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Human cognition operates on a "Global-first" cognitive mechanism,prioritizing information processing based on coarse-grained details. Thismechanism inherently possesses an adaptive multi-granularity descriptioncapacity, resulting in computational traits such as efficiency, robustness, andinterpretability. The analysis pattern reliance on the finest granularity andsingle-granularity makes most existing computational methods less efficient,robust, and interpretable, which is an important reason for the current lack ofinterpretability in neural networks. Multi-granularity granular-ball computingemploys granular-balls of varying sizes to daptively represent and envelop thesample space, facilitating learning based on these granular-balls. Given thatthe number of coarse-grained "granular-balls" is fewer than sample points,granular-ball computing proves more efficient. Moreover, the inherentcoarse-grained nature of granular-balls reduces susceptibility to fine-grainedsample disturbances, enhancing robustness. The multi-granularity construct ofgranular-balls generates topological structures and coarse-graineddescriptions, naturally augmenting interpretability. Granular-ball computinghas successfully ventured into diverse AI domains, fostering the development ofinnovative theoretical methods, including granular-ball classifiers, clusteringtechniques, neural networks, rough sets, and evolutionary computing. This hasnotably ameliorated the efficiency, noise robustness, and interpretability oftraditional methods. Overall, granular-ball computing is a rare and innovativetheoretical approach in AI that can adaptively and simultaneously enhanceefficiency, robustness, and interpretability. This article delves into the mainapplication landscapes for granular-ball computing, aiming to equip futureresearchers with references and insights to refine and expand this promisingtheory.

Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method