  Learning is a process which can update decision rules, based on past
experience, such that future performance improves. Traditionally, machine
learning is often evaluated under the assumption that the future will be
identical to the past in distribution or change adversarially. But these
assumptions can be either too optimistic or pessimistic for many problems in
the real world. Real world scenarios evolve over multiple spatiotemporal scales
with partially predictable dynamics. Here we reformulate the learning problem
to one that centers around this idea of dynamic futures that are partially
learnable. We conjecture that certain sequences of tasks are not
retrospectively learnable (in which the data distribution is fixed), but are
prospectively learnable (in which distributions may be dynamic), suggesting
that prospective learning is more difficult in kind than retrospective
learning. We argue that prospective learning more accurately characterizes many
real world problems that (1) currently stymie existing artificial intelligence
solutions and/or (2) lack adequate explanations for how natural intelligences
solve them. Thus, studying prospective learning will lead to deeper insights
and solutions to currently vexing challenges in both natural and artificial
intelligences.
