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Comment by gonehome | Hacker News Reader

DL isn’t just good at automating human tasks (see AlphaZero and AlphaGo).

It can be used to recognize patterns and train to solve problems better than humans. Though in this case since the market is people making predictions about a prediction I’m not sure how much it’d help.

There’s probably something about humans using a rough estimate of what the stock has been historically to inform where it could go though so it’s value is probably non-zero.

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By DL, i meant generic DL techniques like CNN, LSTM (used in this article), which relies on large amount of labeled data to train, and predict on similar data, thus my comment about automation. Alpha Go (Zero) is very specialized for the game of go, not sure how much of its specialized algorithm could transfer to other generic use cases.

>Alpha Go (Zero) is very specialized for the game of go, not sure how much of its specialized algorithm could transfer to other generic use cases.

AlphaZero is a generalized successor, and it does just that:

https://en.m.wikipedia.org/wiki/AlphaZero

Good to know it generalized, but seems it's only generalized on board games like problems where you have a problem space to search through, is that understanding correct? If so, it probably won't help in the use cases we are talking about here, right?

Yeah it's not an AGI, but I think the response was more that DL isn't necessarily limited to automating tasks that are easy for humans but hard for machines without DL (which I think was your original statement).

DL could potentially do a better job by recognizing patterns in the dataset that lead towards winning (making more money) that humans might miss. Like how AlphaZero can recognize moves in Go or Chess that humans don't understand are the best moves to make.

It's not obvious to me how this would be done, but I think it's plausible that some clever implementation could help.

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