  This paper addresses a fundamental question: how good are our current
self-supervised visual representation learning algorithms relative to humans?
More concretely, how much "human-like" natural visual experience would these
algorithms need in order to reach human-level performance in a complex,
realistic visual object recognition task such as ImageNet? Using a scaling
experiment, here we estimate that the answer is several orders of magnitude
longer than a human lifetime: typically on the order of a million to a billion
years of natural visual experience (depending on the algorithm used). We obtain
even larger estimates for achieving human-level performance in ImageNet-derived
robustness benchmarks. The exact values of these estimates are sensitive to
some underlying assumptions, however even in the most optimistic scenarios they
remain orders of magnitude larger than a human lifetime. We discuss the main
caveats surrounding our estimates and the implications of these surprising
results.
