> what basis do you have for assuming an LLM is fundamentally incapable of doing this?
because I have no basis for assuming an LLM is fundamentally capable of doing this.
> what basis do you have for assuming an LLM is fundamentally incapable of doing this?
because I have no basis for assuming an LLM is fundamentally capable of doing this.
Good on you for spelling out this reasoning, but it is manifestly unsound. For a wide variety of values of X, people a few years ago had no reason to expect that LLMs would be capable of X. Yet here we are.
In 1989, Gary Kasparov said that it was "ridiculous!" to suggest a computer would ever beat him at chess.
"Never shall I be beaten by a machine!”
In 1997 he lost to Deep Blue.
Yeah, and back then people moved the goal posts too, saying Deep Blue was just "brute-forcing" chess (which isn't even true since it's not a pure minimax search).
Deep Blue was brute forcing chess in the sense that AlphaGo wasn't brute forcing Go.
And today he's got salient observations on politics which hold much of his attention, and Deep Blue is shut off and has done nothing further.
Not a good argument for turning everything over to the Deep Blues. What's Deep Blue done for me lately?
This is something that could be demonstrated rather than just argued.
Train an LLM only on texts dated prior to Newton and see if it can create calculus, derrive the equations of motion, etc.
If you ask it about the nature of light and it directs you to do experiments with a prism I'd say we're really getting somewhere.
We tried this experiment with humans, back in the 17th century, and only a few[1] out of millions managed it given a whole human lifetime each.
[1] Obviously Newton counts as one. Leibniz like Newton figured out calculus. Other people did important work in dynamics though no one else's was as impressive as Newton's. But the vast majority of human-level intelligences trained on texts prior to Newton did not create calculus or derive the equations of motion or come close to doing either of those things.
Except this has been said since the 2010's and has been proven wrong again and again. Clearly the theory that LLM's can't "extrapolate" is woefully incomplete at best (and most likely simply incorrect). Before the rise of ChatGPT, the onus was on the labs to show it was plausible. At this point, I think the more epistemologically honest position is to put the burden back on the naysayers. At the least, they need to admit they were wrong and give a satisfactory explanation why their conceptual model was unable to account for the tremendous success of LLM's and why their model is still correct going forward. Realistically, progress on the "anti-LLM" side requires a more nuanced conceptual model to be developed carefully outlining and demonstrating the fundamental deficiencies of LLMs (not just deficiencies in current LLMs, but a theory of why further advancements can't solve the deficiencies).
Incidentally, similar conversations were had about ML writ large vs. classical statistics/methods, and now they've more or less completely died down since it's clear who won (I'm not saying classical methods are useless, but rather that it's obvious the naysayers were wrong). I anticipate the same trajectory here. The main difference is that because of the nature of the domain, everyone has an opinion on LLM's while the ML vs. statistics battle was mostly confined within technical/academic spaces.