  Dualities are widely used in quantum field theories and string theory to
obtain correlation functions at high accuracy. Here we present examples where
dual data representations are useful in supervised classification, linking
machine learning and typical tasks in theoretical physics. We then discuss how
such beneficial representations can be enforced in the latent dimension of
neural networks. We find that additional contributions to the loss based on
feature separation, feature matching with respect to desired representations,
and a good performance on a `simple' correlation function can lead to known and
unknown dual representations. This is the first proof of concept that computers
can find dualities. We discuss how our examples, based on discrete Fourier
transformation and Ising models, connect to other dualities in theoretical
physics, for instance Seiberg duality.
