  Motivated by machine learning, we introduce a novel method for randomly
generating inflationary potentials. Namely, we treat the Taylor coefficients of
the potential as weights in a single-layer neural network and use gradient
ascent to maximize the number of e-folds of inflation. Inflationary potentials
"learned" in this way develop a critical point, which is typically a local
maximum but may also be an inflection point. We study the phenomenology of the
models along the gradient ascent trajectory, finding substantial agreement with
experiment for large-field local maximum models and small-field inflection
point models. For two-field models of inflation, the potential eventually
learns a genuine multi-field model in which the inflaton curves significantly
during the course of its descent.
