI is amazing… or terrifying, depending on who you ask. This is a technology that elicits strong, almost existential reactions. So, as a Memorial Day special, we're running an episode of Recode Media with Peter Kafka that digs into the giant ambitions and enormous concerns people have about the very same tech. First up: Joshua Browder (@jbrowder1), a Stanford computer science dropout who tried to get an AI lawyer into court. Then: Microsoft's CTO Kevin Scott (@kevin_scott) pitches a bright AI future. Plus: hype-deflator, cognitive scientist and author Gary Marcus (@GaryMarcus) believes in AI, but he thinks the giants of Silicon Valley are scaling flawed technology now—with potentially dangerous consequences. Subscribe for free to Recode Media to make sure you get the whole series: https://bit.ly/3IOpWuB Pivot will return on Friday! Learn more about your ad choices. Visit podcastchoices.com/adchoices
Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Twitter: https://twitter.com/MLStreetTalk In a historic and candid Senate hearing, OpenAI CEO Sam Altman, Professor Gary Marcus, and IBM's Christina Montgomery discussed the regulatory landscape of AI in the US. The discussion was particularly interesting due to its timing, as it followed the recent release of the EU's proposed AI Act, which could potentially ban American companies like OpenAI and Google from providing API access to generative AI models and impose massive fines for non-compliance. The speakers openly addressed potential risks of AI technology and emphasized the need for precision regulation. This was a unique approach, as historically, US companies have tried their hardest to avoid regulation. The hearing not only showcased the willingness of industry leaders to engage in discussions on regulation but also demonstrated the need for a balanced approach to avoid stifling innovation. The EU AI Act, scheduled to come into power in 2026, is still just a proposal, but it has already raised concerns about its impact on the American tech ecosystem and potential conflicts between US and EU laws. With extraterritorial jurisdiction and provisions targeting open-source developers and software distributors like GitHub, the Act could create more problems than it solves by encouraging unsafe AI practices and limiting access to advanced AI technologies. One core issue with the Act is the designation of foundation models in the highest risk category, primarily due to their open-ended nature. A significant risk theme revolves around users creating harmful content and determining who should be held accountable – the users or the platforms. The Senate hearing served as an essential platform to discuss these pressing concerns and work towards a regulatory framework that promotes both safety and innovation in AI. 00:00 Show 01:35 Legals 03:44 Intro 10:33 Altman intro 14:16 Christina Montgomery 18:20 Gary Marcus 23:15 Jobs 26:01 Scorecards 28:08 Harmful content 29:47 Startups 31:35 What meets the definition of harmful? 32:08 Moratorium 36:11 Social Media 46:17 Gary's take on BingGPT and pivot into policy 48:05 Democratisation
Will truth and reason survive the evolution of artificial intelligence? AI researcher Gary Marcus says no, not if untrustworthy technology continues to be integrated into our lives at such dangerously high speeds. He advocates for an urgent reevaluation of whether we're building reliable systems (or misinformation machines), explores the failures of today's AI and calls for a global, nonprofit organization to regulate the tech for the sake of democracy and our collective future. (Followed by a Q&A with head of TED Chris Anderson) Hosted on Acast. See acast.com/privacy for more information.
Sam Harris speaks with Stuart Russell and Gary Marcus about recent developments in artificial intelligence and the long-term risks of producing artificial general intelligence (AGI). They discuss the limitations of Deep Learning, the surprising power of narrow AI, ChatGPT, a possible misinformation apocalypse, the problem of instantiating human values, the business model of the Internet, the meta-verse, digital provenance, using AI to control AI, the control problem, emergent goals, locking down core values, programming uncertainty about human values into AGI, the prospects of slowing or stopping AI progress, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe. Learning how to train your mind is the single greatest investment you can make in life. That's why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life's most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.
Blake Lemoine is the ex-Google engineer who concluded the company's LaMDA chatbot was sentient. Gary Marcus is an academic, author, and outspoken AI critic. The two join Big Technology Podcast to debate the utility of AI chatbots, their dangers, and the actual technology they're built on. Join us for a fascinating conversation that reveals much about the state of this technology. There's plenty to be learned from the disagreements, and the common ground as well. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices
Gary Marcus, a professor emeritus of psychology and neural science at NYU and the author of “Rebooting AI,” joins Scott to discuss artificial intelligence including the overall hype cycle, ChatGPT, and useful applications. Follow Gary on Twitter, @GaryMarcus. Scott opens with his thoughts on CEO pay, specifically Tim Cook’s pay cut. He then wraps up by discussing a recent partnership between Walmart and Salesforce. Algebra of Happiness: your body is an instrument, not an ornament. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Gary Marcus is the author of Rebooting AI and an artificial intelligence entrepreneur who's a loud critic of many of the field's biggest promises. Marcus joins Big Technology Podcast this week to discuss the high profile breakthroughs such as LaMDA and Dall-E, and explain why putting too much faith in the field's ability may be dangerous. We begin with a discussion of the AI-generated art piece that won a competition in Colorado last week. Learn more about your ad choices. Visit megaphone.fm/adchoices
Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud YT: https://www.youtube.com/watch?v=ZDY2nhkPZxw We have a chat with Prof. Gary Marcus about everything which is currently top of mind for him, consciousness [00:00:00] Gary intro [00:01:25] Slightly conscious [00:24:59] Abstract, compositional models [00:32:46] Spline theory of NNs [00:36:17] Self driving cars / algebraic reasoning [00:39:43] Extrapolation [00:44:15] Scaling laws [00:49:50] Maximum likelihood estimation References: Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets https://arxiv.org/abs/2201.02177 DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT https://arxiv.org/pdf/1912.02292.pdf Bayesian Deep Learning and a Probabilistic Perspective of Generalization https://arxiv.org/pdf/2002.08791.pdf
Professor Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. Gary said in his recent next decade paper that — without us, or other creatures like us, the world would continue to exist, but it would not be described, distilled, or understood. Human lives are filled with abstraction and causal description. This is so powerful. Francois Chollet the other week said that intelligence is literally sensitivity to abstract analogies, and that is all there is to it. It's almost as if one of the most important features of intelligence is to be able to abstract knowledge, this drives the generalisation which will allow you to mine previous experience to make sense of many future novel situations. Also joining us today is Professor Luis Lamb — Secretary of Innovation for Science and Technology of the State of Rio Grande do Sul, Brazil. His Research Interests are Machine Learning and Reasoning, Neuro-Symbolic Computing, Logic in Computation and Artificial Intelligence, Cognitive and Neural Computation and also AI Ethics and Social Computing. Luis released his new paper Neurosymbolic AI: the third wave at the end of last year. It beautifully articulated the key ingredients needed in the next generation of AI systems, integrating type 1 and type 2 approaches to AI and it summarises all the of the achievements of the last 20 years of research. We cover a lot of ground in today's show. Explaining the limitations of deep learning, Rich Sutton's the bitter lesson and "reward is enough", and the semantic foundation which is required for us to build robust AI.
In this special edition, Dr. Tim Scarfe, Yannic Kilcher and Keith Duggar speak with Gary Marcus and Connor Leahy about GPT-3. We have all had a significant amount of time to experiment with GPT-3 and show you demos of it in use and the considerations. Note that this podcast version is significantly truncated, watch the youtube version for the TOC and experiments with GPT-3 https://www.youtube.com/watch?v=iccd86vOz3w
Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:37 – Singularity 05:48 – Physical and psychological knowledge 10:52 – Chess 14:32 – Language vs physical world 17:37 – What does AI look like 100 years from now 21:28 – Flaws of the human mind 25:27 – General intelligence 28:25 – Limits of deep learning 44:41 – Expert systems and symbol manipulation 48:37 – Knowledge representation 52:52 – Increasing compute power 56:27 – How human children learn 57:23 – Innate knowledge and learned knowledge 1:06:43 – Good test of intelligence 1:12:32 – Deep learning and symbol manipulation 1:23:35 – Guitar
As I mentioned in the Weekend Longreads segment, Gary Marcus and Ernest Davis had an op ed in the times that has changed the way I think about the state of AI. But they’re also the authors of a great new book, Rebooting AI: Building Artificial Intelligence We Can Trust. There’s a reason people remain fearful about AI… it hasn’t earned our trust yet. In all sorts of ways that we get into on this episode. And also, the interesting ways AI development needs to change to take the state of the art to the next level. Sponsor: MintMobile.com/ride Learn more about your ad choices. Visit megaphone.fm/adchoices
Today we're joined by Gary Marcus, CEO and Founder at Robust.AI, well-known scientist, bestselling author, professor and entrepreneur. Hear Gary discuss his latest book, ‘Rebooting AI: Building Artificial Intelligence We Can Trust’, an extensive look into the current gaps, pitfalls and areas for improvement in the field of machine learning and AI. In this episode, Gary provides insight into what we should be talking and thinking about to make even greater (and safer) strides in AI.
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