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.
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.
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.
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 ?
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.
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.
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.
