7 ms·

One impressive statistic from the paper: AlphaZero analyzes 80,000 chess positions per second, while Stockfish looks at 70,000,000. Seventy million, three order

One impressive statistic from the paper: AlphaZero analyzes 80,000 chess positions per second, while Stockfish looks at 70,000,000. Seventy million, three orders of magnitude higher. Yet AG0 beats Stockfish half the time as White and never loses with either color. A stunning demonstration of generality indeed.

1024core 9y ago
So ... what if you combined Stockfish and AG0, and let AG0 explore 70M positions instead of 80K? Would it improve even faster?
- thom 9y ago
  The issue is you can’t evaluate positions that fast in AlphaZero (currently).
  - thom 9y ago
    It would be interesting to see if there were some way to extract a couple of new heuristics from AlphaZero that could be implemented fast enough to incorporate in Stockfish's evaluator though. I suppose this is the age old problem of black-box models: _why_ does it think this?
    - bhouston 9y ago
      I think that it is almost always possible to extract optimized models from nn and implement them faster. I wonder if this can be generalized. Nn to optimized fix algo for Max speed? This has to already exist as it is very obvious.
      - thom 9y ago
        I dunno, seems like Google would just do this instead of keep around the pesky neural net at runtime. There's an _awful_ lot of computation going on inside, and it's necessarily hugely interconnected. I'd be impressed if someone had already done it, but it seems a great avenue of research if not. I suppose it goes hand in hand with models for which you can actually _explain_ their results, which certainly is an active area of research.
        robrenaud 9y ago
        There are well-known techniques that work pretty well to shrink neural nets a lot while keeping almost all of their performance. See Geoffrey Hinton's model distillation papers. The first AlphaGo paper had a system that used tons of computation, and was followed up by one that used much less and worked even better. Not speaking for Google, but I think it's a bit of a race to publish great results first. I wouldn't be surprised to see something better than this that uses 1000 times less resources published in a year or two, just like what happened with Go. First prove it's possible, than figure out how to make it much more efficient.
        thom 9y ago
        Thanks for these googlable hints. :)
        gwern 9y ago
        A really good example of model distillation also comes from DM: their new realtime WaveNet used in Google Assistant. The first WaveNet was ungodly slow due to redundant computation; but even after that, it still was not realtime simply because the CNN is too deep and slow. But you need the CNN to be deep & big in order to train good audio generation. Model distillation to the rescue: take a wide fast small CNN and train it to imitate the slow deep WaveNet. Result: WaveNet quality realtime voice generation which can be deployed to the masses.
- EvgeniyZh 9y ago
  What if you combined a bus that gets you to work in 10 minutes and plane that gets you from Paris to Brazil, would it get you from Paris to Brazil in 10 minutes?
  - eternalcode 9y ago
    yes. In an imaginary and hypothetical sense. :)

1024core 9y ago

So ... what if you combined Stockfish and AG0, and let AG0 explore 70M positions instead of 80K? Would it improve even faster?

thom 9y ago
The issue is you can’t evaluate positions that fast in AlphaZero (currently).
- thom 9y ago
  It would be interesting to see if there were some way to extract a couple of new heuristics from AlphaZero that could be implemented fast enough to incorporate in Stockfish's evaluator though. I suppose this is the age old problem of black-box models: _why_ does it think this?
  - bhouston 9y ago
    I think that it is almost always possible to extract optimized models from nn and implement them faster. I wonder if this can be generalized. Nn to optimized fix algo for Max speed? This has to already exist as it is very obvious.
    - thom 9y ago
      I dunno, seems like Google would just do this instead of keep around the pesky neural net at runtime. There's an _awful_ lot of computation going on inside, and it's necessarily hugely interconnected. I'd be impressed if someone had already done it, but it seems a great avenue of research if not. I suppose it goes hand in hand with models for which you can actually _explain_ their results, which certainly is an active area of research.
      - robrenaud 9y ago
        There are well-known techniques that work pretty well to shrink neural nets a lot while keeping almost all of their performance. See Geoffrey Hinton's model distillation papers. The first AlphaGo paper had a system that used tons of computation, and was followed up by one that used much less and worked even better. Not speaking for Google, but I think it's a bit of a race to publish great results first. I wouldn't be surprised to see something better than this that uses 1000 times less resources published in a year or two, just like what happened with Go. First prove it's possible, than figure out how to make it much more efficient.
        thom 9y ago
        Thanks for these googlable hints. :)
        gwern 9y ago
        A really good example of model distillation also comes from DM: their new realtime WaveNet used in Google Assistant. The first WaveNet was ungodly slow due to redundant computation; but even after that, it still was not realtime simply because the CNN is too deep and slow. But you need the CNN to be deep & big in order to train good audio generation. Model distillation to the rescue: take a wide fast small CNN and train it to imitate the slow deep WaveNet. Result: WaveNet quality realtime voice generation which can be deployed to the masses.
EvgeniyZh 9y ago
What if you combined a bus that gets you to work in 10 minutes and plane that gets you from Paris to Brazil, would it get you from Paris to Brazil in 10 minutes?
- eternalcode 9y ago
  yes. In an imaginary and hypothetical sense. :)