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.

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...

> 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…”.

[dead]

Do you have anywhere you recommend where I can read more on this?

Do you mean after? People do this!! But I think it’s a bit different. It won’t be apples to apples because the data volume I think is just so much different. Maybe there are good experiments for something like this.

AI could discover candidate novel physics without autonomously operating new physical experiments, and humans or instruments can later independently validate the result. This is analogous to how Einstein developed theories whose predictions were confirmed by experiments and observations only years or decades later.

Pharmaceuticals already are…

It could be a good theoretical physicist. Actually it could be a good experimental physicist as well since senior experimental physicists use grad students for the manual labor.

Why not?

Solving "open problems" will push the field forward.

Tokens are the most basic input unit of an LLM. But tokens don't generally correspond to words or letters, rather sub-word sequences. So Strawberry might be broken up into two tokens 'straw' and 'berry'. It has trouble distinguishing features that are "sub-token" like specific letter sequences because it doesn't see letter sequences but just the token as a single atomic unit. 'Straw' and 'r' are two tokens but an LLM is entirely blind to the fact that 'straw' has one 'r' in it.

As an analogy, I might ask you to identify the relative activations of each of the three cone types on your retina as I present some solid color image to your eyes. But of course you can't do this, you simply do not have cognitive access to that information. Individual color experiences are your basic vision tokens.

LLMs see tokens, not words spelled out with letters.

Imagine verbally asking someone who has never seen written text the same question: unless they memorized the answer for the specific word you're asking about, they'd have to guess.

but a human doesn't attempt to make up an answer, the human knows that he doesn't know?

I have slight dyslexia. I can't automatically write double consonants all the time.

But counting certain type of letters is very simple algorithmic task especially in written text. Any reasonably intelligent actually thinking thing should come up with algo and then execute it. Which to me sounds like reasonable minimum bar for general intelligence.

> computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.

I don't think it's irrelevant but perhaps not in the way you're assuming. When assessing AGI I'm not evaluating counting characters or even the execution of math operators at any scale or speed. As you observe, computer software from Regex to spreadsheets and Mathematica already handle that well. But AGI isn't about what computers can do, it's about whether AIs can do the specific things which, until now, have been uniquely human capabilities. Like understanding nuanced context and then coming up with novel approaches to solve a new kind of problem not relying on any specific prior training or knowledge (the 'G' is for General).

Most definitions of AGI start from a baseline that already assumes easily passing a Turing test and doing anything via text response that a high school graduate could. I ding LLMs not for failing to count but for failing to intuitively understand the nuanced context of a simple class of problem it hasn't seen in its training data. I fully understand that the reason LLMs fail letter counting is that they operate at the token level. They weren't trained on individual letters first, like human 2nd graders.

The only reason recent LLMs get strawberry and blueberry correct now is that they have those words on their pre-training 'cheat sheet'. However, the underlying fundamental weakness in the way LLM intelligence works which leads to this failure mode still hasn't been addressed. Even when the frontier labs add "recognize any sub-token counting question and write a Python script" to the training cheat sheet so LLMs always pass that test... they'll still be unable to recognize a simple class of problem which isn't on their 'cheat sheet'. As long as that's the case, to me, they aren't AGI because they can't fully replicate human-like recognition of novel problem classes. And it's not just about letter-counting. That gap and others like it lead to many other kinds of non-human brittleness in LLM problem solving. Those are the classes of reasoning, intuition and insight that the ARC-AGI series has been trying to queue up as targets. Not to show how bad LLMs are but to help them be great in all these counter-intuitive edge cases

> which just brings us back to the whole birds vs planes thing.

That just says we don't need to design an AI like a brain. That's not part of this discussion at all.

> computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.

I'm confused, is your argument something like "It's too easy so AGI doesn't need to be able to do it"?

The fact that very basic computers can do it makes failures embarrassing when testing for AGI, not irrelevant.

It matters under the lens of AGI. Artificial intelligence that can meet or exceed human intelligence. Sure, it's good at some things but rather limited at others.

Breadth of capabilities matters... and a promotional video is nice and all, but people are throwing this term around like it's a prize they've won, but they've not gotten there yet.

The "dream" that these labs are mostly selling is the ability for capital to subscribe to their AI for cheaper than it costs a human to do some task. Not to have a "human + AI hybrid where the human is responsible". It's what the whole AGI valuation is based off of, in that scenario, with no human oversight, the agent they lease has to be responsible for the task?

> What do you mean by bearing no real responsibility for its actions?

If you give an "intelligent agent" offered by one of these model providers a task of updating the content of your website, and it updates it with inappropriate adult content, who incurs the cost of the machine's error? The model provider generally does not.

It it makes a mistake and deletes your website from AWS, who is responsible?

If it targets another website because it decides that it is "part" of your website and attempts to break into it, who is responsible?

If future jobs are simply reduced to liability scape goats (or more appropriately reverse centaurs) for management to pin things on then I'm taking up goose farming.