Speaker
Description
New techniques in AI are rapidly being developed, extended and applied to challenging problems in biology. At the same time, as new assays, new data efforts, and greater understanding is developed in biology, the class and scope of problems that are amendable to AI approaches is growing. In order to survey the current frontier of the interface between AI methodology and biology and to chart future directions and challenges, we held an “NSF-NIH Joint Workshop on Emerging AI in Biology” in June 2023 that gathered approximately 40 experts on the intersection of research in AI and biology. I will present some of the insights and discussion from this workshop. Topics include challenges related to biological applications in the following areas: federated learning; privacy, security and fairness; transfer learning; automated science and active learning; explainability and causality; and scalability.
