  We present an approach to cosmology in which the Universe learns its own
physical laws. It does so by exploring a landscape of possible laws, which we
express as a certain class of matrix models. We discover maps that put each of
these matrix models in correspondence with both a gauge/gravity theory and a
mathematical model of a learning machine, such as a deep recurrent, cyclic
neural network. This establishes a correspondence between each solution of the
physical theory and a run of a neural network. This correspondence is not an
equivalence, partly because gauge theories emerge from $N \rightarrow \infty $
limits of the matrix models, whereas the same limits of the neural networks
used here are not well-defined. We discuss in detail what it means to say that
learning takes place in autodidactic systems, where there is no supervision. We
propose that if the neural network model can be said to learn without
supervision, the same can be said for the corresponding physical theory. We
consider other protocols for autodidactic physical systems, such as
optimization of graph variety, subset-replication using self-attention and
look-ahead, geometrogenesis guided by reinforcement learning, structural
learning using renormalization group techniques, and extensions. These
protocols together provide a number of directions in which to explore the
origin of physical laws based on putting machine learning architectures in
correspondence with physical theories.
