Comment by petilon

I'm not in the business of defining AGI, but "really good" obviously isn't that.

> What if it can be Einstein, but can’t draw a Pelican, write a solid college-level essay, or fold clothes?

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.

Can definitely write college level essays and have for a while. The jobs is that when LLMs first started getting popular, but aren’t quite common professors were that some of the worst students in class started writing the best essays. Now everyone complains because they can detect the slop, but most human writing is so bad. But the really good human writing is still much better.

Adding sibling comments, I think some people may be overestimating how well the median human can draw a pelican, or create an SVG of a pelican (depending if we’re comparing to an image generation model, or SVG generation).

Folding clothes is happening. https://www.youtube.com/watch?v=cRZNwgvcWUg

AI in math is ongoing. https://spectrum.ieee.org/ai-in-mathematics

How good was Einstein at drawing pelicans on bicycles by writing SVG code?

Checkmate, meatbags.

Pelicans are a solved problem at this point. An open-weight model on my own machine gave me this: https://crimson-jeri-74.tiiny.site/

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.

I can't draw a pelican. Literally my only point of reference would be AI pelican drawings from the test. Otherwise I wouldn't know how to draw one at all.

I would be able to draw an accurate bicycle, but I'm an outlier on that. Most people could not draw one [1].

[1]: https://www.booooooom.com/2016/05/09/bicycles-built-based-on...

Laundry folding has become a doable demo for startups, and ChatGPT has been spitting out college essays for years.

That’s like that scene in the I, Robot movie when Will Smith’s character is asking the robot “can you turn an emtpy canvas into a work of art, or compose a symphony?” and the robot replies “can you?”.

I would maybe argue that Einstein was the most LLM-like of great thinkers.

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.

This is as good a time as any to note that we might be closing in on a new conceptual revolution in our own time as it relates to holography and an information centric approach to spacetime. Obviously It's the furthest possible thing from a guarantee, but it has much of the enthusiasm and motivation that string theory had previously enjoyed in prior decades.

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.

There was symbolic AI programs in the 1980’s that “discovered” Kepler’s laws and the resulting solar system model from just tycho brache’s astronomical observations. That was the the very first “new physics” ever.

i wonder if we could train a modal, and omit all data prior to 1899, and see what happens?

as always, as good as its prompt...

Would this be possible without it being able to run novel real-world physics experiments autonomously?

(Note: I am not suggesting we let it do this. Please don't, in fact)

That’s already been done. I know of at least one novel result contributed by Claude to frontier physics. I’m sure there is more.

For it to be like a human it wouldn't just need to solve existing phsyics problems, it would need to push the field forward and introduce new paradigms.