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- Hacker News
- If you want to peek inside how a model solves a math problem have a look at some data visualizations I made solving basic multiplication.[0]
I wanted to demonstrate capacity (how well it does a thing) instead of capability (which things it does, like drawing a pelican on a bicycle with SVG or solving a Rubik's Cube). To understand how LLMs solve math, look at the simplest case of multiplication. I deconstructed and classified the thinking token output. It is very important that model training yields thinking token output that structurally follows an observe, orient, decide, act (do the multiplication), and observe again loop.
by dataviz1000 - Increasingly, I've begun to think of LLMs as sources of really interesting random objects: large pieces of "reasonable thinking" conditioned on a task. It's not that these are correct, in general, but instead they're a concentrated form of random search where that "randomness" is very likely to follow plausible, human patterns.
You can toss it at a task with a suitable machine for transforming that raw material into action and it'll rattle through and sample "plausible human behavior" at that endpoint.
There are more clever ways to use it, but a general tool here is to upgrade any sort of stochastic search to use this new form of random sampling. It'll be way more efficient, properly conditioned, because it just won't visit implausible things nearly as often as competing random sources.
by tel - Since no one has mentioned it yet - just want to point out that Timothy Gowers is a Fields medalist.by BeetleB
- A thoughtful and measured post, as usual from Gowers. The final note is neat and worth pasting out here in full:
> A good sign that LLMs have reached human level for a much wider class of problems will be if they start proving theorems using methods that, like much of the very best human mathematics, are new and surprising but that with hindsight come to seem beautiful and natural. They should also be methods that are difficult to stumble on by accident. It is hard to say precisely what would count as such a proof, but I think we’ll recognise it when we see it.
by n4r9 - Given coding agent's demonstrated difficulties with concurrent code, even relatively simple concurrent code, it would be interesting to see how they do with temporal logic. I don't know enough to throw AI at the problems in that space but I wonder if they wouldn't crash and burn on it.
(I haven't had the opportunity to throw a current-gen frontier model at a concurrent problem because I haven't had one to try out lately. The best concurrency is no concurrency and the second-best concurrency is the "web request" model where many web requests are nominally running concurrently but they are otherwise fully isolated from each other and not trying to communicate at all. So maybe they're better, but I feel like if they were a lot better somebody would have noted that in a place I'd have seen by now.)
by jerf - For a list of AI accomplishments in mathematics see https://mathoverflow.net/questions/502120/examples-for-the-u... - or a candidate list here: https://aimath.robertj1.com/ . Many have observed an affinity of AI to the search for counterexamples - or examples. Looking at afore lists, something much more sociological crosses my mind: There is a hunt for answering prominent, clearly stated problems. I'm not a mathematician, but is this mostly what progress in mathematics is about? How about stating worthwhile problems in the first place? What about theory building? Am I right saying this is equally important, but none of those utilizing AI for mathematics seem to be interested in such?by steinwinde
- > A good sign that LLMs have reached human level for a much wider class of problems will be if they start proving theorems using methods that, like much of the very best human mathematics, are new and surprising but that with hindsight come to seem beautiful and natural. They should also be methods that are difficult to stumble on by accident. It is hard to say precisely what would count as such a proof, but I think we’ll recognise it when we see it
Agreed. I find that after seeing these results from OpenAI we undeniably have a machine that has:
* General knowledge of nearly every subject humanity has ever learned
* The ability to simulate reasoning (albeit sometimes not very well) with that knowledge
* The ability to reference across the domains of knowledge
To me, this is more or less what I would think "Artificial General Intelligence" is. It's the cumulative knowledge of all general human intelligence, baked into an artificial form, which can then use that knowledge to achieve novel goals.
In many cases of mathematical breakthroughs there is an insight that comes from just happening to know a combination of already existing ideas and then combining them to solve that problem. This is where having that general knowledge seems particularly strong because we can run these machines for weeks on end effectively trying to brute force.
That being said, I could never imagine an LLM in its current form inventing something as elegant as the Fourier transform.
by scronkfinkle - This is really an argument about test-time scaling, even though the post never uses the term.
These days "test-time scaling" mostly means letting the model talk to itself for longer, but the first genuinely surprising results came from plain sampling. Google's AlphaCode generated millions of candidate programs and filtered them down to a handful of submissions, which beat the average human programmer in 2022, before ChatGPT even showed up.
Sampling is what AI is good at. Making examples and doing LeetCode are similar in that verification is clear and cheap. Compared to that, "proof" is still a vague concept, except where Lean works. See the fuss over the ABC conjecture. So humans are still needed.
The interesting question to me is what happens after enough learning from "sampling." Isn't AlphaGo's move 37 an AI's nose? If that happens in mathematics, we may end up with results that are correct, machine checkable, and not explainable in any way we find satisfying.
by h_mirin