  We identify a new "order parameter" for the disorder driven many-body
localization (MBL) transition by leveraging artificial intelligence. This
allows us to pin down the transition, as the point at which the physics changes
qualitatively, from vastly fewer disorder realizations and in an objective and
cleaner way than is possible with the existing zoo of quantities. Contrary to
previous studies, our method is almost entirely unsupervised. A game theoretic
process between neural networks defines an adversarial setup with conflicting
objectives to identify what characteristic features to base efficient
predictions on. This reduces the numerical effort for mapping out the phase
diagram by a factor of ~100x. This approach of automated discovery is
applicable specifically to poorly understood phase transitions and exemplifies
the potential of machine learning assisted research in physics.
