  We provide a brief, and inevitably incomplete overview of the use of Machine
Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology.
Astronomy entered the big data era with the first digital sky surveys in the
early 1990s and the resulting Terascale data sets, which required automating of
many data processing and analysis tasks, for example the star-galaxy
separation, with billions of feature vectors in hundreds of dimensions. The
exponential data growth continued, with the rise of synoptic sky surveys and
the Time Domain Astronomy, with the resulting Petascale data streams and the
need for a real-time processing, classification, and decision making. A broad
variety of classification and clustering methods have been applied for these
tasks, and this remains a very active area of research. Over the past decade we
have seen an exponential growth of the astronomical literature involving a
variety of ML/AI applications of an ever increasing complexity and
sophistication. ML and AI are now a standard part of the astronomical toolkit.
As the data complexity continues to increase, we anticipate further advances
leading towards a collaborative human-AI discovery.
