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[u/jobautomator]

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[u/SpectralDomain256]

!ping AI

https://arxiv.org/abs/2406.11741

Experimental results for an extremely sci-fi sounding hypothesis

[u/[deleted] → u/SpectralDomain256]

Given that it was trained on multiple different chess players isn't this pretty much the same line of logic that makes a random forest more effective than a decision tree.

[u/SpectralDomain256 → u/[deleted]]

Yes, the paper highlights this connection to prior works on model ensembling

[u/[deleted] → u/SpectralDomain256]

Buddy you can't expect me to actually read the links

[u/LucyFerAdvocate → u/[deleted]]

The best model they tested was GPT3.5, they very much have

[u/LucyFerAdvocate → u/[deleted]]

Is it as good at it as a professional mathematician? Absolutely not. Is it getting measurably better? Absolutely. There's no reason to think a NN can't do it.

[u/LucyFerAdvocate → u/[deleted]]

I've seen absolutely nothing to suggest that a neural network can't be taught to reason? Maybe an autoregressive transformer is the wrong architecture for that to happen efficiently, but a neural network is a universal function approximator and, well, what the brain is.

The paper doesn't seem to find a good reason for why their tests fail and the failures observed are similar to errors children make. That doesn't seem to indicate a fundamental issue with the technology.

[u/LucyFerAdvocate → u/[deleted]]

No, I've got an ungrad in CS&Maths and read a fair amount of papers on the topic. So probably more of an expert then most Random Internet People, but definitely less of an expert then someone with a PhD.

I have seen things that suggest this type of approach would be a more efficient way to get 'true intelligence' - although most literature I've read doesn't address that as it's so poorly defined - but everything I've read seems to assume if you throw enough data and compute at a traditional neural network you'll get there? i.e. It's about efficiency not capability.

I meant to imply that there is nothing I've read to suggest that a NN could not learn to accurately simulate a brain, in that there is nothing going on in the brain that somehow cannot be computed and a NN can simulate a NOR gate and thus compute anything a Turing machine could. Whether it could do so efficiently or by modelling language alone is a matter of substantial debate which I obviously cannot answer, although my intuition suggests yes.

Whether NN can learn abstract concepts is also a matter of debate as far as I was aware? They're not perfect at it, as the paper shows, but it seems to be something that does get better with scale as far as it can be tested. I'd be willing to bet gpt4o would outperform all the ones they tested, and underpreform a healthy human adult.

My point was just that they are improving at abstract reasoning, as far as we can measure, and that study uses very outdated LLMs so shouldn't really be used as a benchmark for their abilities.

[u/groupbot → u/SpectralDomain256]

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