4 ms·

> e.g. equity trading

narrator 7y ago
As much as I liked to get excited bout these kinds of papers: "Recomputing the AlphaGo Zero weights will take about 1700 years on commodity hardware." [1] https://github.com/leela-zero/leela-zero https://github.com/leela-zero/leela-zero
- fnbr 7y ago
  Yeah, but that's an embarassingly parallel task, so you can recreate it with sufficient resources. If there's a sufficiently lucrative application, businesses will happily pay that.
  - dmurray 7y ago
    And it's already been done for the chess version, in a community effort[0]. Well, "recomputing the weights" isn't plausible for a neural network of this size, but computing weights that give the network a similar level of performance. [0] https://lczero.org/ https://lczero.org/
    - narrator 7y ago
      One thing I think that will be critical to the future of software is community efforts to build deep neural nets that match the AI power of the big companies.
- pixelpoet 7y ago
  There are newer approaches where specialising for the problem domain (Go) gets you a 5-100x speedup: https://github.com/lightvector/KataGo/ https://github.com/lightvector/KataGo/
make3 7y ago
Indeed that's a weird thing to say, especially since AFAIK AlphaGo is for (very large) discrete search spaces (where Monte Carlo Tree Search would be used), & equity trading doesn't strike me as a discrete search space.. is it ?
- dmurray 7y ago
  Yes? At any point in time you can buy or sell a discrete number of shares in a finite universe of stocks. The bigger difference in equity trading is that it's a "game" of hidden information with asymmetric and maybe unknown payoffs. Of course, computers are pretty good at trading stocks, too, but I can say confidently AlphaZero won't be the most useful approach.
  - make3 7y ago
    A part missing in your analysis is that time is involved in the decisions, and that time is continuous, and that time is very important
    - falcor84 7y ago
      Is time really continuous in this case? I have absolutely no idea how it's implemented in practice, but would assume that stock exchange computers use some sort of game loop at a consistent rate.
- degski 7y ago
  Sound is not discrete either, it does not seem to be a problem on computers, though.
  - make3 7y ago
    That's the feature space, not the decision space
amitport 7y ago
Well at least the second author, Tucker Balch, is a computational investing consultant / lecturer. I'm sure they'll continue to work this angle, even if it's not yet there.

As much as I liked to get excited bout these kinds of papers: "Recomputing the AlphaGo Zero weights will take about 1700 years on commodity hardware." [1] https://github.com/leela-zero/leela-zero https://github.com/leela-zero/leela-zero

fnbr 7y ago
Yeah, but that's an embarassingly parallel task, so you can recreate it with sufficient resources. If there's a sufficiently lucrative application, businesses will happily pay that.
- dmurray 7y ago
  And it's already been done for the chess version, in a community effort[0]. Well, "recomputing the weights" isn't plausible for a neural network of this size, but computing weights that give the network a similar level of performance. [0] https://lczero.org/ https://lczero.org/
  - narrator 7y ago
    One thing I think that will be critical to the future of software is community efforts to build deep neural nets that match the AI power of the big companies.
pixelpoet 7y ago
There are newer approaches where specialising for the problem domain (Go) gets you a 5-100x speedup: https://github.com/lightvector/KataGo/ https://github.com/lightvector/KataGo/

make3 7y ago

Indeed that's a weird thing to say, especially since AFAIK AlphaGo is for (very large) discrete search spaces (where Monte Carlo Tree Search would be used), & equity trading doesn't strike me as a discrete search space.. is it ?

dmurray 7y ago
Yes? At any point in time you can buy or sell a discrete number of shares in a finite universe of stocks. The bigger difference in equity trading is that it's a "game" of hidden information with asymmetric and maybe unknown payoffs. Of course, computers are pretty good at trading stocks, too, but I can say confidently AlphaZero won't be the most useful approach.
- make3 7y ago
  A part missing in your analysis is that time is involved in the decisions, and that time is continuous, and that time is very important
  - falcor84 7y ago
    Is time really continuous in this case? I have absolutely no idea how it's implemented in practice, but would assume that stock exchange computers use some sort of game loop at a consistent rate.
degski 7y ago
Sound is not discrete either, it does not seem to be a problem on computers, though.
- make3 7y ago
  That's the feature space, not the decision space

amitport 7y ago

Well at least the second author, Tucker Balch, is a computational investing consultant / lecturer. I'm sure they'll continue to work this angle, even if it's not yet there.