  The mechanism by which thermodynamics sets the direction of time's arrow has
long fascinated scientists. Here, we show that a machine learning algorithm can
learn to discern the direction of time's arrow when provided with a system's
microscopic trajectory as input. The performance of our algorithm matches
fundamental bounds predicted by nonequilibrium statistical mechanics.
Examination of the algorithm's decision-making process reveals that it
discovers the underlying thermodynamic mechanism and the relevant physical
observables. Our results indicate that machine learning techniques can be used
to study systems out of equilibrium, and ultimately to uncover physical
principles.
