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AI language models are struggling to “get” math

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

#91

Post by mgraczyk »

pessimizer wrote: post #89
mgraczyk wrote: post #82

Earlier quoted context omitted.

This is factually wrong, both in terms of quantity and quality. Current AI models are not "just sort of repeating and copying from memory". This is just an incorrect characterization of how they work and how they perform. AI skeptics often say things like this then backpedal with something like "Well they aren't really repeating what they heard, but their generative model is just a slightly more sophisticated version…

> Current AI models are not "just sort of repeating and copying from memory". This is just an incorrect characterization of how they work and how they perform. You say this, but don't explain how. Because this is exactly what they are doing. > AI skeptics often say things like this I'm not really an AI skeptic. I think that we're very close to AI being indistinguishable from people. There are clearly problems that ne…

I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work.

If you polled 1M random English speakers randomly and asked them whether or not a system that "just sort of repeating and copying from memory" could produce completely novel answers in response to completely novel questions, I suspect that the overwhelming majority would respond by saying no.

Similarly if you asked 1000 people working on LLMs whether they work by "copying from memory", I suspect nearly all would say no. It would be accurate to say they are "generating text via a probabilistic model of language, which is encoded in the weights of a neural network", but there really is just no sense in which the models are "copying" anything.

That being said, these models do "copy" some text in the sense that they can reconstruct some strings from their training input. For example every LLM I have played with can recite the first few paragraphs of A Tale of Two Cities verbatim. But that's a capability they have _in spite of_ their actual design, not because of it.

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

#92

Post by TuringTest »

woah wrote: post #84
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…

Have you ever used Github CoPilot? It does a lot of useful work, automating away rote typing in programming. Have you tried Dall-E or Stable Diffusion? They make good looking images. This comment seems completely unmoored from where the state of the art is right now.

Math follows a completely different approach with respect to how machine-learning AIs do their thing.

Reason derives its strength in having a few primitives and creating new assertions through the transformation of symbols by following precise rules (which is how algorithms work).

In ML-based AIs, everything is imprecise and probabilistic, and this kind of generation gets its strength from building recognizable from utterly imprecise inputs and training - quite the opposite of how logic and reason evolve. Now, "classic" AI was a powerful way to derive new knowledge, and automatic theorem proving is a strong discipline; but the recent breakthroughs in AI are not directly applicable to classic techniques.

Do you know what machine-learning AIs could be good for? Generating "insight" in problem solvers for guiding the theorem demonstrations through the proof search space, trying to find the best sub-spaces to explore. If there's a way to create human-like general AI, it will likely combine both kinds of generation - the rational methods of symbolic logic and the "irrational" statistical methods of ML.

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

#93

Post by phillipharr1s »

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/

Pretty sure the first continuation is a famous piece with a few notes messed up. Can't remember the name. Honestly it only sounds marginally better than the old markov chain continuations.

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

#94

Post by Der_Einzige »

ryandvm wrote: post #49
burlesona wrote: post #29

I genuinely wonder if we will find there are some inherent tradeoffs to knowledge and understanding such that if we ever have machines that can “think like humans” they would in practice run into human-like cognition limits: ie such machines would be “bad at math” in the same way humans are “bat at math” compared to conventional computers.

Indeed. I posit that as we get closer and closer to simulating how the human brain works in the pursuit of artificial intelligence, we're going to start seeing more and more of the same "bugs" that humans have (logical fallacies, susceptibility to illusions, mental illness, etc.) You think your job sucks now, just wait until you're dealing with the general AI over on the UX team that's trying to get your ass fired be…

At first, I thought it was surprising that a language model with a restricted vocabulary (e.g. banning the letter "E") acts significantly more "mentally ill", and then I thought about how I would come across if forced to use that constraint all the time, and I realized that maybe I'd appear mentally ill too!

You can play with LMs with constrained vocabularies here: https://huggingface.co/spaces/Hellisotherpeople/Gadsby

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

#95

Post by pessimizer »

mgraczyk wrote: post #91
pessimizer wrote: post #89

Earlier quoted context omitted.

> Current AI models are not "just sort of repeating and copying from memory". This is just an incorrect characterization of how they work and how they perform. You say this, but don't explain how. Because this is exactly what they are doing. > AI skeptics often say things like this I'm not really an AI skeptic. I think that we're very close to AI being indistinguishable from people. There are clearly problems that ne…

I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work. If you polled 1M random English speakers randomly and asked them whether or not a system that "just sort of repeating and copying from memory" could produce completely n…

> I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work.

Then we're arguing about the semantics of the word "copy." That is not an interesting argument when you know exactly what I mean and can express it clearly.

edit: If it helps, either substitute your description in whenever I say 'pretty much copy' or change the word "copy" to whatever word you want to use. But even though I can't reproduce the opening paragraph to A Tale of Two Cities verbatim, I can certainly write something that is "copying" it without doing that, and anyone who was familiar with the book and read my paragraph would agree with me.

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

#96

Post by vladf »

phillipharr1s wrote: post #93
vladf wrote: post #61

Earlier quoted context omitted.

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

Pretty sure the first continuation is a famous piece with a few notes messed up. Can't remember the name. Honestly it only sounds marginally better than the old markov chain continuations.

Isn’t that as good as it gets? The whole point of the continuations is that given a short leading prompt from a real piece that it should continue it realistically.

It didn’t get to train on the test set, if that’s what you’re implying, and I find it hard to believe the assertion that continuations are copies of the train set (if that’s your claim).

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

#97

Post by p1esk »

bloep wrote: post #80
vladf wrote: post #61

Earlier quoted context omitted.

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.

I don't see a lot of progress in AudioLM compared to results from 2018: https://storage.googleapis.com/magentadata/papers/maestro/in...

What's more interesting and concerning - listen carefully to the first piano continuation example from AudioLM, notice the similarity of the last 7 seconds to Moonlight sonata: https://youtu.be/4Tr0otuiQuU?t=516

I'm afraid we will see a lot of this with music generation models in the near future.

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

#98

Post by mgraczyk »

pessimizer wrote: post #95
mgraczyk wrote: post #91

Earlier quoted context omitted.

I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work. If you polled 1M random English speakers randomly and asked them whether or not a system that "just sort of repeating and copying from memory" could produce completely n…

> I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work. Then we're arguing about the semantics of the word "copy." That is not an interesting argument when you know exactly what I mean and can express it clearly. edit: If i…

It is semantics, but that was your whole point no?

> That's because they're not modelling anything

If we agree on "how LLMs work", then how can you claim that they aren't modeling anything? They are modeling language, and while it's unlikely current paradigms will be proving new mathematical truths, it's completely plausible to me that bigger models will be able to handle simple math word problems like those in the article, precisely because LLMs can model the "Alice", "Apple", and "Bob" entities.

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

#99

Post by p1esk »

dwringer wrote: post #88
CactusOnFire wrote: post #41

Earlier quoted context omitted.

AI is bad at Audio. AI can do MIDI fine.

MIDI is extraordinarily expressive and is likely used to sequence a large majority of music produced within the last three decades. A lot of the instruments you hear are synthesizers or samplers running directly from MIDI. There is a lot more to what MIDI can do, and is used for, than the conception most people have from "canyon.mid" or old website background music. If an AI can do MIDI just fine then it's an extreme…

If an AI can do MIDI just fine then it's an extremely small leap to doing audio just fine.

Unfortunately this is not true. It takes a huge amount of human effort to make MIDI encoded music sound good. The difference between MIDI and raw audio music generation is the same as the difference between drawing a cartoon and producing a photograph.

To clarify, yes MIDI can be expressive, but what's being generated when people say "AI generates MIDI music" is basically a piano roll.

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

#100

Post by p1esk »

vladf wrote: post #96
phillipharr1s wrote: post #93

Earlier quoted context omitted.

Pretty sure the first continuation is a famous piece with a few notes messed up. Can't remember the name. Honestly it only sounds marginally better than the old markov chain continuations.

Isn’t that as good as it gets? The whole point of the continuations is that given a short leading prompt from a real piece that it should continue it realistically. It didn’t get to train on the test set, if that’s what you’re implying, and I find it hard to believe the assertion that continuations are copies of the train set (if that’s your claim).

It definitely copied a piece of Moonlight sonata in the last 7 seconds of the first continuation sample: https://youtu.be/4Tr0otuiQuU?t=516
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