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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.
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/...
We can't (and the existing AI Chess players don't try to) brute force every move in Chess. The compute resource needed would be quite extreme, and if you could do it at all, you'd _solve_ Chess and we'd be able to say confidently "White always wins" or, perhaps as likely "It's a draw" when players know what they're doing. For example we have solved Tic-Tac-Toe and Connect Four, in both those games the first player wins if they play perfectly, regardless of what their opponent does.
AI Chess players begin with an opening "book" which is a collection of sets of early moves players have found to be strong. Human players typically learn to play Chess openings this way too, but from an actual written books as learning that some openings are weak the hard way can be disheartening and also misleading. Once book moves are exhausted, or if the opponent deviates from the book and plays in an unorthodox style, the AI has a function that assesses arbitrary positions and ranks them as more or less desirable. For example, a position where you have lost a pawn is generally worse than one where you haven't, but one where you threaten the opponent's King with a Rook may be worth the loss of the pawn. It can "brute force" lots of possible plays and responses, to see which ones consistently end in good positions and favour those, but by no means enough to foresee the end of the game from the beginning.
Towards the end, what we _have_ brute forced is the endgame tablebases. These tell us how in practice a win can be forced by one side, or a draw reached, from particular positions, usually with only a handful of pieces remaining such as a King and two Bishops versus a King with one Bishop and a Knight. But there is no prospect of the tablebases eventually expanding to cover the whole game. They're already _enormous_ and they cover only an infinitesimal part of the game.
Anyway, while brute forcing Chess is implausible and will probably never happen, brute forcing Go isn't merely implausible, it would require far more compute and storage than could conceivably exist in our universe. It is _literally impossible_. Go is being attacked by AIs in the same way humans do it, by intuition. It's just that we're scared to label what the machines do as "intuition", or the same, to label human intuition as just a neutral network blindly associating certain patterns with success despite not having any communicable "understanding" of what it has done.
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.