Life at the edge: accelerators and obstacles to emerging ML-enabled research fields

Abstract

Machine learning is transforming several scientific disciplines, and has resulted in the emergence of new interdisciplinary data-driven research fields. Surveys of such emerging fields often highlight how machine learning can accelerate scientific discovery. However, a less discussed question is how connections between the parent fields develop and how the emerging interdisciplinary research evolves. This study attempts to answer this question by exploring the interactions between machine learning research and traditional scientific disciplines. We examine different examples of such emerging fields, to identify the obstacles and accelerators that influence interdisciplinary collaborations between the parent fields.