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It is.
LLMs and similar systems aren't human, not in the slightest.
They're nowhere near that. People are happy they can count letters correctly.
As stated, be really nice if there was a sound case for why this won't change in the near future.
The jump to where we are was sudden and surprising, the next one could be as well.
If a cat surprises you with a sudden jump to a wardrobe top just under a ceiling, how likely is it to then surprise you with jumping through the ceiling ?
There's no superhuman data, this isn't as easy. What happened until know was just adding more scale and it led to increasing returns until it got to the point there was no new data to add. And this is still a system that's basically a glorified retrieval tool because it has trouble with basic logic unless it uses 'chain of thought' which is pretty expensive and not much better.
So do I, but they gave me the PhD anyway. Applied math, so there were a lot of computers rather than just the traditional coffee maker and a chalkboard, but you'd be surprised at how heavily even the more-respectable pure mathematicians rely on that chalkboard (or whiteboard, these days) to keep track of a chain of thought.
And on that subject, math is one field where you can add new perfect-quality synthetic data with no obvious bound. Generating a proof may be NP-complete, but verifying a rigorous proof can be done by non-AI computer programs. Both OpenAI and Meta have started using Lean to train models to generate new proofs. It's not quite as good a target as Go self-play, since "play against yourself" is a naturally good difficulty-level for self-improvement, whereas some theorems are deceptively complex to state and easy to prove or vice-versa, but it's not a slow-growing data set in the same way as "mimic our recorded human language" is, and yet it's natural to translate back and forth between the formal proof language and the basic-logic-plus-much-more subset of human languages.
I'd still say it's possible that there's some other "spark" of out-of-sample creativity that humans have and models trained under current techniques just can't acquire, but if that's what you're shooting for, at least lead your target. Even if current progress does stall out, we may end up going from "has trouble with basic logic" to "(dis?)proved the Riemann hypothesis" before things plateau again.
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The entire course of technological progress was us surprising ourselves by jumping through what we thought for millenia was the ceiling. Either we've been achieving superhuman data every time, or there is much more human data than we think.
It's ridiculous to conflate human cognition and culture with LLMs. Quality of thought depends on data to a much more limited degree, people can also discern truth from nonsense with some reflection.
LLMs can't.
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