161–170 of 428 posts
Impressive work by Google research team. I'm both impressed and scared. This is our Deep Blue moment folks. a history is made.
IBM lost in 1996, 2-4, and then won in 1997, 3.5-2.5. If they had played a third match with Kasparov, especially a longer match, it is not at all clear that they would have won.
Kasparov asked for a third match of 10 games, to be played over 20 days, but IBM would not give it to him.
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Challenging Ke Jie is way too small a goal for DeepMind at this point. I wonder if even the idea from the AGA stream today, to get all the best pros in the world together and challenge AlphaGo as a team, is enough. Perhaps releasing the core AlphaGo as open source (to the extent it's not dependent on internal Google machinery), or at least publishing its trained model, may be the next step. Let people "challenge them…
I wonder if even the idea from the AGA stream today, to get all the best pros in the world together and challenge AlphaGo as a team, is enough. Has this been tried? That is, have Players 2-9 (or some subset) ever competed as a group against a dominant Player 1? Unless it's been tested, I wouldn't take it for granted that a group would beat an individual.
There Go Seigen, the genius who brought about the Go revolution that Redmond talked about at post-game conf today, was beaten by Honinbou Shusai + his students.
Also, "lesser" pros routinely call out mistakes in master games (and the masters agree). Games are often decided by easily avoidable mistakes, even at the highest level.
Perhaps the last big question was whether AlphaGo could play ko positions. AlphaGo played quite well in that ko fight and furthermore, even played away from the ko fight allowing Lee Sedol to play twice in the area. I definitely did not expect that. Major credit to Lee Sedol for toughing that out and playing as long as he did. It was dramatic to watch as he played a bunch of his moves with only 1 or 2 seconds left on…
A possible explanation is:
During self reinforcement learning, AlphaGo learned to minimize Ko potential by maximizing its probability of winning through diminished available Ko moves.
It would be interesting to see how AlphaGo would be able to capitalize on a game that emphasized Ko play, but that would take more time with AlphaGo to emphasize that kind of play.
edit: I'm not sure why, but I think Lee Sedol is partly holding back, or not playing at his maximum ability. It feels like these games are more along the lines of query games.
I look forward to the next two games because I'm 100% certain Lee Sedol is going to query the AI with some new queries.
Seems to be playing at super-human levels. I'm no where near a strong player but it seems like AlphaGo is far ahead of Lee Sedol.
The Chinese 9 Dan player Ke Jie basically said the game is lost after around 40 mins or so. He still thinks that he has a 60% chance of winning against AlphaGo (down from 100% on day one). But I doubt Google will bother to go to China and challenge him.
Challenging Ke Jie is way too small a goal for DeepMind at this point. I wonder if even the idea from the AGA stream today, to get all the best pros in the world together and challenge AlphaGo as a team, is enough. Perhaps releasing the core AlphaGo as open source (to the extent it's not dependent on internal Google machinery), or at least publishing its trained model, may be the next step. Let people "challenge them…
I can think of a worthy goal for AlphaGo: make a program which can play better than top pros which runs on a macbook pro.
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Alas, time is not on meat's side in this one.
I'll grant that for this very constrained problem ;-) I respect your work a lot. I've studied and used ML myself, including gensim, in industry. I've given nowhere near your level of contribution to society / the field. My opinion is true AI is quite a ways off. I haven't read anything from a ML researcher that says it isn't. Perhaps you weren't saying true AI is nearer.
So let me ask you this. What would you consider to be "true AI"? At what point are you willing to say, "Okay, that's it, computers are just plain smarter than we are?" Because, frankly, it seems to me that that day is getting closer and closer.
Saying that AIs can't be smarter than humans because they don't think and act like humans is like saying that airplanes don't "truly" fly because they don't flap their wings.
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I was thinking the opposite. AI has already been beating humans on smaller boards, so maybe the only chance we have is to start playing go on bigger and bigger boards.
Huh, interesting. I'm not well-versed in Go at all. It would be interesting if AlphaGo's maneuvers remain opaque to humans for an extended period of time. Can anyone at this point say with confidence that its strategies will indefinitely remain unknown?
My (long) commentary here: https://www.facebook.com/yudkowsky/posts/10154018209759228 Sample: At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for probability of long-term victory rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol's probabili…
It's important to remember that this is an accomplishment of humanity, not a defeat. By constructing this AI, we are simply creating another tool for advancing our state of being. (or something like that)
It is not a tool if you can't control it. Politicians fool countries delivering empty promises about better health, education and security. A supraintelligent AI could promise making humans rich, healthy and powerful, to then break its promise and dominate the world. https://en.wikipedia.org/wiki/AI_box
Or it could devote its entire power to making human lives the best and most comfortable they can be because humanity is some super-precious resource in the universe and it feels it's unimportant because it's just a bunch of silicon and electrons.
Supraintelligent AI being evil is FUD imho because we can't reason about supraintelligent AI.
In 1978 chess IM David Levy won a 6 match series 4.5-1.5 - he was better than the machine, but the machine gave him a good game (the game he lost was when he tried to take it on in a tactical game, where the machine proved stronger). It took until 1996/7 for computers to match and surpass the human world champion.
I'd say the difference was that for chess, the algorithm was known (minimax + alpha-beta search) and it was computing power that was lacking - we had to wait for Moore's law to do its work. For go, the algorithm (MCTS + good neural nets + reinforcement learning) was lacking, but the computing power was already available.