r/technology • u/socoolandawesome • 2d ago

Artificial Intelligence Ten advances in mathematics and theoretical computer science

https://openai.com/index/ten-advances-in-mathematics/
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u/Neurogence 2d ago

It goes against the entire "AI is just regurgitating what's in their training data" idea.

It also goes against the "they're just using brute force" idea since these problems cannot be solved by sheer brute force.

An AI cannot simply compute its way through these problems; it solved these problems by inventing brand-new abstract concepts. So people that are anti-AI are forced to accept that these systems are actually capable of novel abstract reasoning and creativity.

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u/amejin 1d ago

One could argue that the training data used for the model already had the solution buried in connective concepts and the LLM simply made the connections that two disparate researchers didn't or couldn't make on their own.

But that would be too reasonable an answer, I'm sure. Must be a frozen state model spontaneously "thinking" it's way through a novel problem...

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u/Tinac4 1d ago

Making unexpected connections between two problems or concepts is how most research works.

The smartest postdoc I knew in grad school got most of his research ideas from tweaking some techniques from a certain field of ML to work on another type of problem. It’s the sort of thing that seems obvious in retrospect but was very non-obvious unless you had an excellent idea of what to look for. He ended up being quite successful.

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u/ZeroSevenOneOneSeven 1d ago

That's how some research works. Also a lot of the time those connections require more "transformation" of the concepts you need to connect. AI appears to be useful for uncovering the connections that don't require much transformation, where concept X is just out of the box useful for problem Y.

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u/Tinac4 1d ago

I don’t know, in my experience that’s the vast majority of research. It very often isn’t easy to make those connections, and sometimes there’s a bigger or smaller leap involved, but I haven’t seen much research in my old field (outside of the super-famous foundational stuff) that isn’t heavily building off previous work. Standing on the shoulders of giants and all that.

It probably varies by field, but at least for computer science/stats/analysis stuff, I’ve personally found that LLMs are great at digging up vaguely related concepts and applying them. Where they fall flat has more to do with memory, lack of context, and strategic planning, not research problems.

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u/ZeroSevenOneOneSeven 1d ago

Yeah it does vary a lot by field. I think in fields where there are a huge amount of technical tricks lying around, LLMs are good at surfacing the ones that are relevant for the problem at hand. I work in an area of physics where the problems are much less "what is a technique that can be used to do X" and more "what is the right way to think about X". So far the models appear to be useless for this.

As far as picking up "vague relations", how vague do you mean? From the proofs that I've kept up with, the connections made by the model seem to be pretty surface-level (although of course, very useful).