It taught itself chess in quite a human-like way, developing an “intuition” like no other chess machine has ever done, and it combined this with an amount of cold calculation. Instead their highly-honed intuition guides them to focus their calculation on the most relevant lines. Stronger players tend to calculate fewer variations than weaker ones. This brings to mind a remark made by Jonathan Rowson after Michael Adams crushed him in a match in 1998: “I was amazed at how little he saw.” Quite the opposite in fact: Stockfish examined 70 million positions per second while AlphaZero contented itself with about 0.1 percent of that: 80,000 per second. It didn’t calculate more variations than Stockfish. Google headquarters in London from inside, with the DeepMind section on the eighth floor. Nine hours and 44 million games of split-personality chess later, AlphaZero had (very possibly) taught itself enough to become the greatest chess player, silicon- or carbon-based, of all time. The quality of chess in game two was a just a tiny bit better than the first. AlphaZero had taught itself its first chess lesson. At the end of this game, AlphaZero had learned that the losing side had done stuff that wasn’t all that smart, and that the winning side had played better. Game one would have involved totally random moves. It then started learning chess by playing games against itself. It was primed with the rules of chess, and nothing else. AlphaZero was developed by DeepMind (a Google-owned company) to specialize in learning how to play two-player, alternate-move games. How is it different from other engines and why is it so much better? In this two-part article I’ll try to explain a bit of what goes on under AlphaZero’s hood.įirst, let’s reflect on what happened. The reactions from the chess community to this match ranged from admiration to utter disbelief. By now you've heard about the new kid on the chess-engine block, AlphaZero, and its crushing match win vs Stockfish, the strongest open-source chess engine.
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