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> I think "novel" is ill defined here That's exactly my point. When people say "LLMs will never do something novel," they seem to be leaning on some vague, ill
by qnleigh 7mo ago
> I think "novel" is ill defined here
That's exactly my point. When people say "LLMs will never do something novel," they seem to be leaning on some vague, ill-defined notion of novelty. The burden of proof is then to specify what degree of novelty is unattainable and why.
As for evidence that they can do novel things, there is plenty:
1. I really did ask Gemini to multiply 167,383 * 426,397 before posting this question. It answered correctly.
2. SVGs of pelicans riding bicycles
3. People use LLMs to write new apps/code every day
4. LLMs have achieved gold-medal performance on Math Olympiad problems that were not publicly available
5. LLMs have solved open problems in physics and mathematics [0,1]
That is as far as they have advanced so far. What's next? Where is the limit? All I want to say is that I don't know, and neither do you :).
[0] https://www.reddit.com/r/Physics/comments/1n77h10/and_several_of_the_main_proof_ideas_were/ https://www.reddit.com/r/Physics/comments/1n77h10/and_severa... (Mark Raamsdonk is a pretty famous researcher in high-energy physics, not just some random guy)
[1] https://mathstodon.xyz/@tao/115855840223258103 https://mathstodon.xyz/@tao/115855840223258103
[2] https://news.ycombinator.com/item?id=47497757 https://news.ycombinator.com/item?id=47497757
- qnleigh 7mo agoActually here's an even better list of progress on a number of open math problems, with plenty of caveats and exposition: https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems https://github.com/teorth/erdosproblems/wiki/AI-contribution...
- staticassertion 7mo agoThis is great observational data but it's an early "step 1", I'd definitely need to see an actual analysis of these cases and likely want to have that analysis involve a review of relevant training data.
- fenomas 7mo agoWhat you're asking for is exactly what's in the link you replied about. It collects analysis of each solution (or attempt), and info about whether the AI's solution could be found anywhere in the literature.
- staticassertion 7mo agoWhere?
- fenomas 7mo agoThe high-order bit for for each case is the category it's in and the "Outcome" column - that summarizes if the solution was full/partial/wrong, if AI had assistance, etc. Then further discussion for each one is linked from the number. Then the "Literature result" columns have a citations for where similar published results were found. The ones with no "Literature" column, like in the first section, are cases where no similar published results have been found (implying that the solution would not have been trained on). Note that in some cases a published solution was found but it wasn't similar to the AI's. (this is all explained with more detail and caveats at the top of the page)
- staticassertion 7mo agoSorry, I suppose I'm asking for a lot of handholding here, which isn't really fair. I'm actually just sick right now and have crazy brain fog. Thanks for the assistance! I'll read through. FWIW I've wavered on this topic quite a bit. Not too long ago I leaned more heavily towards "complex cognitive capabilities can be expressed using statistical token generation", I've started leaning the other way, but I'm not committed so it's great to circle back on the state of things.
- fenomas 7mo agoNot at all - didn't mean to sound snarky, I just wanted to add that I was omitting details and caveats. FWIW, personally I think it muddies things to frame the question as if "..using statistical token generation" was a limitation. NNs are Turing-complete, so what LLMs do can just be considered "computation" - the fact that they compute via statistical token generation is an implementation detail. And if you're like most people, "can cognition happen via computation?" is a less controversial question, which then puts LLMs/cognition topics easily into the "in principle, obviously, but we can debate whether it's achievable or how to measure it" category.