4 ms·

The issue is you can’t evaluate positions that fast in AlphaZero (currently).

by thom 9y ago

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.

The issue is you can’t evaluate positions that fast in AlphaZero (currently).

by 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.