Comment by somenameforme
11 hours ago
Language (human and computer alike) is excessively redundant. Read any sort of chain of logic or debate from somebody and you could sum it up, quite accurately in about 5 words. The rest is either fluff or supporting statements that should flow naturally and logically from the initial premise. My own post here is a perfect example. Everything I said after the first few words is little more than dumping directly connected statements.
Train on a massive body of text, figure out what correlates with what, and next thing you know you have a rather impressive facade of logic that can even connect things in novel ways where a connection is clearly called for, but not yet made. I call it a facade because LLMs will be able to advance knowledge significantly in finding these clear connections, but they exist only because no human can hold more than a tiny percent of all knowledge in their own mind.
Where I expect they will run into issues is in finding the unclear connections - like going from an existence where math doesn't exist, to one where somebody 'invented', or more aptly - discovered, math. That's inventing something from nothing, rather than just logically connecting pieces. I don't see how this is possible with a token prediction algorithm.
Anyhow, the point I'm making is that language itself includes encoded logic. And so LLMs working as token prediction algorithms are able to exploit this functionality to produce statements that offer a facsimile of logical reasoning under a constrained domain.
I am no expert, just curious:
What is it that makes something truly novel or creates something from nothing?
When we do it, do we apply existing concepts, combine them with a general intuition for how physics work in the real world, and use that to form a hypothesis that we then test in experiments?
Again I think the example of math is good. Many isolated tribes still don't even have numbers. They simply refer to things in broad quantifiers like - none, one, few, some, many. And that's perfectly fine for their needs! Many of the problems that you need math to solve - or that lead naturally to math, like currency, only exist once you've already discovered mathematics.
So try putting yourself in this ancient mindset before mathematics. How did somebody invent it, come up with the concept of numbering everything, further develop the various 'tricks' for manipulating these numbers, and so on? In terms of raw 'complexity' it's far less impressive than the latest LLM models solving some obscure mathematics problem that almost nobody understands.
But in terms 'intelligence', I find it vastly more impressive - because it's again this sort of difficult to describe concept of going from nothing to something. There is no logical baseline that naturally and cleanly leads to math. Almost like a child would say when asked how they learned something, 'Oh I just thought it up.' Except in this case, somebody genuinely did!
If we trained a LLM on such texts that only use "none, one, few, some, many" in their language, wouldn't it likely learn representations of individual quantities and arithmetics anyway?
Provided the training data was extensive enough and training rewarded solving problems that require mathematics.
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