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.
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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
