He ignores some details attentive viewers might wonder about (like the handicap, or “komi,” that figures into a game’s score), and as the games unfold he almost never visualizes how a given move affects play. Setting aside the difficulty of explaining advanced computing to a lay audience, Kohs doesn’t do as well as he might have at chronicling the games themselves. But this doc wouldn’t exist if, as everyone expected would happen, Sedol wiped the floor with the computer. This is the conflict occupying the bulk of the film, and those who don’t know the result will do well to remain ignorant until viewing. So the DeepMind team arranged a 2016 match in Seoul against one of the world’s best players, Lee Sedol.

Response to this news in Asia was skeptical, claiming Fan was not a sufficiently impressive player. But we quickly see the results, as a former European champion player, Fan Hui, is recruited for a five-game tournament against the AlphaGo program and loses them all. How this works is, unsurprisingly, a challenge for the film to explain. Rather than having Go players teach a computer how to play, they set things up so the program could learn for itself. The Google-owned DeepMind company, “an Apollo kind of program” for artificial intelligence, decided to take on this challenge by using neural networks. So, while the things some experts say in the doc’s intro scenes - playing Go is “like putting your hand on the third rail of the universe,” one claims - are needlessly overblown, writing a computer program to play this game makes writing the Garry Kasparov-beating Deep Blue chess program look like coding Pong.

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