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

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. Generalization versus extrapolationAchieving scientific understanding from data-driven AI models embedded in a scientific workflow requires access to data and data representations in order to discover patterns-not just in the data, but ultimately in the world. This consists of at least two components:1. The capacity to learn models from data that lead to testable hypotheses-potentially about data that are very different than the original data used to train the models.

Learning continuous models for continuous physics

“…where F is a function that describes the temporal dynamics. Learning an approximation F for the underlying continuous function F is critical for many science and engineering problems [26].…”