  Machine learning and artificial intelligence (ML/AI) are rapidly becoming an
indispensable part of physics research, with domain applications ranging from
theory and materials prediction to high-throughput data analysis. In parallel,
the recent successes in applying ML/AI methods for autonomous systems from
robotics through self-driving cars to organic and inorganic synthesis are
generating enthusiasm for the potential of these techniques to enable automated
and autonomous experiment (AE) in imaging. Here, we aim to analyze the major
pathways towards AE in imaging methods with sequential image formation
mechanisms, focusing on scanning probe microscopy (SPM) and (scanning)
transmission electron microscopy ((S)TEM). We argue that automated experiments
should necessarily be discussed in a broader context of the general domain
knowledge that both informs the experiment and is increased as the result of
the experiment. As such, this analysis should explore the human and ML/AI roles
prior to and during the experiment, and consider the latencies, biases, and
knowledge priors of the decision-making process. Similarly, such discussion
should include the limitations of the existing imaging systems, including
intrinsic latencies, non-idealities and drifts comprising both correctable and
stochastic components. We further pose that the role of the AE in microscopy is
not the exclusion of human operators (as is the case for autonomous driving),
but rather automation of routine operations such as microscope tuning, etc.,
prior to the experiment, and conversion of low latency decision making
processes on the time scale spanning from image acquisition to human-level
high-order experiment planning.
