David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86

ℹ️ EPISODE INFO

Released

April 3, 2020

Duration

1h 48m

Channel

Lex Fridman Podcast

Watch on YouTube

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📝 EPISODE DESCRIPTION

David Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar, and MuZero and lot of important work in reinforcement learning. Support this podcast by signing up with these sponsors:

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EPISODE LINKS: Reinforcement learning (book): https://amzn.to/2Jwp5zG PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 4:09 - First program 11:11 - AlphaGo 21:42 - Rule of the game of Go 25:37 - Reinforcement learning: personal journey 30:15 - What is reinforcement learning? 43:51 - AlphaGo (continued) 53:40 - Supervised learning and self play in AlphaGo 1:06:12 - Lee Sedol retirement from Go play 1:08:57 - Garry Kasparov 1:14:10 - Alpha Zero and self play 1:31:29 - Creativity in AlphaZero 1:35:21 - AlphaZero applications 1:37:59 - Reward functions 1:40:51 - Meaning of life CONNECT:

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🎤 SPEAKERS

• Lex Fridman

  host

• David Silver

  guest

🧾 EPISODE SUMMARY

In this episode of Lex Fridman Podcast, featuring Lex Fridman and David Silver, David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86 explores david Silver on AlphaGo, self-play, and the path to intelligence Lex Fridman and David Silver trace Silver’s journey from childhood programming to leading DeepMind’s landmark work on AlphaGo, AlphaZero, and MuZero. They explain why Go was such a hard AI challenge, how deep reinforcement learning and self-play enabled systems to exceed human world champions, and what these results suggest about intuition, creativity, and general intelligence. Silver details the transition from hand-crafted knowledge and search to learning-based systems that discover their own strategies, and how removing human priors made the algorithms both stronger and more general. The conversation closes with reflections on future real-world applications, the nature of goals and reward in AI, and layered views on the “meaning” of intelligence and life.

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