About this episode
A deep dive on Dwarkesh Patel interview with Eric Jang into how AlphaGo conquered Go by combining a value network, a policy network, and Monte Carlo tree search. We unpack how these two nets shrink the game’s vast space, how self-play trains better strategies, and what this implies for solving hard real‑world problems in science and education—while noting the limits when moving from closed games to open-ended tasks like language models.
Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.
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