  Many fields view stochasticity as a way to gain computational efficiency,
while often having to trade off accuracy. In this perspective article, we argue
that stochasticity plays a fundamentally different role in machine learning
(ML) and is likely a critical ingredient of intelligent systems. As we review
the ML literature, we notice that stochasticity features in many ML methods,
affording them robustness, generalizability, and calibration. We also note that
randomness seems to be prominent in biological intelligence, from the spiking
patterns of individual neurons to the complex behavior of animals. We conclude
with a discussion of how we believe stochasticity might shape the future of ML.
