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Neural Network Chess Computer Abandons Brute Force For "Human" Approach
An anonymous reader writes: A new chess AI utilizes a neural network to approach the millions of possible moves in the game without just throwing compute cycles at the problem the way that most chess engines have done since Von Neumann. 'Giraffe' returns to the practical problems which defeated chess researchers who tried to create less 'systematic' opponents in the mid-1990s, and came up against the (still present) issues of latency and branch resolution in search. Invented by an MSc student at Imperial College London, Giraffe taught itself chess and reached FIDE International Master level on a modern mainstream PC within three days.
For comparison, GnuChess also plays at an International Master level. The article says this chess engine is much slower than GnuChess.
Humans are able to play chess at a high level because they are able to brutally prune the decision tree.....a grandmaster can quickly eliminate most moves as useless (although he/she will probably think of it in reverse terms: saying he/she quickly identified the important moves in the position). A computer that could combine that kind of pruning with the massive searching power would be ridiculously powerful. Better than our current computers by an order of magnitude.
"First they came for the slanderers and i said nothing."
You make some very important points in your post: for your new product to take over, it needs to do everything the old product does, and then do something better. However, take this into account:
1) The team that built Deep Blue were IBM employees, and had so they had different resources available. I doubt this student (I call him kid) had a grandmaster available to help him fine-tune his evaluator, or a fab to build custom silicon for his chess-playing machine. Also, it is very instructive to watch the documentary "Game Over" to learn a few things about how IBM used the game against Kasparov to push up their share price. That should gave some idea of the resources they have thrown at the project.
2) The same Deep Blue team were coming from the CS department at Carnegie-Mellon Univ. where they did their Ph.D. on computer chess, and studied with a prof that spent a lot of his career on this subject. They were grown-ups with a lot of experience in the field, and much wiser than a young student.
3) The current computer chess champion (Komodo) again had its evaluator fine-tuned by a grandmaster: https://en.wikipedia.org/wiki/...
4) Most of the top chess programs have been written by programmers that have written other chess engines before. Their "success" is their 3rd of 4th re-write of a chess engine, and no amount of talent can replace that kind of experience.
Given all these points (and a lot more that can be identified along the same lines) I would say this kid did a good job.