Considering he's beaten Lee Sedol and Gu Li, who knows how this will play out.
But no doubt he'll talk to Sedol about his take away from playing AlphaGo, especially since part of playing against any computer is reverse engineering its decision tree.
It's not simply an algorithm. It as learning computer. It remembers and learns from previous matches. He can probably play more matches against AlphaGo if he wants to but the computer will keep getting better and probably faster than he is.
It's worth mentioning that Lee Sedol mentioned in an interview that even if he loses a single game against AlphaGo, he will have lost the match. He was expecting to win all 5 games.
Did Lee Sedol have access to a dataset of AlphaGo games in preparation for this match series? I wonder if it would help him if he could study the computers moves and strategies in other matches.
I don't think that AlphaGo was trained more on Lee Sedol's games, than on others' games. The team said that they can't find computer weaknesses until AlphaGo plays against top caliber.
> It would be surprising if AlphaGo hadn't be trained on historic matches with Lee giving him an early edge until Lee can adapt.
This kind of learning doesn't work like that. It doesn't learn from specific examples, or make meaningful inferences from single data points. It learns tiny gradients from millions of examples. If we had an approach that could create a meaningfully distinct strategy depending on {whether we included or excluded {every game Lee Sedol has played in his life} from training}, that would be wildly more significant than just beating him.
The question (as Ke Jie says, but the headline hides) is not if AlphaGo will beat him, but when.
I was taught a computer would not beat a professional player in my life time. Now, there is maybe one player who can beat AlphaGo. I guess this won't be true for too long. When? That would be an interesting bet.
> Either way, it's interesting to note that AlphaGo had literally thousands of games to learn from to find weaknesses in human play, but Lee Sedol seems to have only needed 3 before he was able to find weaknesses in AlphaGo's play.
To be fair we can't know how many games Sodol played in his own head to figure this out.
AlphaGo has also improved very quickly.
Without doubt, the AlphaGo seen playing against Fan Hui would have lost against Lee Sedol. But in a couple of months its playing level raised significantly.
Lee Sedol said he could beat AlphaGo, based on the Fan Hui games. Ke Jie said he could beat AlphaGo, based on the Lee Sedol games.
Ke Jie belongs to a similar category than Lee Sedol, and we could see how Lee Sedol was completely dominated by AlphaGo, 3-0 so far. It is not unreasonable to say AlphaGo will most likely beat Ke Jie, and even if that doesn't happen the first time, AlphaGo can be improved by adding more infrastructure and training time.
I suspect that with time Lee may be able to even out the advantage. It would be surprising if AlphaGo hadn't be trained on historic matches with Lee giving him an early edge until Lee can adapt.
I hope that Lee can adapt, but I doubt it. Lee will be training at a slow pace analyzing 4 or 5 games. AlphaGo will be playing hundreds of millions against itself - and it is a worthy competitor of itself already.
Although Go and AlphaGo are very different than Chess and DeepBlue, there is one interesting analogy... Once chess beat us, they pulled ahead very quickly and the gap isn't closing. The ELO rating of top chess programs [0] is pulling away from top humans [1]. There are natural differences between computational superiority and creativity, but it seems like AlphaGo has captured a lot of the latter.
Just that in that time AlphaGo will have played millions of more matches against itself, learning at a much faster rate than him. Not sure, he might still beat the machine though. That needs to be seen.
This is different because while AlphaGo did beat Lee Sedol a while ago. Lee was ranked 2nd in the world for Go. Ke Jie, AlphaGo's current opponent, is ranked 1st in the world.
Wasn't reading the whole thread, but was it possible for Lee Sedol to play against the final AlphaGo before? Although AlphaGo seems to be a huge achievement I would find the lack of training before a bit unfair as AlphaGo was probably able to play lots of Games from Sedol before.
It's only been the first round and I'm not throwing in the towel yet. Unlike AlphaGo, Lee Sedol has an opportunity to learn from their opponents since AlphaGo takes about 30 days of wall clock time to train the networks. There will be 5 games during the next week.
Despite my optimism, the writing is on the wall. AlphaGo and algorithms like it will only improve as you throw more CPU time at them. I actually want Lee Sedol to win, not because it would uphold some kind of human supremacy but because I want to see the AI guys put some more effort (and CPU time) into it. It would be a real shame if they'd win on their first attempt.
As a programmer and a go player, I knew this day would come, but I'm a bit disappointed that this is how it happened, for two reasons:
1. As the game of go progresses, the number of reasonable moves decreases, so that as the game progresses, players on average play closer and closer to optimally. By the end of the game, even weak amateurs can calculate the optimal move. Logically, I would guess that stronger players are able to play optimally earlier than weak ones. Lee Sedol is known for his strong middle and endgame, often falling behind early on and making it up late in the game. He is so strong at this that he has driven an entire generation of go players to developing very strong endgame. But AlphaGo, running Monte Carlo simulations, almost certainly can brute force the game earlier than Lee Sedol can. Lee Sedol is playing AlphaGo on its own turf. A player known for their opening prowess, such as Kobayashi Koichi in his heyday, might have had an advantage that Lee Sedol doesn't. (Note: I'm not strong enough to analyze Lee Sedol or Kobayashi Koichi's play styles; I'm repeating what I've heard from professionals.)
2. I hoped that when an AI beat a pro at go, it would be with a more adaptive algorithm, one not specifically designed to play go. If my understanding of AlphaGo is correct, it's basically just Monte Carlo: the advances made were primarily in improving the scoring function to be more accurate earlier, and the tree pruning function, both of which are go-specific. It's not really a new way of thinking about go (at least, since Monte Carlo was first applied to go). It's just an old way optimized. The AI can't, for example, explain its moves, or apply what it learned from learning go to another game. It's certainly a milestone in Go AI, and I don't want to downplay what an achievement this is for the AlphaGo developers, but I also don't think this is the progress toward a more generalized AI that I hoped would be the first to beat a professional.
AlphaGo can be beaten. It uses reinforcement learning so it will perform the set of moves that in the past led to its win. So predictable. Sedol just needs to take control and make it play in a predictable fashion. Also, perhaps play obscure moves that AlphaGo wouldn't have trained on. Perhaps next year's Go winner will have a PhD in computer science.
My rough summary of the match, informed by the various commentators and random news stories.
Game 1: Lee Sedol does not know what to expect. He plays testing moves early and gets punished, losing the game decisively.
Game 2: Lee Sedol calms down and plays as if he is playing a strong opponent. He plays strong moves waiting for AlphaGo to make a mistake. AlphaGo responds calmly keeping a lead throughout the game.
Game 3: Lee Sedol plans a strategy to attack white from the start, but fails. He valiantly plays to the end, creating an interesting position after the game was decided deep in AlphaGo's territory.
Game 4: Lee Sedol focuses on territory early on, deciding to replicate his late game invasion from the previous game, but on a larger scale earlier in the game. He wins this game with a brilliant play at move 78.
Game 5: The prevailing opinion ahead of the game was that AlphaGo was weak at attacking groups. Lee Sedol crafted an excellent early game to try to exploit that weakness.
Tweet from Hassabis midgame [0]:
#AlphaGo made a bad mistake early in the game (it didnt know a known tesuji) but now it is trying hard to claw it back... nail-biting.
After a back and forth late middlegame, Myungwan Kim 9p felt there were many missed chances that caused Lee Sedol to ultimately lose the game by resignation in the late endgame behind a few points.
Ultimately, this match was a momentous occasion for both the AI and the go community. My big curiosity is how much more AlphaGo can improve. Did Lee Sedol find fundamental weaknesses that will continue to crop up regardless of how many CPUs you throw at it? How would AlphaGo fare against opponents with different styles? Perhaps Park Jungwhan, a player with a stronger opening game. Or perhaps Ke Jie, the top ranked player in the world [1], given that they'd have access to the game records of Lee Sedol?
I also wonder if the quick succession of these games on an almost back-to-back game schedule played a role in Lee Sedol's loss.
Myungwan Kim felt that if Lee Sedol were to play AlphaGo once more, the game would be a coinflip since AlphaGo is likely stronger, but would never fix its weakness between games.
Fan Hui has already learned from AlphaGo. He's been playing matches against her regularly, and (perhaps as a result of that) won _all_ his games in the last European championship.
But no doubt he'll talk to Sedol about his take away from playing AlphaGo, especially since part of playing against any computer is reverse engineering its decision tree.
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