Learning from learning machines: a new generation of AI technology to meet the needs of science
L. Pion-Tonachini
K. Bouchard
Héctor García Martín
S. Peisert
W. B. Holtz
A. Aswani
D. Dwivedi
H. Wainwright
G. Pilania
Benjamin Nachman
B. Marrone
N. Falco
P. Prabhat
Daniel B. Arnold
Alejandro Wolf-Yadlin
Sarah Powers
S. Climer
Q. Jackson
Ty Carlson
M. Sohn
P. Zwart
Neeraj Kumar
Amy Justice
Claire Tomlin
Daniel A. Jacobson
G. Micklem
Georgios Gkoutos
Peter J. Bickel
J. Cazier
Juliane Müller
B. Webb-Robertson
Rick L. Stevens
Mark Anderson
Ken Kreutz-Delgado
Michael W. Mahoney
James B. Brown

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.
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