  Human cognition operates on a "Global-first" cognitive mechanism,
prioritizing information processing based on coarse-grained details. This
mechanism inherently possesses an adaptive multi-granularity description
capacity, resulting in computational traits such as efficiency, robustness, and
interpretability. The analysis pattern reliance on the finest granularity and
single-granularity makes most existing computational methods less efficient,
robust, and interpretable, which is an important reason for the current lack of
interpretability in neural networks. Multi-granularity granular-ball computing
employs granular-balls of varying sizes to daptively represent and envelop the
sample space, facilitating learning based on these granular-balls. Given that
the number of coarse-grained "granular-balls" is fewer than sample points,
granular-ball computing proves more efficient. Moreover, the inherent
coarse-grained nature of granular-balls reduces susceptibility to fine-grained
sample disturbances, enhancing robustness. The multi-granularity construct of
granular-balls generates topological structures and coarse-grained
descriptions, naturally augmenting interpretability. Granular-ball computing
has successfully ventured into diverse AI domains, fostering the development of
innovative theoretical methods, including granular-ball classifiers, clustering
techniques, neural networks, rough sets, and evolutionary computing. This has
notably ameliorated the efficiency, noise robustness, and interpretability of
traditional methods. Overall, granular-ball computing is a rare and innovative
theoretical approach in AI that can adaptively and simultaneously enhance
efficiency, robustness, and interpretability. This article delves into the main
application landscapes for granular-ball computing, aiming to equip future
researchers with references and insights to refine and expand this promising
theory.
