One Giant Step for a Chess-Playing Machine
The stunning success of AlphaZero, a deep-learning algorithm, heralds a new age of insight — one that, for humans, may not last long.
By Steven Strogatz
December 26, 2018
In early December, researchers at DeepMind, the artificial-intelligence company owned by Google’s parent corporation, Alphabet Inc., filed a dispatch from the frontiers of chess.
A year earlier, on Dec. 5, 2017, the team had stunned the chess world with its announcement of AlphaZero, a machine-learning algorithm that had mastered not only chess but shogi, or Japanese chess, and Go. The algorithm started with no knowledge of the games beyond their basic rules. It then played against itself millions of times and learned from its mistakes. In a matter of hours, the algorithm became the best player, human or computer, the world has ever seen.
The details of AlphaZero’s achievements and inner workings have now been formally peer-reviewed and published in the journal Science this month. The new paper addresses several serious criticisms of the original claim. (Among other things, it was hard to tell whether AlphaZero was playing its chosen opponent, a computational beast named Stockfish, with total fairness.) Consider those concerns dispelled. AlphaZero has not grown stronger in the past twelve months, but the evidence of its superiority has. It clearly displays a breed of intellect that humans have not seen before, and that we will be mulling over for a long time to come.
