AI Can't Learn The Way Humans Do - This Could Fix That

In this episode of Decoded, Ankit and Francois walk through the motivation and math behind world models. They cover a number of areas including why sample efficiency is one of the biggest unsolved problems in AI, how deterministic differentiable control and Newtonian physics represent a "perfect world model," and why the action space explosion makes chess tractable but robotics nearly intractable. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Transcript: https://ycrootaccess.substack.com/p/world-models-an-intuitive-introduction Chapters: 00:00 — Intro 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 01:04:30 — Does this pass the squint test? 01:08:00 — Outro

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