[beering]

A lot of the space outside of the convex hull is just untried things. You can brute-force trying random things and checking the result and eventually learn something new. With a better heuristic, you can make better guesses and learn new things much more efficiently. There’s no reason to believe that kind of guess-and-check is outside of the reach of LLMs, or that most of our new discoveries are not found the same way.

[llbbdd]

I come back to something like this idea when I consider the distinction being made that LLMs can only combine and interpolate between points in their training material. I could write a brute-force program that just used an English dictionary to produce every possible one-billion-gazillion word permutation of the words within, with no respect for rules of language, and chances are there would be some provable, testable, novel insight somewhere in the results if you had the time to sift through and validate all of it. LLMs seem like a tool that can search that space more effectively than any we've had before.

[autoexec]

If we managed to create very fast monkeys with typewriters and software that can review their output quickly enough that we end up with a result that's worth reading we'd still have people insisting that we've created intelligence. The monkeys however remain monkeys.

[llbbdd]

I think intelligence is an orthogonal, mostly philosophical question aside from whether these tools can produce novel, useful output vs purely recombinant output.

[autoexec]

I think that enough purely recombinant output will eventually produce novel, useful output.

[ykl]

I think of most things you can get to by guess and checking as definitionally inside of the hull; most forms of guess and checking are you take some existing thing, randomize a bunch of its parameters, and see what you get. Whereas with something like relativity, there's not even a starting point that you can randomize and guess/check from the pre-existing knowledge space that will lead you to relativity. That's more like, adding a new dimension to the space entirely.

It's possible LLMs can handle this after all! But at least so far we only have existence proofs of humans doing this, not LLMs yet, and I don't think it's easy to be certain how far away LLMs are from doing this. I should distinguish between LLMS and AI more generally here; I'm skeptical LLMs can do this, I think some other kind of more complete AI almost certainly can.

I supposed you could just, I dunno, randomly combine words into every conceivable sentence possible and treat each new sentence as a theory to somehow test and brute force your way through the infinite possible theories you could come up with. But at that point you're closer to the whole infinite random monkeys producing Shakespeare thing than you are to any useful conclusion about intelligence.

[drdeca]

I think your point about “you could randomly generate a sequence of words, which could in principle produce a text interpretable as expressing any particular expressible-as-a-sequence-of-words novel good idea” pretty much refutes the idea that guessing and checking can only result in things inside such a convex hull, unless said hull already contains everything. Of course, there’s a significant role to play by the “checking” part.

Like, “take a random sequence of bits and interpret it as Unicode” is at one end of a scale, and “take a random sequence of words in a language” is just a tad away from it, and the scale continues in that direction for quite a while.

[ykl]

This assumes that everything outside of the convex hull can already be described using existing language. If you need new language to describe what is outside of the convex hull, is this something an LLM can do?

I actually don't know the answer to that; my understanding is that LLMs by nature of what they are can't understand concepts that are independent of the existing language they are trained on, but I don't have enough in-depth nitty-gritty knowledge of like, core LLM implementation details and architecture and stuff to know if that understanding is correct or not.

[drdeca]

I suppose it is conceivable that there are some useful ideas that cannot be described in terms of language we understand (e.g. if there are ideas that are alien to us and beyond what can be described using https://en.wikipedia.org/wiki/Natural_semantic_metalanguage#... ), but, if there is, I'm not sure those are ideas we can communicate to one-another?

By "If you need new language" do you mean like, coining new words?

I don't see what would prevent them from doing this? LLMs can process text that includes newly coined terms, and respond to that text in ways that use those newly coined words in accordance with the descriptions of the meanings given for those new words in the prompt. They can also make up new words+definitions when asked to do so. Now, whether they can, without being told to do so, recognize that it would be useful to coin a new word for something, and then start using it, I don't know of any instances of this, but based on the previous two things, I don't see a reason to expect this to be fundamentally beyond what they can do?

I don't know what it would mean for a concept to be "independent of the existing language they are trained on". If there are ideas that can't be expressed in terms of the semantic primes all ideas we can express can be expressed in terms of, then I guess such an idea would be independent of our language, but I think that's a much stricter condition than what you mean (and I'm not sure if there even are any good ideas that can't be indirectly expressed in terms of semantic primes -- I kind of suspect not, unless they are like, ideas that are too big to fit in a human mind anyway).

Of course, the outputs these models produce is causally downstream from the data they are trained on, and the distribution they produce over text is largely based on the distribution over text in the training data, but altered in a number of ways (for example, to make them implement the character of the "assistant" persona).

[bsder]

> You can brute-force trying random things and checking the result and eventually learn something new.

And most of the mathematicians seem to welcome this "brute forcing" by the LLMs. It connects pieces that people didn't realize could be connected. That opens up a lot of avenues for further exploration.

Now, if the LLMs could just do something like ingesting the Mochizuki stuff and give us a decent confirmation or disproof ...

[scarmig]

It's also worth noting in that in very high dimension, the convex hull will contain massive volume. It could well be the case that humans established that convex hull millions of years ago, and all of our inventions and innovations sense have fallen inside it.

[davebren]

> There’s no reason to believe that kind of guess-and-check is outside of the reach of LLMs

This doesn't make any sense, by their nature they can't "guess-and-check" things outside their training set.