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Notes -
I follow JimDMiller ("James Miller" on Scott's blogs, occasionally /u/sargon66 back when we were on Reddit) on Twitter, and was amused to see how much pushback he got on the claim:
On the one hand, it's not inconceivable that LLMs can get very good at producing text that "interpolates" within and "remixes" their data set without yet getting good at predicting text that "extrapolates" from it. Chain-of-thought is a good attempt to get around that problem, but so far that doesn't seem to be as superhuman at "everything" as simple Monte Carlo tree search was at "Go" and "Chess". Humans aren't exactly great at this either (the tradition when someone comes up with previously-unheard-of knowledge is to award them a patent and/or a PhD) but humans at least have got a track record of accomplishing it occasionally.
On the other hand, even humans don't have a great track record. A lot of science dissertations are basically "remixes" of existing investigative techniques applied to new experimental data. My dissertation's biggest contributions were of the form "prove a theorem analogous to existing technique X but for somewhat-different problem Y". It's not obvious to me how much technically-new knowledge really requires completely-conceptually-new "extrapolation" of ideas.
On the gripping hand, I'm steelmanning so hard in my first paragraph that it no longer really resembles the real clearly-stated AI-dismissive arguments. If we actually get to the point where the output of an LLM can predict or surpass any top human, I'm going to need to see some much clearer proofs that the Church-Turing thesis only constrains semiconductors, not fatty grey meat. Well, I'd like to see such proofs, anyway. If we get to that point then any proof attempts are likely either going to be comically silly (if we have Friendly AGI, it'll be shooting them down left and right) or tragically silly (if we have UnFriendly AGI, hopefully we won't keep debating whether submarines can really swim while they're launching torpedos).
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