The tools of artificial intelligence — neural networks in particular — have been good to physicists. For years, this technology has helped researchers reconstruct particle trajectories in accelerator experiments, search for evidence of new particles, and detect gravitational waves and exoplanets. While AI tools can clearly do a lot for physicists, the question now, according to Max Tegmark, a physicist at the Massachusetts Institute of Technology, is: “Can we give anything back?”
Tegmark believes that his physicist peers can make significant contributions to the science of AI, and he has made this his top research priority. One way physicists could help advance AI technology, he said, would be to replace the “black box” algorithms of neural networks, whose workings are largely inscrutable, with well-understood equations of physical processes.
The idea is not brand-new. Generative AI models based on diffusion — the process that, for instance, causes milk poured into a cup of coffee to spread uniformly — first emerged in 2015, and the quality of the images they generate has improved significantly since then. That technology powers popular image-producing software such as DALL·E 2 and Midjourney. Now, Tegmark and his colleagues are learning whether other physics-inspired generative models might work as well as diffusion-based models, or even better.