Last week Hydra, a chess-playing computer, completed its rout of Michael Adams, the seventh-ranked human player in the world. Hydra won five of six games, and Adams barely escaped with a draw in the other game. ChessBase has the details, including a page where you can play through the six games.
It’s time to admit that computers play better chess than people.
This may seem inevitable in hindsight, but for the longest time people insisted that human chess players had something special which computers could never duplicate. That was true, up to a point. Computers have never succeeded at approaching chess the way people do. The best human players make subtle, intuitive judgments that are probably based on pattern-matching deep in their neural circuitry. Often an elite player cannot verbalize how he knows that one configuration of pieces is dangerous when another nearly identical configuration is not. He just knows. He does calculate in the “if he does this, I’ll do that, then he’ll do this, …” fashion, but only when necessary.
Every attempt to transplant human “intelligence” into a chess computer has failed miserably. Computers understand very little about chess. They rely instead on rudimentary judgment about chess positions, coupled with prodigious calculation, looking ahead at hundred of millions or billions of possible board positions.
Chess players classify game situations into two categories, “tactical” and “positional”. Tactical situations feature direct, violent clashes between pieces, and call mostly for calculation, with intuition as a backstop. Positional situations are slow and subtle, requiring deep judgments and long maneuvers. Everybody expected computers to excel at tactics. The big surprise is that the computer approach seems to work well in positional situations too. Somehow, calculation can substitute for judgment, even when conditions seem to require judgment.
This is not to say that it’s easy to create a chess computer that plays as well as Hydra. Quite the contrary. Great effort has been spent on perfecting computer chess algorithms. That effort has gone not to teaching computers about chess, but to improving the algorithms for deciding when to cut off calculations and when to calculate more deeply. Indeed, algorithmic improvements have been a much bigger factor even than Moore’s Law over the years.
Chess computers have succeeded by ignoring what human chessplayers do best, and doing instead what computers do best. And what computers do best is to run programs written by very clever human programmers.
