  Rapid advances in artificial intelligence (AI) in the last decade have
largely been built upon the wide applications of deep learning (DL). However,
the high carbon footprint yielded by larger and larger DL networks becomes a
concern for sustainability. Furthermore, DL decision mechanism is somewhat
obsecure and can only be verified by test data. Green learning (GL) has been
proposed as an alternative paradigm to address these concerns. GL is
characterized by low carbon footprints, small model sizes, low computational
complexity, and logical transparency. It offers energy-effective solutions in
cloud centers as well as mobile/edge devices. GL also provides a clear and
logical decision-making process to gain people's trust. Several statistical
tools have been developed to achieve this goal in recent years. They include
subspace approximation, unsupervised and supervised representation learning,
supervised discriminant feature selection, and feature space partitioning. We
have seen a few successful GL examples with performance comparable with
state-of-the-art DL solutions. This paper offers an introduction to GL, its
demonstrated applications, and future outlook.
