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.

翻译：人类认知遵循“全局优先”的认知机制，优先基于粗粒度细节进行信息处理。这种机制内在地具有自适应多粒度描述能力，从而产生高效性、鲁棒性和可解释性等计算特性。现有大多数计算方法过度依赖最细粒度与单粒度分析模式，导致其缺乏高效性、鲁棒性和可解释性，这也是当前神经网络可解释性不足的重要原因。多粒度粒球计算通过使用不同粒度的粒球自适应地表示和覆盖样本空间，并基于这些粒球进行学习。由于粗粒度“粒球”的数量少于样本点，粒球计算更高效。此外，粒球固有的粗粒度特性降低了对细粒度样本扰动的敏感性，从而增强了鲁棒性。粒球的多粒度构造生成了拓扑结构和粗粒度描述，自然提升了可解释性。粒球计算已成功应用于多种人工智能领域，催生了包括粒球分类器、聚类技术、神经网络、粗糙集和进化计算在内的创新理论方法，显著提升了传统方法的高效性、噪声鲁棒性和可解释性。总体而言，粒球计算是人工智能中一种罕见的创新理论方法，能够自适应且同步地增强高效性、鲁棒性和可解释性。本文深入探讨了粒球计算的主要应用场景，旨在为未来研究者提供完善和推广这一潜力理论的参考与启示。