Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, Holtz, W. Bradley, Anil J. Aswani, Dipankar Dwivedi, Haruko Murakami Wainwright, Ghanshyam Pilania, Benjamin Philip Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf‐Yadlin, Sarah Powers, Sharlee Climer, Quinn Tyler Jackson, Ty Carlson, Michael B. Sohn · arXiv (Cornell University) · 2021
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.