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Learning from learning machines: a new generation of AI technology to meet the needs of science

Authors :
Pion-Tonachini, Luca
Bouchard, Kristofer
Martin, Hector Garcia
Peisert, Sean
Holtz, W. Bradley
Aswani, Anil
Dwivedi, Dipankar
Wainwright, Haruko
Pilania, Ghanshyam
Nachman, Benjamin
Marrone, Babetta L.
Falco, Nicola
Prabhat
Arnold, Daniel
Wolf-Yadlin, Alejandro
Powers, Sarah
Climer, Sharlee
Jackson, Quinn
Carlson, Ty
Sohn, Michael
Zwart, Petrus
Kumar, Neeraj
Justice, Amy
Tomlin, Claire
Jacobson, Daniel
Micklem, Gos
Gkoutos, Georgios V.
Bickel, Peter J.
Cazier, Jean-Baptiste
Müller, Juliane
Webb-Robertson, Bobbie-Jo
Stevens, Rick
Anderson, Mark
Kreutz-Delgado, Ken
Mahoney, Michael W.
Brown, James B.
Publication Year :
2021

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2111.13786
Document Type :
Working Paper