The remarkable performance of modern AI systems has been driven by unprecedented scales of data, computation, and energy -- far exceeding the resources required by human intelligence. This disparity highlights the need for new guiding principles and motivates drawing inspiration from the fundamental organizational principles of brain computation. Among these principles, modularity has been shown to be critical for supporting the efficient learning and strong generalization abilities consistently exhibited by humans. Furthermore, modularity aligns well with the No Free Lunch Theorem, which highlights the need for problem-specific inductive biases and motivates architectures composed of specialized components that solve subproblems. However, despite its fundamental role in natural intelligence and its demonstrated benefits across a range of seemingly disparate AI subfields, modularity remains relatively underappreciated in mainstream AI research. In this work, we review several research threads in artificial intelligence and neuroscience through a conceptual framework that highlights the central role of modularity in supporting both artificial and natural intelligence. In particular, we examine what computational advantages modularity provides, how it has emerged as a solution across several AI research areas, which modularity principles the brain exploits, and how modularity can help bridge the gap between natural and artificial intelligence.

翻译：现代人工智能系统的卓越性能是由前所未有的数据规模、计算量和能耗所驱动的——这些资源远超人类智能所需。这种差异凸显了对新指导原则的需求，并促使我们从大脑计算的基本组织原则中汲取灵感。在这些原则中，模块化已被证明对于支持人类持续表现出的高效学习能力和强大泛化能力至关重要。此外，模块化与“没有免费午餐定理”高度契合，该定理强调了问题特定归纳偏置的必要性，并激励构建由解决子问题的专用组件组成的架构。然而，尽管模块化在自然智能中起着基础性作用，并且在一系列看似不同的人工智能子领域中已证明其益处，它在主流人工智能研究中仍未得到充分重视。在本工作中，我们通过一个概念框架回顾了人工智能和神经科学中的若干研究方向，该框架强调了模块化在支持人工与自然智能中的核心作用。具体而言，我们探讨了模块化提供了哪些计算优势、它如何在多个人工智能研究领域作为解决方案出现、大脑利用了哪些模块化原则，以及模块化如何有助于弥合自然与人工智能之间的差距。