  Strongly correlated phases of matter are often described in terms of
straightforward electronic patterns. This has so far been the basis for
studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show
that artificial intelligence (AI) can provide an unbiased alternative to this
paradigm for phases with subtle, or even unknown, patterns. Long- and
short-range spin correlations spontaneously emerge in filters of a
convolutional neural network trained on snapshots of single atomic species. In
the less well-understood strange metallic phase of the model, we find that a
more complex network trained on snapshots of local moments produces an
effective order parameter for the non-Fermi-liquid behavior. Our technique can
be employed to characterize correlations unique to other phases with no obvious
order parameters or signatures in projective measurements, and has implications
for science discovery through AI beyond strongly correlated systems.
