What if it can be Einstein, but can't draw a Pelican, write a solid college-level essay, or fold clothes?
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.
> 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 Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
If a model can't learn on their own to play some new game just as well as humans do, it's not AGI.
It's okay if they would take some hours or days of learning (like humans might), but if they can't do it at all during their normal operation, that's not general intelligence
> You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
But humans have general intelligence. AGI is about matching human ability, and we know this is possible in principle because brains exist
The only important part of 'general' is the ability to learn from experiential data and update your own model. That's what leads to general capability. Humans can't oneshot any task natively, but we can practice for a while until we uncover often novel methods of accomplishing something.
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).
Most people can't draw a bicycle. There was an artist 10 years ago that asked people to sketch a bike, and then turned these sketches into 3D renders - quite funny.
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.
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].
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.
What if it can be Einstein, but can't draw a Pelican, write a solid college-level essay, or fold clothes?
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.
> 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 Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
If a model can't learn on their own to play some new game just as well as humans do, it's not AGI.
It's okay if they would take some hours or days of learning (like humans might), but if they can't do it at all during their normal operation, that's not general intelligence
> You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
But humans have general intelligence. AGI is about matching human ability, and we know this is possible in principle because brains exist
The only important part of 'general' is the ability to learn from experiential data and update your own model. That's what leads to general capability. Humans can't oneshot any task natively, but we can practice for a while until we uncover often novel methods of accomplishing something.
4 replies →
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).
Most people can't draw a bicycle. There was an artist 10 years ago that asked people to sketch a bike, and then turned these sketches into 3D renders - quite funny.
https://mymodernmet.com/gianluca-gimini-velocipedia-bicycles...
https://qz.com/681345/an-artists-3d-renderings-of-bicycles-d...
1 reply →
Folding clothes is happening. https://www.youtube.com/watch?v=cRZNwgvcWUg
AI in math is ongoing. https://spectrum.ieee.org/ai-in-mathematics
I can also recommend this channel to get a sense of where AI is in frontier maths.
https://www.youtube.com/@easy_riders
How good was Einstein at drawing pelicans on bicycles by writing SVG code?
Checkmate, meatbags.
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.
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.
> Pelicans are a solved problem at this point. An open-weight model on my own machine gave me this
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…”.
1 reply →
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.
Call me when Astra gets the Nobel prize... We'll have AGI when prizes have two categories, one for assisted humans, and one for pure-AI.
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?”.
Wouldn’t that mean producing novel work like relativity and QED?
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.
[dead]