  Physical neural networks (PNNs) are a class of neural-like networks that
leverage the properties of physical systems to perform computation. While PNNs
are so far a niche research area with small-scale laboratory demonstrations,
they are arguably one of the most underappreciated important opportunities in
modern AI. Could we train AI models 1000x larger than current ones? Could we do
this and also have them perform inference locally and privately on edge
devices, such as smartphones or sensors? Research over the past few years has
shown that the answer to all these questions is likely "yes, with enough
research": PNNs could one day radically change what is possible and practical
for AI systems. To do this will however require rethinking both how AI models
work, and how they are trained - primarily by considering the problems through
the constraints of the underlying hardware physics. To train PNNs at large
scale, many methods including backpropagation-based and backpropagation-free
approaches are now being explored. These methods have various trade-offs, and
so far no method has been shown to scale to the same scale and performance as
the backpropagation algorithm widely used in deep learning today. However, this
is rapidly changing, and a diverse ecosystem of training techniques provides
clues for how PNNs may one day be utilized to create both more efficient
realizations of current-scale AI models, and to enable unprecedented-scale
models.
