The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems. It is then tempting to imagine the path towards developing an autonomous AI scientist also responsible for designing experiments and hypothesis generation. Interestingly, this framing brings a new perspective on the role of causality and agency that incorporates recent advances in machine learning and artificial intelligence.
The moderated post-talk conversation will take place one week later on Wednesday, March 16 at 4 p.m. Dr. Zenna Tavares, Associate Research Scientist at the Zuckerman Institute and Data Science Institute of Columbia University, will be the conversant.
Register in advance for this webinar: