  Learning is traditionally studied in biological or computational systems. The
power of learning frameworks in solving hard inverse-problems provides an
appealing case for the development of `physical learning' in which physical
systems adopt desirable properties on their own without computational design.
It was recently realized that large classes of physical systems can physically
learn through local learning rules, autonomously adapting their parameters in
response to observed examples of use. We review recent work in the emerging
field of physical learning, describing theoretical and experimental advances in
areas ranging from molecular self-assembly to flow networks and mechanical
materials. Physical learning machines provide multiple practical advantages
over computer designed ones, in particular by not requiring an accurate model
of the system, and their ability to autonomously adapt to changing needs over
time. As theoretical constructs, physical learning machines afford a novel
perspective on how physical constraints modify abstract learning theory.
