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Computer Beats Go Champion
Koreantoast writes: Go (weiqi), the ancient Chinese board game, has long been held up as one of the more difficult, unconquered challenges facing AI scientists... until now. Google DeepMind researchers, led by David Silver and Demis Hassabis, developed a new algorithm called AlphaGo, enabling the computer to soundly defeat European Go champion Fan Hui in back-to-back games, five to zero. Played on a 19x19 board, Go players have more than 300 possible moves per turn to consider, creating a huge number of potential scenarios and a tremendous computational challenge. All is not lost for humanity yet: DeepMind is scheduled to face off in March with Lee Sedol, considered one of the best Go players in recent history, in a match compared to the Kasparov-Deep Blue duels of previous decades.
What makes this especially interesting, is the victory was not achieved with the sort of brute-force approach used by Deep Blue in chess. This used a deep neural net, and algorithms similar to how we believe that humans think. Last time I heard about this, they could consistently beat humans on a 9x9 board, and were working on 13x13. I was surprised to hear that can already win on a full sized 19x19 board. I thought that was still a few years away. This is amazing progress.
I've read the paper.
It doesn't quite use a "brute-force" approach, but it certainly does use significant, and intelligently designed, Monte Carlo searches which are informed by well-trained neural networks. The neural-network alone approach, without any Monte Carlo search during play, is not as strong, though it does appear to equal a state of the art conventional Go program. See Figure 4b.
And the training of the neural networks and construction of their training sets certainly did need quite a bit of 'brute force' as well as 'efficiently wielded force in large quantity'.
No, this is not an accurate understanding of Go strategy or how it is played at the highest level.
In fact, if the game is played in the way you describe, previous computer algorithms were quite good at analyzing the local interactions of pieces, yet were roundly defeated even by top-level amateurs with handicaps. The reason is that at more sophisticated levels of play, one's skill level is correlated with how one perceives and evaluates the entire board. There is a sort of "gestalt" of Go that good players seem to grasp in ways that are very difficult to objectively describe, and sometimes a stone placement can seem arbitrary but become pivotal many, many moves later. This is reflective of a deep and global strategy that computer algorithms--at least until now, it seems--have had tremendous difficulty in emulating.
There is a name for this "not AI" comment: The AI effect. Basically, whatever can be done with a machine is automatically considered "not AI", because it's no longer magical, just engineering.
https://en.wikipedia.org/wiki/...
As wickerprints pointed out, this is completely false. A good move in a local position in Go may not be the best move overall and may, in fact, be a bad move when other areas on the board are taken into consideration. If a computer program split the board into smaller and smaller sections it could very easily get confused by a good player. Also, the number of moves possible at any given time in Go is exponentially higher than in Chess; you can brute force every possible path in Chess, you can't (not yet) in Go. It wasn't that it was more popular; it was much easier.
-SaNo
I googled Fan Hui: one source says he's 8 dan amateur, another that he's 2 dan pro. That's only a little bit better than go programs have been for several years, and much weaker than the best professional players. If he's a top player in Europe, that mostly says that go isn't played at a very high level in Europe. I think that the progress that has been made on go software is really great, but the claim to have beat a 'go champion' seems a bit of a spin.
let's play global thermonuclear war
Videos are available.
a,e,i,o,u and sometimes w and y (at be if of up cwm by)
Poker is a game of incomplete knowledge - you don't know what cards are in the other players hands.
Go is a game of complete knowledge. As is chess. And draughts.
The two classes are completely different.
Birds are not dinosaur descendants;birds are dinosaurs, for all useful meanings of "birds", "are" and "dinosaurs"
You read...the...paper?!
I don't know how they do things wherever you come from, but this is Slashdot.
Next time, just read the headline and skim the summary, then spout off whatever pops into your head.
Informed commentary, sheesh!
And the training of the neural networks and construction of their training sets certainly did need quite a bit of 'brute force' as well as 'efficiently wielded force in large quantity'.
To be fair, it'd take a fair bit of brute force training for a human to beat Fan Hui too - you aren't exactly going to rock up, read a pamphlet explaining the rules and win 5-0 on your first ever attempt at the game.