  We explore the behavior of a standard convolutional neural net in a
continual-learning setting that introduces visual classification tasks
sequentially and requires the net to master new tasks while preserving mastery
of previously learned tasks. This setting corresponds to that which human
learners face as they acquire domain expertise serially, for example, as an
individual studies a textbook. Through simulations involving sequences of ten
related visual tasks, we find reason for optimism that nets will scale well as
they advance from having a single skill to becoming multi-skill domain experts.
We observe two key phenomena. First, \emph{forward facilitation}---the
accelerated learning of task $n+1$ having learned $n$ previous tasks---grows
with $n$. Second, \emph{backward interference}---the forgetting of the $n$
previous tasks when learning task $n+1$---diminishes with $n$. Amplifying
forward facilitation is the goal of research on metalearning, and attenuating
backward interference is the goal of research on catastrophic forgetting. We
find that both of these goals are attained simply through broader exposure to a
domain.
