Application-Driven Innovation in Machine Learning
David Rolnick Alan Aspuru-Guzik Sara Beery Bistra Dilkina Priya L. Donti Marzyeh Ghassemi Hannah Kerner Claire Monteleoni Esther Rolf Milind Tambe Adam White
As applications of machine learning proliferate, innovative algorithms
inspired by specific real-world challenges have become increasingly important.
Such work offers the potential for significant impact not merely in domains of
application but also in machine learning itself. In this paper, we describe the
paradigm of application-driven research in machine learning, contrasting it
with the more standard paradigm of methods-driven research. We illustrate the
benefits of application-driven machine learning and how this approach can
productively synergize with methods-driven work. Despite these benefits, we
find that reviewing, hiring, and teaching practices in machine learning often
hold back application-driven innovation. We outline how these processes may be
improved.