It may not be useful for anything else, but at least it can say that.
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.
“I don’t know”
[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.
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.
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.
The models don't get to see the text written down. By the time your input reaches them at all it's been converted into tokens. By the time they start thinking about it its been converted into embeddings in a sort of concepts-and-word-fragments space.
I do think it's a definite weakness of most LLM systems that they are bad at admitting (maybe because they're bad at knowing) when they don't really know something. (I have the impression that Anthropic's models are better at this than OpenAI's, but that isn't based on careful research or anything.)
What's the actual behaviour of today's LLM systems on these questions when they're allowed to "think"? Someone upthread mentioned that Sonnet 5 at "medium" thinking level mostly gets them right but makes mistakes sometimes. It would be interesting if we could see what its chain-of-thought looks like in these cases.
... I just tried six questions of this kind on Sonnet 5 at "medium" thinking -- this is the default thing you get from free-Claude -- and it got them all right in a way that at least superficially looks as if it's spelling them out and counting. Obviously this isn't enough to guarantee that there isn't anything grossly wrong with its reasoning capabilities in this area, but it doesn't look to me like "falling apart" and it doesn't look like strong evidence that thinking of it as a stochastic parrot is helpful here.
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.
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
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?
This example still misses my point, which isn't related to usefulness or economic value. I concede that LLMs can have greater utility and economic value than humans on many tasks. The point is most definitions of AGI include something like "can fully replicate all the routine daily tasks done by any competent high-school graduate." That's not related to whether LLMs can solve many high-value problems faster and at larger scale than any human. That was also true of ENIAC in 1946.
The fact an airplane can fly faster and farther than any bird is irrelevant to whether an airplane can "fully replicate all the routine daily tasks done by any competent bird." That's the bird equivalent to most AGI definitions. An airplane can't build a nest or recognize the signals encoded in birdsong.
In the same way airplanes fail the 'bird replacement' requirement, AIs currently fail most AGI requirements only on the terms: "fully", "all" and "any". And in this context, airplanes scoring 15,000% more than birds on 'speed' and 'distance' doesn't matter any more than AIs scoring 15,000% more than humans on 'add 10,000 numbers'. We still aren't near AGI because LLMs cannot fully match any high-schooler's ability to independently conceive new approaches to novel problems not in their prior training data.
which gets us closer to philosophical questions which I'm personally not that interested in.
>In the same way airplanes fail the 'bird replacement' requirement, AIs currently fail most AGI requirements only on the terms: "fully", "all" and "any".
I'm not sure we want a machine that fully succeeds that test.
Planes pass the 'bird replacement' test on the only criteria that matters to us ... flying.
If we wanted nest making planes I think we'd have them by now. Nest making doesn't rate highly on the problems we're looking to solve though.
I don't want a machine that is moody, or depressed or has schizophrenia, which are all pat of the human condition.
We don't need the human "intuition magic dust" to do 99.99999% of useful work.
They're machines designed to do the work we don't want to. That's as "general" as their intelligence needs to be.
I'd prefer if my clothes folding machine did not have an existential crisis.
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.
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.
But if I can't calculate it myself I know to use that basic computer to do it, not make up an answer.
> Human intelligence and machine intelligence are only going to cross over to a certain degree.
That's where the word "General" kicks in. If there's big limitations on the overlap forever, then there will never be AGI.
maybe. we'll see.
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.