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.
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.
Therefore: the current transformer architecture is fundamentally incapable of AGI because the models have no mutable long-term memory.
You only have weights (large immutable memory), or context (small mutable memory).
Humans have mutable long-term memory: I can learn a new skill, adapt an old skill to new information, or learn new knowledge today that I couldn't perform/didn't know yesterday. I don't have a training cutoff.
Context engineering is an attempt to paper over this limitation. You can get really far with context engineering and huge models, but you will never get to AGI because there are many tasks where humans' mutable long-term memory outperforms.
For example, a human can invent a new musical instrument and then learn how to play the instrument they just invented. That's inference (inventing an instrument) leading to training (neuroplasticity). Humans have the ability to train our NNs with considerably fewer training samples. Everything that you can do with transformers is in one causal direction: training -> inference.
So if we take a huge with enough compute (CPUs, b200s, petabytes of SSDs), we install on it both the Astra, and the toolsuite to incorporate new sensory inputs (threads/sessions), camera, microphone, temp sensors, the lot, into a new version of the model. This model is then swapped for the old model, or traffic slowly brought over, or even adjusting weights in place.
Then my hypothesis is that thing as a whole could achieve AGI.
This feels like a very close approximation on how we humans evolve our brain. By encountering new experiences/sensations, classifying them as negative or positive to us, filling it away in neurons. Or by training motor skills etc. In the end we get more connections between neurons in our brain and we are capable of more.
Bingo, LLM architecture just does not lend itself to becoming AGI. They can get really good, sure, but they will always struggle with novel input and scenarios.
The more training data that is shoved in to them, the more they'll seem to solve novel situations, but in reality it'll be things that exist in the training data.
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.
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].
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.
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.
Therefore: the current transformer architecture is fundamentally incapable of AGI because the models have no mutable long-term memory.
You only have weights (large immutable memory), or context (small mutable memory).
Humans have mutable long-term memory: I can learn a new skill, adapt an old skill to new information, or learn new knowledge today that I couldn't perform/didn't know yesterday. I don't have a training cutoff.
Context engineering is an attempt to paper over this limitation. You can get really far with context engineering and huge models, but you will never get to AGI because there are many tasks where humans' mutable long-term memory outperforms.
For example, a human can invent a new musical instrument and then learn how to play the instrument they just invented. That's inference (inventing an instrument) leading to training (neuroplasticity). Humans have the ability to train our NNs with considerably fewer training samples. Everything that you can do with transformers is in one causal direction: training -> inference.
So if we take a huge with enough compute (CPUs, b200s, petabytes of SSDs), we install on it both the Astra, and the toolsuite to incorporate new sensory inputs (threads/sessions), camera, microphone, temp sensors, the lot, into a new version of the model. This model is then swapped for the old model, or traffic slowly brought over, or even adjusting weights in place.
Then my hypothesis is that thing as a whole could achieve AGI.
This feels like a very close approximation on how we humans evolve our brain. By encountering new experiences/sensations, classifying them as negative or positive to us, filling it away in neurons. Or by training motor skills etc. In the end we get more connections between neurons in our brain and we are capable of more.
Bingo, LLM architecture just does not lend itself to becoming AGI. They can get really good, sure, but they will always struggle with novel input and scenarios.
The more training data that is shoved in to them, the more they'll seem to solve novel situations, but in reality it'll be things that exist in the training data.
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...
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.
> 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…”.
For me it was the wheels. I couldn't stop staring at the wheels... how did it get them so freaking perfect? Mad respect to GLM 5.3.
Laundry folding has become a doable demo for startups, and ChatGPT has been spitting out college essays for years.
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...
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.
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.
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