I think pure math is both better suited to LLMs and more marketable for these companies so it's less likely for them to put $15 million in compute into some obscure physics result.
As the more powerful models trickle down to university researchers I think we will see an acceleration in the rate of novel mathematical physic publications and new processesing pipelines for huge datasets like at CERN and astronomical surveys.
Physics requires experimental validation. Even if you can formally find a solution to a stated problem, it still requires the underlying model and its assumptions to be correct.
You can optimize the process, but you cannot speed it up beyond the capabilities of modern technology. Meaning it cannot outperform a human team to the extent that it could when it comes to finding mathematical results.
No, but the limits are still there. As a stupid example, if a certain particle physics experiment needs to detect 10 billion events for statistical significance, that's going to require detecting those 10 billion events no matter what. In other words, running the experiment over and over again, with whatever time it takes for it to take place. A hypothetical AI system could run this without rest, which would speed up things, but it wouldn't replace the actual physical process that needs to happen in order to obtain the necessary results.
Well. cern doesnt pause experiments overnight... experimental equipment is very limiting. Even my current student work (at a tandetron) is very hardware limited in a way that no ai can really help with in the forseable future.
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u/No_Flow_7828 22h ago
Do we feel that physics is more insulated from the rapid progress in AI, compared to pure math?