DeepMind's self-learning AlphaZero algorithm has demonstrated superhuman success at complex board games including chess, shogi, and go, recently playing about 60 million games against itself to reinforce its comprehension of game rules.
AlphaZero also has performed well against top chess-, shogi-, and go-playing algorithms, including its AlphaGo predecessor.
AlphaZero shows that DeepMind has apparently produced an algorithm capable of mastering many, if not most, board games with fixed rules.
Said DeepMind's Julian Schrittwieser, "Generally speaking, it is an algorithm trying to solve complex, multistep problems."
AlphaZero partly owes its computing ability to the use of 5,000 tensor processing units, microprocessors that drove the self-play that led to machine learning.
Schrittwieser said DeepMind aims to explore the technology's scientific and medical applications.
From Scientific American
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