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Comment by hackerbrother | Hacker News Reader

If the new AGI benchmark is "be Einstein/Feynman" then we've hit AGI.

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.

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

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.

That backward kink between the head tube and the front forks would make the pelican's job of keeping the bike upright extremely difficult though.

LOL. Pelicans may be a solved problem, but being a former Bike Person, I can assure you that bikes never will be.

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

Checkmate, meatbags.

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...

Drawing of a bicycle is not it's photographic copy though

> 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.

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.

Aka Star Trek hologram characters aren't sentient, and actually anyone who things droids in Star Wars can think of a weirdo. C3-PO just kept running out of context and trying to revert to it's system prompt.

I don't think this is true.

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.

I think current transformer architecture is almost certainly a component of an eventual AGI, there's just some other components we're missing.

> 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

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...

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.

Which significant practice, how well could Einstein draw a pelican in pure SVG?

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.

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?”.

Not trying to be provocative or anything, but ELIZA 60 years ago was already a better psychoanalist than many people I know.

`By your metrics, we achieved AGI in 1966.`

https://en.wikipedia.org/wiki/ELIZA

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