AlphaZero: DeepMind's New Chess AI | Two Minute Papers #216 | Summary and Q&A

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December 21, 2017
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AlphaZero: DeepMind's New Chess AI | Two Minute Papers #216

TL;DR

Google DeepMind's AlphaZero defeats Stockfish, the best computer chess engine, after only 4 hours of self-play learning.

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Questions & Answers

Q: How does AlphaZero compare to Stockfish in terms of performance?

AlphaZero outperforms Stockfish in 28 out of 100 games, draws 72 games, and never loses, showcasing its superior performance.

Q: How was AlphaZero trained, and what makes it different from AlphaGo Zero?

AlphaZero is trained through self-play after being given the rules of the game. It is not to be confused with AlphaGo Zero, as it is a new variant of the algorithm specifically designed for chess.

Q: What is the significance of AlphaZero's victory over Stockfish?

AlphaZero's victory over Stockfish, a handcrafted algorithm without machine learning, demonstrates the power of neural networks and reinforcement learning in surpassing traditional approaches.

Q: How does AlphaZero's performance compare to human players like Magnus Carlsen?

AlphaZero's performance indicates its extraordinary skill level, as it would be expected to win at least 95 out of 100 games against Magnus Carlsen, the human player with the highest Elo rating.

Summary & Key Takeaways

  • AlphaZero, a neural network-based algorithm, challenges Stockfish, the best computer chess engine, and wins 28 games, draws 72 games, and never loses.

  • The algorithm is trained through self-play and is a more general algorithm that can also play Shogi (Japanese chess) at an extremely high level.

  • AlphaZero outperforms Stockfish while evaluating ten times fewer positions per second, suggesting an AI equivalent of intuition.

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