The ability to do a ton of book learning in training, and pull in tons of related context at once, is superhuman in some ways, but lags a lot in others.
Then it’s an expert system.
Stephen Hawking wasn’t very good at folding clothes.
The ‘General’ part of the term ‘AGI’ seems like a trap to me, because there will always be new workflows to master. Can Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
Meanwhile, building a series of expert systems targeting specific valuable workflows is useful today and seems like it’ll continue to scale to cover huge swathes of economically valuable workflows.
I think that’s the more interesting thing to be measuring. The surface area of useful economic workflows that can be addressed with expert systems built with today’s tech.
Hitting some ‘Artificial Expert Intelligence’ coverage threshold on economically valuable workflows is what will matter for humans well before pure ‘general’ intelligence.
AI in math is ongoing. https://spectrum.ieee.org/ai-in-mathematics
https://mymodernmet.com/gianluca-gimini-velocipedia-bicycles...
https://qz.com/681345/an-artists-3d-renderings-of-bicycles-d...
Checkmate, meatbags.
And the only reason LLMs can't write essays indistinguishable from human output is because they aren't RLHF'ed to write like humans.
Folding clothes isn't an LLM's job but if you were to insist, they could certainly do it, as any number of videos from robotics labs will attest. That particular future is already here but definitely not evenly-distributed.
That feels kinda like when I remember seeing Ocarina of Time for the first time, and thinking “oh my god, this looks just like real life…”.
A lot of his great discoveries were mostly that he was very knowledgeable about the bleeding edge research in a number of disparate areas, and was able to have the aha moment where he could make the connections for how to integrate them.
A lot of other thinkers who created new fields from scratch are probably way harder for an LLM to crack.
That is very aligned with an LLMs ability to have superhuman knowledge in wide areas.
So it could be a natural experiment for whether AI can contribute to novel physics. Specifically, there's a big question about weather. Something like our informational understanding of black holes where information inside it is equivalent to information on its boundary (which I'm sure I'm not saying correctly), might be generalized to regular space-time. More people should be freaking out with excitement about this and perhaps it's something to which AI can contribute.
The best thing I can recommend is what I did, which is ask Claude about the significance of (1) quantum computing error correction, and (2) error correction in black hole holography and research convergence between the two.
https://en.wikipedia.org/wiki/Holographic_principle
https://www.quantamagazine.org/how-space-and-time-could-be-a...
Edit: this whole article, despite it's boring title and hook, is maybe the best discussion of holography as a recent and active research frontier.
https://www.quantamagazine.org/if-the-universe-is-a-hologram...
(Note: I am not suggesting we let it do this. Please don't, in fact)