I'm not saying that LLMs can't learn about the world - I even mention how they obviously do it, even at the learned embeddings level. I'm saying that they're not compelled by their training objective to learn about the world and in many cases they clearly don't, and I don't see how to characterize the opposite cases in a more useful way than "happy accidents."

I don't really know how they are made "good at math," and I'm not that good at math myself. With code I have a better gut feeling of the limitations. I do think that you could throw them off terribly with unusual math quastions to show that what they learned isn't math, but I'm not the guy to do it; my examples are about chess and programming where I am more qualified to do it. (You could say that my question about the associativity of blending and how caching works sort of shows that it can't use the concept of associativity in novel situations; not sure if this can be called an illustration of its weakness at math)

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8 days ago | parent | next [-]

[deleted]

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calf 8 days ago | parent | prev [-]

But this is parallel to saying LLMs are not "compelled" by the training algorithms to learn symbolic logic.

Which says to me there are two camps on this and the verdict is still out on this and all related questions.

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teleforce 7 days ago | parent [-]

>LLMs are not "compelled" by the training algorithms to learn symbolic logic.

I think "compell" is such a unique human trait that machine will never replicate to the T.

The article did mention specifically about this very issue:

"And of course people can be like that, too - eg much better at the big O notation and complexity analysis in interviews than on the job. But I guarantee you that if you put a gun to their head or offer them a million dollar bonus for getting it right, they will do well enough on the job, too. And with 200 billion thrown at LLM hardware last year, the thing can't complain that it wasn't incentivized to perform."

If it's not already evident that in itself LLM is a limited stochastic AI tool by definition and its distant cousins are the deterministic logic, optimization and constraint programming [1],[2],[3]. Perhaps one of the two breakthroughs that the author was predicting will be in this deterministic domain in order to assist LLM, and it will be the hybrid approach rather than purely LLM.

[1] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:

https://www.youtube.com/live/TknN8fCQvRk

[2] "We Really Don't Know How to Compute!" - Gerald Sussman - MIT (2011) [video]:

https://youtube.com/watch?v=HB5TrK7A4pI

[3] Google OR-Tools:

https://developers.google.com/optimization

[4] MiniZinc:

https://www.minizinc.org/

	▲	calf 7 days ago \| parent [-]
		And yet there are two camps on the matter. Experts like Hinton disagree, others agree.