Chess players have analyzed the games that AlphaZero played against the Stockfish program and found many innovative moves and tactics. Chessbase has an analysis that discusses the quality of its games and its playing style.
AlphaZero uses Monte Carlo tree search but has a combined policy and value network for evaluating moves. Previously AlphaGo had separate neural nets for policy and value.
There is a Stanford article that has a simplified tutorial on AlphaZero.
Some have noted that Stockfish had certain settings disabled when it played and lost to AlphaZero.
AlphaZero played a match against the latest and greatest version of Stockfish, and won by an incredible score of 64 : 36, and not only that, AlphaZero had zero losses (28 wins and 72 draws)!
Stockfish is the best chess playing program. In games versus AlphaZero, the Stockfish program was on a computer that was running nearly 900 times faster. AlphaZero was calculating roughly 80 thousand positions per second, while Stockfish, running on a PC with 64 threads (likely a 32-core machine) was running at 70 million positions per second. To better understand how big a deficit that is, if another version of Stockfish were to run 900 times slower, this would be equivalent to roughly 8 moves less deep.
AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variations – arguably a more “human-like” approach to search, as originally proposed by Shannon. Figure 2 shows the scalability of each player with respect to thinking time, measured on an Elo scale, relative to Stockfish or Elmo with 40ms thinking time. AlphaZero’s MCTS scaled more effectively with thinking time than either Stockfish or Elmo, calling into question the widely held belief that alpha-beta search is inherently superior in these domains.
AlphaZero settled on a couple of openings by the end of its learning process. The English Opening and the Queen’s Gambit.
The paper also came accompanied by ten games to share the results. It needs to be said that these are very different from the usual fare of engine games. If Karpov had been a chess engine, he might have been called AlphaZero. There is a relentless positional boa constrictor approach that is simply unheard of. Modern chess engines are focused on activity, and have special safeguards to avoid blocked positions as they have no understanding of them and often find themselves in a dead end before they realize it. AlphaZero has no such prejudices or issues, and seems to thrive on snuffing out the opponent’s play. It is singularly impressive, and what is astonishing is how it is able to also find tactics that the engines seem blind to.
What lies ahead
So where does this leave chess, and what does it mean in general? This is a game-changer.
This completely open-ended AI able to learn from the least amount of information and take this to levels hitherto never imagined is not a threat to ‘beat’ us at any number of activities, it is a promise to analyze problems such as disease, famine, and other problems in ways that might conceivably lead to genuine solutions.
Chess and Go programs will include deep learning and the published techniques and will be vastly improved.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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