Comment by phlakaton
9 hours ago
If it allows you to distinguish readily between human intelligence and computer intelligence, it seems quite relevant to the question of whether computers have achieved something akin to human intelligence.
It may not be useful for anything else, but at least it can say that.
But it's not relevant as a metric to gauge distance to human intelligence. Humans see individual letters, LLMs do not. If I asked you the relative activation of the cones in your retina as I showed you some solid color image, you couldn't do it. You simply do not have cognitive access to that information. But that says nothing about your intelligence.
A more accurate test would be to give it a list of words (or anything represented as a single token) and ask it how many times that token appeared. I'm sure they have no trouble at that task.
but a human doesn't attempt to make up an answer, the human knows that he doesn't know?
Yes, they have some alien failure modes. But that should be expect, they are an alien intelligence. I might be willing to grant that a lack of ability to reflect on its own level of knowledge is a demerit to it being generally intelligent. But then again it is largely an artifact of training. I suspect if there were a guessing penalty during pretraining they would develop or more readily communicate the strength/reliability of their knowledge.
this is so false that Dunning and Kruger invented a name for it
I am not enthusiastic about criteria for human-like intelligence that imply that dyslexic people don't have human-like intelligence.
[EDITED to add:] I actually don't know whether dyslexic people find it difficult to count letters in words, if they have them already written down by someone else. I suspect they find it harder than people who aren't dyslexic. But perhaps "blind people whose spelling is poor" would have been better; I would not want to deny them human-like intelligence either.
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.
Exactly. It's got nothing to do with sensory input and everything to do with reasoning.
If someone asked me how many f's are in a word I hadn't seen before verbally, then a reasoned response would be that I don't know, but I estimate based on the syllables...or ask them to spell it out.
These are all the sorts of questions where general problem solving works, even if the conclusion is "I don't have enough data to speculate".
So that these models fall apart on it so readily means we're either grossly handicapping then with the requirement to "be helpful" or they just fail to recognize the problem and are just stochastically spitting out a high probability token sequence for the input.
which just brings us back to the whole birds vs planes thing.
turns out that flapping wings is not the right way to unlock human flight.
computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.
> 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
appreciate your response, but it's still birds vs planes.
AI does not need to feel emotions or have a heartbeat to be useful. It only needs to perform a task correctly à la Chinese room.
>therefore cannot fully replicate human-like intelligence
this does not follow. planes don't flap wings therefore they cannot fly?
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Models often write python scripts for counting such things…
> 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.
>I'm confused, is your argument something like "It's too easy so AGI doesn't need to be able to do it"?
Do you possess magnetoreception? a stupid pigeon can "see" the earth's magentic field. why are you blind to it? does a lack of magnetoreception make your intelligence any less "general"
no, you're just blind to it because that's just the way it is.
LLMs are blind to character counting because that's the way they are.
It didn't stop ChatGPT from finding the Jacobian Conjecture counterexample.
Human intelligence and machine intelligence are only going to cross over to a certain degree.
same as plane flight and bird flight are only kinda related.
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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.