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this post was submitted on 04 Sep 2024
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LLMs as they stand are already approaching the improvement flatline portion of the sigma curve due to marginal data requirements increasing exponentially.
It's a known problem in the actual AI research field that nobody in private industry likes to talk about.
If it scores 40% this year it'll marginally increase by 10% next year then 5% 3 years later and so on.
AI doesn't follow Moore's law.
So far "more data" has been the solution to most problems, but I don't think we're close to the limit of how much useful information can be learned from the data even if we're close to the limit of how much data is available. Look at the AIs that can't draw hands. There are already many pictures of hands from every angle in their training data. Maybe just having ten times as many pictures of hands would solve the problem, but I'm confident that if that was not possible then doing more with the existing pictures would also work.* Algorithm design just needs some time to catch up.
*I know that the data that is running out is text data. This is just an analogy.