chess-computing relationship is as old as computer science
itself. Pioneers like Babbage, Turing, and von Neumann worked to
create hardware and algorithms to analyze and play chess.
It’s also the most widely-studied game in the history of
Google’s DeepMind has a great track record when it comes to
beating humans at board
games; it does this with a long-term aim to create
something that can be applied to a varied range of situations.
In a new paper, showing off its latest work, DeepMind has described how its AI program has
learned to play chess and a couple of other board games.
With just 4 hours of training, DeepMind’s AlphaZero AI
developed superhuman performance in chess. After a “self-play
reinforcement learning” of 300k steps, it outperformed
Stockfish, the world’s best chess-playing program. It’s worth
noting that the AI was programmed with only rules of chess and
no game strategies were fed.
The AlphaZero algorithm is a more generic version of AlphaGo
Zero algorithm that was used to play Go.
Talking about the results of 100 games against Stockfish,
AlphaZero won 25 games as white and first mover advantage. With
black, it won 3 games. Rest of the games were drawn and
Stockfish wasn’t able to register a single win.
For training, AlphaZero used a single machine with 4 TPUs.
Stockfish played at their strongest skill level using 1GB hash
and 64 threads.
Find more interesting technical details and game results in