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

———

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