Today was day two of a meeting on generative AI in physics, hosted by MIT. My favorite talks today were by Song Han (MIT) and Thea Aarestad (ETH), both of whom are working on making ML systems run ultra-fast on extremely limited hardware. Themes were: Work at low precision. Even 4-bit number representations! Radical. And bandwidth is way more expensive than compute: Never move data, latents, or weights to new hardware; work as locally as you can. They both showed amazing performance on terrible, tiny hardware. In addition, Han makes really cute 3d-printed devices! A conversation at the end that didn't quite happen is about how Aarestad's work might benefit from equivariant methods: Her application area is triggers in the CMS device at the LHC; her symmetry group is the Lorentz group (and permutations and etc). The day started with me on a panel in which my co-panelists said absolutely unhhinged things about the future of physics and artificial intelligence. I learned that many people think we are only years away from having independently operating, fully functional aritificial physicists that are more capable than we are.
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