How the Computer Beat the Go Player

Posted by K R on

God moves the player, he in turn, the piece. But what god beyond God begins the round of dust and time and sleep and agonies? —Jorge Luis Borges, from “Chess,” 1960
The victory in March of the computer program AlphaGo over one of the world's top handful of go players marks the highest accomplishment to date for the burgeoning field of machine learning and intelligence. The computer beat Lee Se-dol at go, a very old and traditional board game, at a highly publicized tournament in Seoul in a 4–1 rout. With this defeat, computers have bettered people in the last of the classical board games, this one known for its depth and simplicity. An era is over, and a new one has begun. The methods underlying AlphaGo, and its recent victory, have startling implications for the future of machine intelligence.


The ascent of AlphaGo to the top of the go world has been stunning and quite distinct from the trajectory of machines playing chess. Over a period of more than a decade a dedicated team of hardware and software engineers hired by IBM built and programmed a special-purpose supercomputer named Deep Blue that did one thing and one thing only: play chess by evaluating 200 million board positions per second. In a widely expected development, the IBM team challenged then reigning world chess champion Garry Kasparov. In a six-game match played in 1996, Kasparov prevailed against Deep Blue by three wins, two draws and one loss but lost a year later in a historic rematch 3.5 to 2.5. (Scoring rules permit half points in the case of a draw.) Read More: Scientific American

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