Description
Chair: Kyunghyun Cho
Democratization of AI/ML in astronomy has been fostered by increased awareness, powerful software tools, and improving education. Yet as diverse AI/ML methods begin to be infused into workflows and inference chains it is legitimate to ask how AI/ML has fundamentally and uniquely contributed to novel science. I address this question in the context of AI as an assistive tool in three contexts: 1) to leapfrog people-centric bottlenecks, 2) as a model-based computational accelerant, and 3) as a hypothesis generation engine. One recent effort of ours surfaces insights of large language models (LLMs) with a focus on user experience (UX). Another demonstrates an unexpected fundamental breakthrough in our understanding of the theory of microlensing via simulation-based inference.
