Princeton Scientist: We Don't Understand AI - Tom Griffiths - #553

Into the Impossible With Brian Keating

Brian Keating

Physics, Natural Sciences, Science

4.7 • 1.1K Ratings

🗓️ 29 April 2026

⏱️ 49 minutes

🔗️ Recording | iTunes | RSS

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Summary

A Princeton cognitive scientist says AI can't think like a child — and giving it more data won't fix that. If the field keeps scaling without solving what's actually missing, the gap between human and machine intelligence won't close. It'll just get more expensive. Tom Griffiths is a professor of psychology and computer science at Princeton, and one of the leading researchers working at the intersection of human cognition and AI. We cover: -why a child learns language from breadcrumbs while AI needs continents of data -the 250-year-old idea that quietly became the foundation of modern language models -what sycophantic AI actually does to your beliefs over time -why solving AGI might have less to do with scale and more to do with understanding what a child's mind really is. The hallucinations don't bother him — it's the sycophancy that should worry you. Key Takeaways: 00:00 The Math Behind How Minds Actually Work 00:30 Why Defining "Thought" Is Harder Than It Looks 04:30 What AI Gets Wrong About Consciousness 07:00 What ChatGPT Actually Revealed About the Field 08:10 Are Humans Really Irrational — Or Solving a Different Problem? 11:00 How Chomsky Turned Language Into a Math Problem 13:55 The Chessboard Analogy That Explains Generative Grammar 15:20 Why Aristotle Got Thought Right and Physics Wrong 19:45 The Man Who Tried to Build AI in the 1600s 22:40 What Everyone Gets Wrong About George Boole 25:25 From Boole to Turing: How Logic Became Computers 27:40 Why Your Brain Runs on Less Energy Than a Light Bulb 28:40 Jensen Huang Says AGI Is Here. Is He Right? 31:45 Why the "AI vs. Human Intelligence" Scale Is Misleading 33:50 Why a Child Still Outlearns Every AI Model 35:20 The Fuzzy Boundary Problem That Broke Rule-Based AI 37:20 How Semantic Networks Rewired the Theory of Memory 39:30 Rosenblatt Built a Brain — Then Minsky Killed It 43:15 The Plane Ride Where Backpropagation Was Solved 44:20 Hallucinations, Sycophancy, and What Should Actually Worry You 47:00 What Has to Change Before AI Can Truly Generalize 50:10 What a Layperson Should Actually Take Away From This ——— 📬 Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt 🌠 Have a .edu email and live in the USA 🇺🇸? You automatically win a meteorite: https://BrianKeating.com/edu 🔔 Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 🎯 Support Into the Impossible on Patreon — get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating ⭐ Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join 📚 My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un 🌐 More: 🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating ✍️ Blog: https://briankeating.com/blog 🎙️ Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #science #physics #astronomy #cosmology #podcast #universe Learn more about your ad choices. Visit megaphone.fm/adchoices

Transcript

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0:00.0	One of the, I think, interesting challenges we have at the moment is having built systems that we don't fully understand.
0:06.0	The man who built modern AI, he's the direct descendant of the man who invented the math that made it possible,
0:12.0	which is insane, but it's not the wildest thing my guest told me today.
0:17.0	That's pretty much exactly what he was trying to do. And he was the right kind of crazy.
0:21.5	Ivan's was trying to invent AI 250 years before computers even existed.
0:27.5	Sycophancy is a major problem. If you take a rational agent and have them interact with a system which is sycophantic,
0:33.3	then that agent is going to become increasingly confident in their beliefs, but no closer to
0:38.7	the truth.
0:39.7	My guest spent 20 years building the mathematics of how minds work, and he just told me three
0:43.8	things that made me question what I thought AI actually was.
0:47.8	Now let me show you from a physicist's point of view.
0:50.4	Whenever I talk to people about consciousness from Chalmers, Bostrum, and upcoming guest Joshua Bach and others, I always get the same thing.
0:58.2	Like, we can't really define what consciousness is, so how do we know what thought is?
1:01.8	So how can you determine what the laws of thought are?
1:05.0	Isn't that kind of an extremely provocative in bold claim?
1:08.4	The way that I approach that question in the book is really by
1:11.8	thinking about what are the kinds of computational problems that minds solve? And that's really
1:17.2	what this enterprise was? It's trying to figure out, like, what's the mathematical structure
1:20.9	that describes the thing that minds are doing, whether that thing is what Aristotle was interested
1:26.7	in, which is just trying to characterize
1:28.0	what good arguments are, through to some of the questions that you were raising about, you know,
1:32.7	like, what does it mean to make a good decision and how do we think about, you know, rationality
	...

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