  Machine learning has achieved remarkable success in many applications.
However, existing studies are largely based on the closed-world assumption,
which assumes that the environment is stationary, and the model is fixed once
deployed. In many real-world applications, this fundamental and rather naive
assumption may not hold because an open environment is complex, dynamic, and
full of unknowns. In such cases, rejecting unknowns, discovering novelties, and
then incrementally learning them, could enable models to be safe and evolve
continually as biological systems do. This paper provides a holistic view of
open-world machine learning by investigating unknown rejection, novel class
discovery, and class-incremental learning in a unified paradigm. The
challenges, principles, and limitations of current methodologies are discussed
in detail. Finally, we discuss several potential directions for future
research. This paper aims to provide a comprehensive introduction to the
emerging open-world machine learning paradigm, to help researchers build more
powerful AI systems in their respective fields, and to promote the development
of artificial general intelligence.
