[youworkwepay]

Or it's time to step back and call it what it is - very good pattern recognition.

I mean, that's cool... we can get a lot of work done with pattern recognition. Most of the human race never really moves above that level of thinking in the workforce or navigating their daily life, especially if they default to various societally prescribed patterns of getting stuff done (eg. go to college or the military <Based on <these criteria>, find a job <based on the best fit with <this list of desirable skills & experiences>, go to <these places> to find love....)

[K0balt]

So, I am conflicted about this.

If we take an example of what is considered a priori as creativity, such as story telling, LLMs can do pretty well at creating novel work.

I can prompt with various parameters, plot elements, moral lessons, and get a de novo storyline, conflicts, relationships, character backstories, intrigues, and resolutions.

Now, the writing style tends to be tone-deaf and poor at building tension for the reader, and it is apparent that the storytelling has little “theory of mind” of the reader, but the material has elements that we would certainly consider to be creative if written by a student.

It seems we must either cede that LLMs can do some creative synthesis, as this and some other experiments of mine suggest, or we must decide that these tasks, such as “creative writing” are not in fact creative, but rather mostly or strictly derivative.

There is some argument to be had in assertions that storytelling is all derivative of certain patterns and variations on a fixed number of tropes and story arcs… but arguing this begs the question of whether humans actually do any “pure” creative work , or if in fact, all is the product of experience and study. (Training data)

Which leads me to the unpleasant conflict about the debate of AI creativity. Is the debate really pointing out an actual distinction, or merely a matter of degree? And what are the implications, either way?

I’m left with the feeling that LLMs can be as capable of creative work as most 8th grade students. What does this say about AI, or developing humans? Since most people don’t exceed an 8th grade level of literacy, what does this say about society?

Is there even such a thing as de novo idea synthesis?

Troubling questions abound.

[danielbln]

To add to this pondering: we are discussing the state today, right now. We could assume this is as good as it's ever gonna get, and all attempts to overcome some current plateau are futile, but I wouldn't bet on it. There is a solid chance that 8th grade level writer will turn into a post-grad writer before long.

[zeroonetwothree]

So far the improvements in writing have not been as substantial as those in math or coding (not even close, really). Is there something fundamentally “easier” for LLMs about those two fields?

[danielbln]

Much more formal structure and generally code can be tested for correctness. Prose doesn't have that benefit. That said, given the right prompt and LLM, you can squeeze out surprisingly good stuff: https://bsky.app/profile/talyarkoni.com/post/3ldfjm37u2s2x

[zeroonetwothree]

I have no doubt that LLMs do creative work. I think this has been apparent since the original ChatGPT.

Just because something is creative doesn’t mean it’s inherently valuable.

[IshKebab]

> Or it's time to step back and call it what it is - very good pattern recognition.

Or maybe it's time to stop wheeling out this tedious and disingenuous dismissal.

Saying it is just "pattern recognition" (or a "stochastic parrot") implies behavioural and performance characteristics that have very clearly been greatly exceeded.

[jampekka]

What the fundamental limitations of "pattern recognition" or "stochastic parrots" that LLMs have exceeded?

[IshKebab]

They can generalise to novel inputs. Ok often they mess it up and they're clearly better at dealing with inputs they have seen before (who isn't?), but they can still reason about things they have never seen before.

Honestly if you don't believe me just go and use them. It's pretty obvious if you actually get experience with them.

[jampekka]

Current LLMs are equivalent to tabular Markov chains (though these are too huge to realistically compute). What's the size limit when a tabular Markov chain can generalize to novel inputs?

[IshKebab]

No idea. I'm not sure how that's relevant anyway.

[dsr_]

Citation needed. Please be more specific, or else this is just a tedious and disingenuous advocacy.

[Lerc]

Gpt4 can add very large integers.

It is evident that it is not recalling the sum because all combinations of integer addition were likely not in the training data, Storing the answer to the sum of all integers up to the size that GPT4 can manage would take more parameters than the model has.

That addition is a small capability but you only need a single counterexample to disprove a theory.

[alexashka]

> That addition is a small capability but you only need a single counterexample to disprove a theory

No, that's not how this works :)

You can hardcode an exception to pattern recognition for specific cases - it doesn't cease to be a pattern recognizer with exceptions being sprinkled in.

The 'theory' here is that a pattern recognizer can lead to AGI. That is the theory. Someone saying 'show me proof or else I say a pattern recognizer is just a pattern recognizer' is not a theory and thus cannot be disproven, or proven.

This is also known as Russell's teapot. https://en.wikipedia.org/wiki/Russell%27s_teapot

If someone claims there's a teapot out in space - the burden of proof is on the person making the claim, not on the person saying it is bullshit.

[Lerc]

It's not hardcoded, reissbaker has addressed this point.

I think you are misinterpreting what the argument is.

The argument being made is that LLMs are mere 'stochastic parrots' and therefore cannot lead to AGI. The analogy to Russell's teapot is that someone is claiming that Russells teapot is not there because china cannot exist in the vacuum of space. You can disprove that with a single counterexample. That does not mean the teapot is there, but it also doesn't mean it isn't.

It is also hard to prove that something is thinking. It is also very difficult to prove that something is not thinking. Almost all arguments against AGI take the form X cannot produce AGI because Y. Those are disprovable because you can disprove Y.

I don't think anyone is claiming to have a proof that an LLM will produce AGI, just that it might. If they actually build one, that too counts as a counterexample to anybody saying they can't do it.

[reissbaker]

GPT-4o doesn't have hardcoded math exceptions. If you would like something verifiable, since we don't have the source code to GPT-4o, consider that Qwen 2.5 72b can also add large integers, and we do have the source code and weights to run it... And it's just a neural net. There isn't secret "hardcode an exception to pattern recognition" in there that parses out numbers and adds them. The neural net simply learned to do it.

[alexashka]

That's interesting, I didn't know that, thanks.

Is the claim then that LLMs are pattern recognizers but also more?

It just seems to me and I guess many others that the thing it is primarily good at is being a better google search.

Is there something big that I and presumably many others are missing and if so, what is it?

[kneegerman]

so how much you have riding on nvidia bro?

[IshKebab]

Nothing. I just use ChatGPT and Claude so I am familiar with their capabilities and limitations.

Imagine if people who had never used VR kept saying it's just a TV on your face, or if people who had never used static types kept saying they're just extra work you have to do, or if people who had never had sex kept saying it's just a way of making babies.

It's a tedious claim when it's so easily disproven by going to a free website and trying it. Why are people so invested in AI being useless that they'll criticise it so confidently without even trying it?