  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.
