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jstrong

> e.g. equity trading

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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

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.

[0] https://lczero.org/

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.

There are newer approaches where specialising for the problem domain (Go) gets you a 5-100x speedup: https://github.com/lightvector/KataGo/

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.

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

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.

Sound is not discrete either, it does not seem to be a problem on computers, though.

That's the feature space, not the decision space

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.

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