  Deep learning is currently being hyped as an almost magical tool for solving
all kinds of difficult problems that computers have not been able to solve in
the past. Particularly in the fields of computer vision and natural language
processing, spectacular results have been achieved. The hype has now
infiltrated several scientific communities. In (nano-)photonics, researchers
are trying to apply deep learning to all kinds of forward and inverse problems.
A particularly challenging problem is for instance the rational design of
nanophotonic materials and devices. In this opinion article, I will first
discuss the public expectations of deep learning and give an overview of the
quite different scales at which actors from industry and research are operating
their deep learning models. I then examine the weaknesses and dangers
associated with deep learning. Finally, I'll discuss the key strengths that
make this new set of statistical methods so attractive, and review a personal
selection of opportunities that shouldn't be missed in the current
developments.
