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.

In what even I, who predicted an AlphaGo series win, regard as a bit of a surprise, AlphaGo defeated Lee Sedol in the first game of the five-game match. AlphaGo won by Lee’s resignation, which came 186 moves into the game, up by (the consensus seems to be) between three and seven points at the time.

Our liveblog has our detailed commentary from the first 120 or 130 moves of the game, after which I went to bed. Here, though, are some highlights and post-game thoughts.

• AlphaGo is much stronger now than it was before. Michael Redmond, a 9-dan pro who was engaged to commentate, remarked as much: AlphaGo didn’t make any serious mistakes.
• One of AlphaGo’s suspected weaknesses before the game was coming out ahead after complicated fights; we thought this because it avoided getting involved in very many serious fights against Fan Hui. It turns out that AlphaGo can indeed fight, and fight well, as we saw with the fight at the top center of the board that developed early on in the running. AlphaGo came out ahead there, or at the very least, ended up with a strong enough position that she¹ could hold it and use it as a base to fight over the center of the board, while strengthening her position in the top left.
• Lee Sedol seemed a little flustered by the level of AlphaGo’s play. He, like a lot of go experts, seemed to make the twin assumptions that: 1) AlphaGo would not improve dramatically between October and today, and 2) AlphaGo was not vastly stronger than Fan Hui in October. AI experts suspected that the first item was false, and they turned out to be correct. That said, it’s hard to fault go experts for lacking experience with AI systems, and not realizing that six months is an eternity for a learning system to improve. It is much easier to fault go experts for the second point, which is one of simple math. A 9-dan pro should beat a 2-dan pro (like Fan Hui) 95%-99% of the time. Although the known AlphaGo-Fan Hui games are too small a sample to draw firm conclusions from, it is telling that AlphaGo defeated Fan Hui by large margins or resignation in all of the long-time games, losing only during quick play; either way, its record suggests that AlphaGo is properly probably at least 5-dan, and may be up at the 9-dan level.
• It’s possible that a lot of AlphaGo’s added strength came from the extra time to think, but it’s difficult to do much more than speculate on that front. Suffice it to say that this could be a contributing factor.
• One thing we haven’t seen very much of is AlphaGo’s ability in close endgames. Given that endgames are basically tests of reading, and AlphaGo has proven to be superb at that so far, I wouldn’t expect its endgames to be any worse than its middle games, but Lee Sedol may nevertheless aim to explore AlphaGo’s endgame capabilities with a more relaxed early game, next time out.
• One of my prematch predictions seems to have turned out true: computers play differently than do humans, and it may be that humans play more optimally than humans. In the late midgame, around move 120 to 150, in the bottom right and center right of the board, AlphaGo played very cautiously, keeping stones alive (particularly in the bottom right) and denying Lee Sedol territory, without aiming to decisively win the corner itself. Go experts would denounce this sort of play as ‘slow’, given that it seems to yield tempo, but if AlphaGo is confident that she’s already ahead, she need not play to do any more than deny Lee the win.

That’s all for now. We’ll be back with more at the end of the weekend, or after any particularly interesting games, and we might liveblog the final game in the series on Monday night. If so, you’ll hear about it here.