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“整体优先”的认知机制，优先基于粗粒度细节进行信息处理。该机制天然具备自适应多粒度描述能力，从而展现出高效性、鲁棒性和可解释性等计算特性。现有大多数计算方法依赖最细粒度与单粒度的分析模式，导致其效率、鲁棒性和可解释性不足，这亦是当前神经网络缺乏可解释性的重要原因。多粒度粒球计算采用不同尺寸的粒球自适应地表示和包络样本空间，并基于这些粒球进行学习。由于粗粒度“粒球”的数量远少于样本点，粒球计算具有更高的效率。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，从而增强鲁棒性。粒球的多粒度结构形成了拓扑结构与粗粒度描述，自然提升了可解释性。粒球计算已成功应用于人工智能多个领域，推动了包括粒球分类器、聚类技术、神经网络、粗糙集与进化计算在内的创新理论方法的发展，显著改善了传统方法的效率、噪声鲁棒性与可解释性。总体而言，粒球计算是人工智能领域中一种罕见且创新的理论方法，能够自适应地同时提升效率、鲁棒性与可解释性。本文深入探讨了粒球计算的核心应用场景，旨在为未来研究者完善和拓展这一前景广阔的理论提供参考与启示。