Unfortunately for ghosts, now it’s computers that are revealing these goosebump-inducing moves.
As many will remember, AlphaGo—a program that used machine learning to master Go—decimated world champion Ke Jie earlier this year. Then, the program’s creators at Google’s DeepMind let the program continue to train by playing millions of games against itself. In a paper published in Nature earlier this week, DeepMind revealed that a new version of AlphaGo (which they christened AlphaGo Zero) picked up Go from scratch, without studying any human games at all. AlphaGo Zero took a mere three days to reach the point where it was pitted against an older version of itself and won 100 games to zero.
Now that AlphaGo’s arguably got nothing left to learn from humans—now that its continued progress takes the form of endless training games against itself—what do its tactics look like, in the eyes of experienced human players? We might have some early glimpses into an answer.
AlphaGo Zero’s latest games haven’t been disclosed yet. But several months ago, the company publicly released 55 games that an older version of AlphaGo played against itself. (Note that this is the incarnation of AlphaGo that had already made quick work of the world’s champions.) DeepMind called its offering a “special gift to fans of Go around the world.”
Since May, experts have been painstakingly analyzing the 55 machine-versus-machine games. And their descriptions of AlphaGo’s moves often seem to keep circling back to the same several words: Amazing. Strange. Alien.
“They’re how I imagine games from far in the future,” Shi Yue, a top Go player from China, has told the press. A Go enthusiast named Jonathan Hop who’s been reviewing the games on YouTube calls the AlphaGo-versus-AlphaGo face-offs “Go from an alternate dimension.” From all accounts, one gets the sense that an alien civilization has dropped a cryptic guidebook in our midst: a manual that’s brilliant—or at least, the parts of it we can understand.
Will Lockhart, a physics grad student and avid Go player who codirected The Surrounding Game (a documentary about the pastime’s history and devotees) tried to describe the difference between watching AlphaGo’s games against top human players, on the one hand, and its self-paired games, on the other. (I interviewed Will’s Go-playing brother Ben about Asia’s intensive Go schools in 2016.) According to Will, AlphaGo’s moves against Ke Jie made it seem to be “inevitably marching toward victory,” while Ke seemed to be “punching a brick wall.” Any time the Chinese player had perhaps found a way forward, said Lockhart, “10 moves later AlphaGo had resolved it in such a simple way, and it was like, ‘Poof, well that didn’t lead anywhere!’”
By contrast, AlphaGo’s self-paired games might have seemed more frenetic. More complex. Lockhart compares them to “people sword-fighting on a tightrope.”