AlphaGo takes Lee Sedol in a second game, going up 2-0 and setting itself up for a match victory, if it wins any of the remaining games. Here’s the game replay, or you can go to DeepMind’s Youtube channel and watch the stream. The replay has decent commentary in the chat window, albeit from strong amateur players rather than pros. You can also find some remarks from An Younggil, GoGameGuru’s resident pro, in the comment thread.
This one played out differently; fights didn’t develop until later on in the game, and Lee Sedol did indeed play a more normal opening. He also took a lot more time to play today, a sign of greater caution and probably greater respect for AlphaGo. AlphaGo, however, got ahead and remained ahead on time, and Lee was into byo-yomi while AlphaGo still had fifteen minutes of main time left. At about move 210, Lee resigned, and seemed shaken by the loss.
Which is understandable. Go players and go experts had gotten used to the idea that their pursuit was computationally unassailable. (On your average desktop PC, it still is, for now.) It’s undoubtedly a bit of a shock to them that the tables have turned so quickly; I think we from the computational side are a little better able to recover, because reading about how AlphaGo plays makes us think, “Oh, yeah, that sounds like it should work really well.”
What does it mean for go, though? Well, let’s look at what happened to chess: it’s still played at high levels, human matches draw more eyes than machine matches, you can always find an opponent who is about as good as you are, and correspondence chess is basically dead.
Wait a minute. That last one sounds bad.
Something we discovered, as chess engines capable of beating the best humans became easily available, is that chess, under the rules we use, is a very draw-happy game. In fact, in correspondence chess, where the use of AI as an aid is accepted, draws are now the overwhelmingly most likely outcomes for games: between 80% and 90% of games in the most recent few correspondence chess championships have been drawn. This is widely seen as bad: a tie is a deeply unsatisfying outcome, and it makes sorting players in a tournament a bit of a pain. Much ink both real and digital has been spilled on how to solve the draw problem in chess; if it should turn out (as it did for checkers) that optimal play in chess inevitably leads to a draw, something will probably have to be done, but no chess expert wants to commit to changing the rules in a way whose implications may not be known.
Go1 is a game whose ultimate scoring comes down to points. Playing first is known to be advantageous, so the rules traditionally spot white (the second player to move) 6.5 or 7.5 points, depending on the scoring method in use. The half-point ensures that there can be no ties, and should AI reveal heretofore unknown differences in strength from that 6.5 or 7.5 edge, the handicap can simply be adjusted.
So, where will go be in ten years? Just as vital as today, and perhaps even more so: a hundred thousand people have watched AlphaGo play Lee Sedol on Youtube, and I can’t help but think that a lot of those are English-speakers who have not yet been really introduced to the game. Although I still find go inscrutable, and don’t expect I’ll ever be anything more than a poor recreational player, some of the fellow AI nerds watching will likely be drawn to the game, and once you have us nerds on your side…
1. And, for that matter, most solutions for how to handle tafl matches: bidding on the number of turns a win will take, and playing two-game matches, switching sides and giving the win to the player who wins fastest (if they split the games), both yield a natural, built-in way to compensate for shifts in observed balance. Furthermore, since tafl rules are, as yet, not standardized in any meaningful way, differing tournament to tournament, tafl has an edge on chess that go does not: the tafl community is very open to rules changes to support greater balance or deeper strategy.