[sally_glance]

Can we be sure? Maybe it's just very rare for experience, education and memories to line up in exactly the way that allows synthesizing something innovative. So it requires a few billion candidates and maybe a couple of generations too.

[chongli]

I want to point back to my remark about everyday people.

if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more

This isn't a throwaway comment. I do this all the time myself, at work. Everywhere I've worked, I do this. I challenge the assumptions and try to make things better. It's not a rare thing at all, it's just not revolutionary.

Revolutions are rare. Perhaps only a handful of them have ever happened in any one particular field. But you simply will not ever go from Aristotelian physics to Newtonian physics to General Relativity by merely "synthesizing the data they were trained on", as the previous comment supposed.

Edit: I should also say something about experimentation. You can't do it from an armchair, which is all an LLM has access to (at present). Real people learn things all the time by conducting experiments in the world and observing the results, without necessarily working as formal scientists. Babies learn a lot by experimenting, for example. This is one particular avenue of new knowledge which is entirely separate from experience, education, memories, etc. because an experiment always has the potential to contradict all of that.

[sally_glance]

Experimentation leads to experience, so I feel like this was included by the parent comment. And in the case of writing software, agents are able to experiment today. They run tests, check log output, search DBs... Sure, they can't have apples fall on their heads like Newton had but they can totally observe the apple falling on someones head in a video.

[chongli]

Experimentation leads to experience

Of course it does, but only after the fact. You don't have any experience of the result of the experiment before you perform it.

Sure, they can't have apples fall on their heads like Newton had but they can totally observe the apple falling on someones head in a video

I have strong doubts that LLMs have any understanding whatsoever of what's happening in images (let alone videos). The claim (I've sometimes heard) that they possess a world model and are able to interpret an image according to that model is an extremely strong one, that's strongly contradicted by the fact that they: a) continue to hallucinate in pretty glaring ways, and b) continue to mis-identify doctored (adversarial) images that no human would mis-identify (because they don't drastically alter the subject).

[sally_glance]

In software, they can and do perform experiments (make a change then observe the log output). I don't think they possess a "world model" or that it's worth spending too much thought on... My reasoning is more along the lines that our brains are also just [very advanced] inference machines. We also hallucinate and mis-identify images (there are image/video classification tasks where humans have lower scores).

For me the most glaring difference to how humans work is the lack of online learning. If that prevents them from being able to innovate, I'm not so sure.

[chongli]

Software is not the world. It’s a tiny bit of what humans do.

The lack of online learning is a critical fault. Much of what humans learn (such as anything based on mathematics) has a dependency tree of stuff to learn. But even mundane stuff involves a lot of dependent learning. For example, ask an LLM to write a cookbook and it can synthesize from recipes that are already out there but good luck having it invent new cooking techniques that require experimentation or invention (new heat source, new cooking utensils, etc).

[sally_glance]

I guess we'll just have to wait and see how things turn out. Currently it seems we have examples of where it seems like the technology allows some amount of innovation (AlphaGo, software, math proofs) and examples where they seem surprisingly stupid (recipes?).

Btw, it looks like there is a growing body of research evaluating exactly this. I found this nice overview with even some benchmarks specifically for scientific innovation: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Dis...