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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.
the big Computer tournaments are run by TCEC at chessdom.com - there it would be paired against other engines, of whom Komodo and Stockfish have been pretty much dominating every year since season 2 -
truth is, all computer chess is computer vs. computer nowadays - the losses come from different evaluations of positions - then the programmers try to correct it, etc - but since all engines are running the same hardware with resources, the best performers should win -
you can follow Season 8 (round 1b right now) here
http://tcec.chessdom.com/live....
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ah honey, we're all resplendent - Bill Mallonee
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."
Well, yes, that kind of is the issue. The computation chess masters make, the actual thoughts, could be handled on a 1950 computer no problem.
The question is how. It isn't brute force, though they do delve into plies ass desired. The real trick is knowing which handful to explore mentally. And if it were just pattern matching against known games, it would ne done by computer already that way, too.
(-1: Post disagrees with my already-settled worldview) is not a valid mod option.
It only plays at around the level of GnuChess, so don't be impressed.
You should be impressed. Not by it's level of play (which is not impressive), but by the fact that it:
1. Taught itself to play
2. Reached FIDE Master Level in THREE DAYS.
To be honest, I'm not even sure why this is a story.
See #1 and #2 above.
Are we sure it did not just learn how to install and launch GnuChess?
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I thought Claude Shannon https://en.wikipedia.org/wiki/... wrote the first program to play chess. Among other things he dabbled in.
Sigh. The reason that humans experts are no longer competitive is because human experts prune where Deep Ply fears to trust static analysis. Pitted against a relentless algorithm which resists intuitive pruning, grand-master human pruning leaks a full pawn or two per game.
It's damn amazing how well grand-master level pruning actually works, but don't mistake this for flawless chess. Beautiful? Maybe. Flawless? Not even close.
When it was still somewhat competitive between man and machine, the human chess players would think they were pressing an overwhelming advantage, only to discover themselves mired in tiny, unanticipated tactical disadvantages move after move after move after move. "The damn thing keeps finding these fiddling resources!" If you weren't careful, you could easily lose from what had initially appeared to be a won position (and it probably would have been, against a human opponent blind to all those fiddling resources).
The trick for the competitive chess programmer was to achieve the right balance in the static evaluator so that tangible material gains didn't consistently outweigh less tangible advantage of tempo. Matthew Lai in his paper does not seem to grasp this essential trajectory of computer chess. He seems to think it's remarkable that his Oldsmobile displays more rigidity on the impact sled than the lunar lander, when it's pretty clear to everyone else involved that no Oldsmobile ever made was going to win the space race. The ply-based chess engines had their static evaluators hand-tuned by experts over many decades within a space gram clock-cycle budget.
Until he actually defeats all these programs on existing commodity hardware at existing tournament time controls, he's comparing watermelons to kiln-dried coconut flakes.
It's the same problem with new technology. It isn't enough to merely be better in some personally favoured dimension of merit. Your immature new thing has to be better enough to actually pass the mature old thing on its own terms.
Got a better substrate than silicon? Yeah? What's your defect density cranking out 10,000 wafers per month? Oh, you haven't actually developed all that quality-control infrastructure yet, but you figure you can do it at half the price once you work out the final kink from your strained bullerene crystal lattice?
Awesome progress, pal, but I think I'll invest my own Bitcoin elsewhere.
For the record, I've long believed that the trade-off moving from depth to sophistication wouldn't prove particularly steep (for the right sophistication). But any gradient that's a net loss (no matter how small) provides pretty much no immediate competitive incentive for anyone to invest any real effort hoeing that row.
The great thing about neural networks is that they don't actually require much real effort. The machine itself does most of the work in 72 hours. And then what have you got? A RISC chip that never actually kills x86 (because those idiots were busy touting microcosmic instruction set efficiency long after the real game had shifted to streamlining the cache hierarchy, where's there's no low-hanging ideological shortcut to help you overcome the first-mover fat-payroll advantage).
I have seen something else under the sun: The race is not to the swift or the battle to the strong, nor does food come to the wise or wealth to the brilliant or favor to the learned; but sunk cost and legacy happen to them all.
The point is not to win. Chess supercomputers already do that. The point is to write a program that can play well using limited resources, and maybe learn something about how humans do it.
You said yourself: "It's damn amazing how well grand-master level pruning actually works."
One could pre-calculate all possible positions
Shannon calculated the number of chess positions as 10 to the power 50.
At that rate, it would take longer than the expected life of our galaxy to compute one move, even if every molecule of the earth were turned into a supercomputer.
Well, yes, that kind of is the issue. The computation chess masters make, the actual thoughts, could be handled on a 1950 computer no problem.
The question is how. It isn't brute force, though they do delve into plies as desired. The real trick is knowing which handful to explore mentally. And if it were just pattern matching against known games, it would be done by computer already that way, too.
What?
FTFY... (although perhaps a few players I know might be thinking about it the original way it was written)
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.
There are a few blocks with "input" and "hidden layer 1"/ hidden layer 2. What does that mean? Absolutely nothing
At some point you have to stop explaining subject specific phrases in an article, "hidden layer" means something something to people who have a basic understanding of the subject, google it if you don't.
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