  We pose that microscopy offers an ideal real-world experimental environment
for the development and deployment of active Bayesian and reinforcement
learning methods. Indeed, the tremendous progress achieved by machine learning
(ML) and artificial intelligence over the last decade has been largely achieved
via the utilization of static data sets, from the paradigmatic MNIST to the
bespoke corpora of text and image data used to train large models such as GPT3,
DALLE and others. However, it is now recognized that continuous, minute
improvements to state-of-the-art do not necessarily translate to advances in
real-world applications. We argue that a promising pathway for the development
of ML methods is via the route of domain-specific deployable algorithms in
areas such as electron and scanning probe microscopy and chemical imaging. This
will benefit both fundamental physical studies and serve as a test bed for more
complex autonomous systems such as robotics and manufacturing. Favorable
environment characteristics of scanning and electron microscopy include low
risk, extensive availability of domain-specific priors and rewards, relatively
small effects of exogeneous variables, and often the presence of both upstream
first principles as well as downstream learnable physical models for both
statics and dynamics. Recent developments in programmable interfaces, edge
computing, and access to APIs facilitating microscope control, all render the
deployment of ML codes on operational microscopes straightforward. We discuss
these considerations and hope that these arguments will lead to creating a
novel set of development targets for the ML community by accelerating both
real-world ML applications and scientific progress.
