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Comment by preston4tw | Hacker News Reader

This is one thing I've been wondering about AI: will its broad training enable it to uncover previously covered connections between areas the way multi-disciplinary people tend to, or will it still miss them because it's still limited to its training corpus and can't really infer.

If it ends up being more the case that AI can help us discover new stuff, that's very optimistic.

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In some sense, AI should be the most capable at doing this within math. Literally the entire domain in its entirety can be tokenized. There are no experiments required to verify anything, just theorem-lemma-proof ad nauseam.

Doing this like in this test, it's very tricky to rule out the hypothesis that the AI is just combining statements from the Discussion / Future Outlook sections of some previous work in the field.

Math seems to me like the hardest thing for LLMs to do. It requires going deep with high IQ symbol manipulation. The case for LLMs is currently where new discoveries can be made from interpolation or perhaps extrapolation between existing data points in a broad corpus which is challenging for humans to absorb.

Alternatively, human brains are just terrible at "high IQ symbol manipulation" and that's a much easier cognitive task to automate than, say, "surviving as a stray cat".

This line of reasoning implies "the stochastical parrot people are right, there is no intelligence in AI". Which is the opposite of what AI thought leaders are saying.

I think this may be the first time I've seen "thought leaders" used unironically. Is there any reason to believe they're right?

What makes you think it was used unironically? :)

I reject the Stochastic Parrot theory. The claim is more about comparative advantage; AI systems already exist that are superhuman on breadth of knowledge at undergrad understanding depth. So new science should be discoverable in fields where human knowledge breadth is the limiting factor.

> AI systems already exist that are superhuman on breadth of knowledge at undergrad understanding depth

Two problems with this:

1. AI systems hallucinate stuff. If it comes up with some statement, how will you know that it did not just hallucinate it?

2. Human researchers don't work just on their own knowledge, they can use a wide range of search engines. Do we have any examples of AI systems like these that produce results that a third-year grad student couldn't do with Google Scholar and similar instructions? Tests like in TFA should always be compared to that as a baseline.

> new science should be discoverable in fields where human knowledge breadth is the limiting factor

What are these fields? Can you give one example? And what do you mean by "new science"?

The way I see it, at best the AI could come up with a hypothesis that human researchers could subsequently test. Again, you risk that the hypothesis is hallucination and you waste a lot of time and money. And again, researchers can google shit and put facts together from different fields than their own. Why would the AI be able to find stuff the researchers can't find?

If they solve tokenization, you'll be SHOCKED at how much it was holding back model capabilities. There's tons of works at NeurIPS about various tokenizer hacks or alternatives to bpe which massively improve various types of math that models are bad at (i.e. arithmatic performance)

This is kinda getting at a core question of epistemology. I’ve been working on an epistemological engine by which LLMs would interact with a large knowledge graph and be able to identify “gaps” or infer new discoveries. Crucial to this workflow is a method for feedback of real world data. The engine could produce endless hypotheses but they’re just noise without some real world validation metric.

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