Yann LeCun spoke explicitly on this idea recently and he asserts definitively that the LLM would not be able to add anything useful in that scenario. My understanding is that other AI researchers generally agree with him, and that it's mostly the hype beasts like Altman that think there is some "magic" in the weights that is actually intelligent. Their payday depends on it, so it is understandable. My opinion is that LeCun is probably correct.

There is some ability for it to make novel connections but it's pretty small. You can see this yourself having it build novel systems.

It largely cannot imaginr anything beyond the usual but there is a small part that it can. This is similar to in context learning, it's weak but it is there.

It would be incredible if meta learning/continual learning found a way to train exactly for novel learning path. But that's literally AGI so maybe 20yrs from now? Or never..

You can see this on CL benchmarks. There is SOME signal but it's crazy low. When I was traing CL models i found that signal was in the single % points. Some could easily argue it was zero but I really do believe there is a very small amount in there.

This is also why any novel work or findings is done via MASSIVE compute budgets. They find RL enviroments that can extract that small amount out. Is it random chance? Maybe, hard to say.

How about this for an evaluation: Have this (trained-on-older-corpus) LLM propose experiments. We "play the role of nature" and inform it of the results of the experiments. It can then try to deduce the natural laws.

If we did this (to a good enough level of detail), would it be able to derive relativity? How large of an AI model would it have to be to successfully derive relativity (if it only had access to everything published up to 1904)?

Preface: Most of my understand of how LLMs actually work comes from 3blue1brown's videos, so I could easily be wrong here.

I mostly agree with you, especially about distrusting the self-interested hype beasts.

While I don't think the models are actually "intelligent", I also wonder if there are insights to be gained by looking at how concepts get encoded by the models. It's not really that the models will add something "new", but more that there might be connections between things that we haven't noticed, especially because academic disciplines are so insular these days.

What do they (or you) have to say about the Lee Sedol AlphaGo move 78. It seems like that was "new knowledge." Are games just iterable and the real world idea space not? I am playing with these ideas a little.

Do you have a pointer to where LeCun spoke about it? I noticed last October that Dwarkesh mentioned the idea off handedly on his podcast (prompting me to write up https://manifold.markets/MikeLinksvayer/llm-trained-on-data-...) but I wonder if this idea has been around for much longer, or is just so obvious that lots of people are independently coming up with it (parent to this comment being yet another)?

This is definitely wrong, most AI researchers DO NOT agree with LeCun.

Most ML researchers think AGI is imminent.