7 ms·
See the thing is though, Giraffe's evaluation actually was better than Stockfish's evaluation function, but it took much longer, and thus wasn't able to search
by deepnotderp 9y ago
See the thing is though, Giraffe's evaluation actually was better than Stockfish's evaluation function, but it took much longer, and thus wasn't able to search as deep as Stockfish et al. So in a way, the real triumph of the AlphaGo series was the TPU and GPU army.
- gwern 9y ago> So in a way, the real triumph of the AlphaGo series was the TPU and GPU army. Eh. It's still searching many fewer positions than Stockfish is.
- deepnotderp 9y agoRight right, but my comparison was between giraffe and AlphaGo , not neural networks and Stockfish.
- joshuamorton 9y agoBut it looks like AlphaGo is searching fewer positions per second than Giraffe did. AlphaZero evaluates 80K positions per second, according to this paper, and the Giraffe paper says that Giraffe averaged 258570 evaluations per second when running STS. While we can't directly compare the computer power, this implies that AZ has learned a better representation.
- allenz 9y agoIt's unclear how much of the "better representation" was due to better algorithm vs. more compute/deeper NN.
- joshuamorton 9y agoGiraffe was trained until convergence. Maybe if there was more compute power then, a different model would have been used, but that's deep into the world of silly hypotheticals.
- Retric 9y agoBy starving the competitor of computing power, if you compare A and B you can't give A 10+x the compute power and assume a fair comparison. What's interesting is a demonstration that enough compute power let's NN reach beyond human level play. Though, I don't think that was ever really in doubt.
- allenz 9y agoIt searches fewer positions because it decides where to search using 4 TPUs, which are 180 teraflops each according to Google.
- nl 9y agoThe number of positions evaluated is the number evaluated. Speed doesn’t change that. Speed probably made the initial self play training quicker though.
- allenz 9y agoCompute absolutely matters. With tree search, there's a tradeoff between scoring cost and positions evaluated. AlphaZero can evaluate fewer positions because it uses a huge amount of compute to accurately score each position. It's not just training. Training used 5,000 TPUs.
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- arcticfox 9y agoCan't those teraflops be applied to evaluating more positions instead of deciding which positions to evaluate? It seems that the metric should be compute time, not positions evaluated.
- nl 9y agoPresumably they both had equal clock time - that is a standard chess rule so it would be surprising to see it different.
- setr 9y agoWall clock != cpu clock I can do more in the same wall time with a faster cpu(s); I can afford inefficiencies that the opponent cannot, and accomplish just as much.
- skybrian 9y agoUnlike in most algorithms where correctness and performance are independent, chess engines can't be evaluated without testing performance at the same time; faster is not just faster, it changes the results. So there is a tradeoff between the depth of the search and quality of evaluation. For traditional chess algorithms, better evaluation was rarely worth the cost; it would slow down the search so much that it didn't pay for itself. But this performance tradeoff (like all optimizations) critically depends on hardware. Change the hardware and you change which optimizations are "worth it". AlphaZero is clearly good at using TPU's to maximum effect. But what would its performance be in a CPU only environment? Maybe dumb but deeper searches still win there? This evaluation hasn't been done. This isn't to say that the AlphaZero evaluation is "unfair". Rather that chess engines evolved to be too dependent on their environment. Getting maximum use out of CPU's is a strength, but not being able to use TPU's or even GPU's is a weakness.
- nojvek 9y agoAgree with this. Stockfish is fast enough to run on modern iphone and Android phones. AlphaZero most probably not. But the fact that a generic algorithm absolutely destroys humans and their human crafted programs is the most interesting. Yes TPU + GPU army is a huge amount of computation power but I'm sure they'll be research coming out trying to compress the algorithms enough to use the same computation power as stock fish.
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