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Comment by QML | Hacker News Reader

Modern AI may "beat" humans at certain tasks, but I doubt they are as efficient as humans are at those tasks: it makes sense that throwing more compute at problem will yield better results. But is it possible to get better results while constraining training time?

Edit: I looked up how much time it takes to train: "OpenAI Five plays 180 years worth of games against itself every day, learning via self-play." [1]

[1] https://blog.openai.com/openai-five/

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If you can get hundred years experience in 24 hours by wall clock, why not use it? Maybe it's better to teach AIs to create simplified, but usable models, they can use to simulate and train on.

I view the problem in terms of "computational complexity": if the AI of today requires O(2^n) time to perform at a human level, it is possible to get that down to O(n^2)?

I believe there's a connotation with problems, that if the best algorithm to solve it is exponential (brute-force search), then we truly don't understand the (underlying structure of the) problem.

I don't think it is a fair comparison. We have the ability to build somewhat working strategy from known parts, and then we still need thousands of hours (or man-years in the case of Go) to improve it and to teach the brain to do some parts without conscious attention. Alpha Zero, OpenAI Five and others build strategy (mostly) from scratch.

I have a feeling that it is not possible to reduce algorithmic complexity of finding optimal solutions in most of the intellectual tasks (those that are in NP complexity class and above).

Most likely it is a trade-off. Quickly cobble up suboptimal strategy / build better strategy from scratch avoiding all time-saving benefits of using known parts, and avoiding all the pitfalls of not reevaluating utility of those parts in the current situation.

AIs surely will need to use all the spectrum to compete with humans.

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