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This might be a dumb question, and will inevitably showcase my ignorance in this field to others, but I will risk that; Why can't AI at a certain level execute
by crowie 1y ago
This might be a dumb question, and will inevitably showcase my ignorance in this field to others, but I will risk that;
Why can't AI at a certain level execute algorithms with solutions that have been proved to work for a very long time?
What I mean is, the solution of the Hanoi towers problem is known. It does not take a lot of computational power to achieve the result. What is stopping an AI such as the objects of exam in the paper to execute such algorithms and gather the solutions, like a human programmer would? Do they get sidetracked in the process due to the amount of tokens?
(edit: typo)
- pyman 1y agoIf humanity moves to Mars one day and leaves behind all the AI servers running on solar power, then comes back a billion years later, the AI would still be saying the same things. Why? Because no matter how powerful it is, AI doesn't evolve or grow on its own.
- crowie 1y agoGotcha, but I didn't mean it in that way. What I meant is, that problems like the case-study ones don't need a revolutionary nor an original answer which would require growth, they can be solved with old solutions which I would assume would be in some way embedded into the learning dataset of these models. Yeah, the scope of the problem is bigger, but the correct answer should come down in any case to a correct implementation of the known algorithm. The thing I'm asking is what causes the hindrance which prevents these AIs from performing in appropriate ways given old problems and old solutions.
- ryandvm 1y agoI like your thought experiment and I think you're correct, but that's because we never gave it the physical possibility of a feedback loop (a.k.a. evolution). I think if you added a step where the LLMs tweak their own build process and redeploy, your experiment would have wildly different results.