Monte Carlo Revolution
A groundbreaking approach to evaluating game positions emerged through the use of random simulations, known as Monte Carlo Search. This method, particularly its application in Mogo, demonstrated how random play could yield valuable insights into the search tree of a deterministic game like Go. The evolution of this technique marked significant advancements in computer Go programs, paving the way for future successes in AI.In this clip
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Lex Fridman Podcast
David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86
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