Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

AI language models are struggling to “get” math

spectrum.ieee.org

Post reply on HN
Print view View original

71–80 of 201 posts

Re: AI language models are struggling to “get” math

#71

Post by mjburgess »

yeasurebut wrote: post #70
dimmuborgir wrote: post #59

Earlier quoted context omitted.

Those models are not trained on short loops. They are trained on whole songs just like image generation models are trained on whole images. And yet they struggle to repeat sections, modulate to a different key, create bridges, intros and outros. After a few seconds of hallucinating a melodic line they simply abandon the idea and migrate to another one. There is no global structure whatsoever.

Musicians don’t spit out an album in one sitting and they’re highly trained in theory. They get bored and tired of a process and take breaks. They come up with an album of loops composed together over time. AIs state will forever be constrained to the limits of human cognition and behavior as that’s what it’s trained on. I read published research all year. Circular reasoning. Tautology. It’s all over PhD thesis. Ther…

I think it's deeply depressing that AI has been sold as something even capable of modelling anything humans do; and quite depressing that this comment exists.

"AI" is just taking `mean()` over our choice of encodings of our choice of measurements of our selection of things we've created.

There is as much "alike humans" in patterns in tree bark.

AI is an embarrassingly dumb procedure, incapable of the most basic homology with anything any animal has ever done; us especially.

We are embedded in our environments, on which we act, and which act on us. In doing so we physically grow, mould our structure and that of our environment, and develop sensory-motor conceptualisations of the world. Everything we do, every act of the imagination or of movement of our limbs, is preconditioned-on and symptomatic-of our profound understanding of the world and how we are in it.

The idea that `mean(424,34324,223123,3424,....)` even has any revelance to us at all is quite absurd. The idea that such a thing might sound pleasant thru' a speaker, irrelevant.

This is a product of i dont know what. On the optimist side, a cultish desire to see Science produce a new utopia. On the pessimisst side, a likewise delusional desire to see Humans as dumb machines.

What a sad state!

Re: AI language models are struggling to “get” math

#72

Post by hgomersall »

lupire wrote: post #21
yshrestha wrote: post #9

Language models can generate a Python function that does the math perfectly. I bet you would get better results if you tweaked the prompt to say "Generate a Python program that solves X math problem" and then just ran the resulting Python script. It does not need to be AGI to be useful.

You mean "generate a Python function that calls a library that does math perfectly, right?

In the limit, it's going to design an AI to write some python to call a library that does the math perfectly.

Re: AI language models are struggling to “get” math

#73

Post by denton-scratch »

dimmuborgir wrote: post #39
mjburgess wrote: post #27

How much of this is just "AI is bad at everything", but in the math case, it's easier for the lay person to tell . It's all just passable garbled nonesense that the reader (goes to lengths) to interept based on their prior knowledge, which is not expressed in the syntax of what these systems output. In the case of mathematics, we're far less willing to "BS away" the interpretive failures. But if we were equally deman…

AI is bad at music also. Even the state of the art transformer models can't produce more than a few seconds of coherent melodic phrases.

It doesn't surprise me that an AI model for language can't grok maths or music. I can't see how a language model can map to maths. Hell, I don't even know how to describe music in words. It's possible to articulate some maths in words, but that often involves using words with unexpected definitions.

Re: AI language models are struggling to “get” math

#74

Post by Sharlin »

sva_ wrote: post #15
sharemywin wrote: post #7

Earlier quoted context omitted.

Online Integral Calculator Solve integrals with Wolfram|Alpha https://www.wolframalpha.com/calculators/integral-calculator...

Now try to make a computer prove that there are no natural numbers a,b,c; so that a^n + b^n = c^n for any n > 2.

Shifting the goal posts a bit, aren't we?

Re: AI language models are struggling to “get” math

#75

Post by the_af »

sharemywin wrote: post #3
PaulHoule wrote: post #2

Ashby strikes again. Current sequence models don't have the right structures to represent math. Even if they use floating point internally, they can't really float the point because the nonlinearity in the model has a certain scale. A system that processes language can take advantage of the human desire for closure https://www.eurogamer.net/blood-in-the-gutter to fool people into thinking it is more capable than it r…

computers already do math. language models just need to translate problems into code of some kind that can be run to get the answer. executive function/planning is probably the biggest problem at this point for ai.

> language models just need to translate problems into code of some kind that can be run to get the answer

A huge "just"! Isn't this the magic step? Translating ambiguous symbols to meaning and combining them in meaningful ways is a big deal which, apparently, these AI models cannot do. They can just parrot things.

Re: AI language models are struggling to “get” math

#76

Post by pessimizer »

That's because they're not modelling anything. The shocking thing about current AI models is that just sort of repeating and copying from memory what you've heard and seen gets you 97% of the way to imitating a person.* They still need to generate actual models somewhere to create consistency; so many generated images with one eye completely different from the other, or three arms, or fingers that grow into their cellphones.

If you solve this, you've probably solved almost anything in the simulation field. I have no confidence that the solution will even be complicated. Information consumed needs to be used to add to some sort of model, and that model always needs to be used as part of input. The complicated part would be to make that base model able to modify itself reasonably based on input, to tolerate constant inconsistency, and to constantly refine itself towards consistency i.e. ruminate.

I think a huge difference (which I think was approached through theories of embodied cognition) is that people start with a model (or the ability to create a model) of themselves. We can apply that model to other things and use it both to change how we ourselves behave, and how we speculate about the invisible states of other things. It's not for nothing that we can (and must) anthropomorphize anything.

-----

* Which was huge towards the confirmation of my belief that this is all people do 97% of the time.

Re: AI language models are struggling to “get” math

#77

Post by the_af »

thwayunion wrote: post #16
JacobiX wrote: post #5

Earlier quoted context omitted.

I'm not so sure about that. Of course computers can do arithmetic operations, but this is not the same as solving math problems, proving theorems, etc. Even mathematical objects are approximated up to an approximation error in a computer (like a differentiable manifold or a real number).

> Of course computers can do arithmetic operations, but this is not the same as solving math problems, proving theorems, etc. Computers can solve math problems and prove theorems; this remains a significant subfield of Computer Science with lots of industrial use cases. However, pure machine learning based approaches toward these problems remain subpar. > Even mathematical objects are approximated up to an approximat…

> Computers can solve math problems and prove theorems

But the specification of the problem must be done by a human, translating to a formalized system that the software can understand. And if there's a problem in the formal specification, it's mostly up to the human to notice and fix; the computer will happily output garbage or crash or enter an infinite loop.

So it seems this translation, going from an exploration of the problem statement, usually in ambiguous terms, to a formal specification, and the awareness to possibly detect whether the answers make sense and the specs were right, is uniquely human.

Re: AI language models are struggling to “get” math

#78

Post by Sharlin »

JacobiX wrote: post #5
sharemywin wrote: post #3

Earlier quoted context omitted.

computers already do math. language models just need to translate problems into code of some kind that can be run to get the answer. executive function/planning is probably the biggest problem at this point for ai.

I'm not so sure about that. Of course computers can do arithmetic operations, but this is not the same as solving math problems, proving theorems, etc. Even mathematical objects are approximated up to an approximation error in a computer (like a differentiable manifold or a real number).

Well, you don't need anything else than basic arithmetic to encode the entirety of, say, ZFC, enumerate every proposition in it, and halt iff you find a proof of whatever theorem you're after. It just might take a while…

Re: AI language models are struggling to “get” math

#79

Post by jxy »

> “When multiplying really large numbers together … they’ll forget to carry somewhere and be off by one,” says Vineet Kosaraju, a machine learning expert at OpenAI. Other mistakes made by language models are less human, such as misinterpreting 10 as 1 and 0, not ten.

So the expert has never seen a seven year old struggling in adding two single digit numbers together? Did the expert learn 1 and 0 being 10 first and learn to speak second?

> The MATH group found just how challenging quantitative reasoning is for top-of-the-line language models, which scored less than 7 percent. (A human grad student scored 40 percent, while a math olympiad champ scored 90 percent.)

Is this that surprising? How would our ieee editor score on the same problem set?

Re: AI language models are struggling to “get” math

#80

Post by bloep »

vladf wrote: post #61
dimmuborgir wrote: post #39

Earlier quoted context omitted.

AI is bad at music also. Even the state of the art transformer models can't produce more than a few seconds of coherent melodic phrases.

Have you heard the piano continuations of AudioLM? https://google-research.github.io/seanet/audiolm/examples/

Indeed, there is lots of denial or ignorance in this thread (ignorance in the technical sense). AudioLM already produced impressive results and it's a tiny fraction of what is already possible because performance simply improves with scale. One can probably solve music generation today with a ~$1B budget for most purposes like film or game music, or personalized soundtracks. This is not science fiction.
Post reply on HN