alt Hacker NewsThat centaurs can outperform humans or AI systems alone is a weaker claim than "these particular AI systems have the required properties to be useful for that". Chess engines consistently produce strong lines, and can play entire games without human assistance: using one does not feel like gambling, even if occasionally you can spot a line it can't. LLMs catastrophically fail at iterated tasks unless they're closely supervised, and using LLMs does feel like gambling. I think you're overgeneralising.
There is definitely a gap in academic tooling, where an "association engine" would be very useful for a variety of fields (and for encouraging cross-pollination of ideas between fields), but I don't think LLMs are anywhere near the frontier of what can be accomplished with a given amount of computing power. I would expect simpler algorithms operating over more explicit ontologies to be much more useful. (The main issue is that people haven't made those yet, whereas people have made LLMs.) That said, there's still a lot of credit due to the unreasonable effectiveness of literature searches: it only usually takes me 10 minutes a day for a couple of days to find the appropriate jargon, at which point I gain access to more papers than I know what to do with. LLM sessions that substitute for literature review tend to take more than 20 minutes: the main advantage is that people actually engage with (addictive, gambling-like) LLMs in a way that they don't with (boring, database-like) literature searches.
I think developing the habit of "I'm at a loose end, so I'll idly type queries into my literature search engine" would produce much better outcomes than developing the habit of "I'm at a loose end, so I'll idly type queries into ChatGPT", and that's despite the state-of-the-art of literature search engines being extremely naïve, compared to what we can accomplish with modern technology.
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We have made those in the 80s. Much was learned about why probabilistic stochastic parrots are a far better model.
We're in agreement. I understand how much harder it is to "think with AI"; the last year of my life has been a brutal struggle to figure this out.
I also agree that neural net LLMs are not the inevitable way to implement AI. I'm most intrigued by the theoretical underpinnings of mathematical proof assistants such as Lean 4. Computer scientists understand the word problem for strings as undecidable. The word problem for typed trees with an intrinsic notion of induction is harder, but constructing proofs is finding paths in this tree space. Just as mechanical computers failed in base ten while at the same time Boole had already developed base two logic, I see these efforts merging. Neural nets struggle to simulate recursion; for proof assistants recursion is baked in. Stare at these tree paths and one sees thought at the atomic level, begging to be incorporated into AI. For now the river runs the other way, using AI to find proofs. That river will reverse flow.