The current societal challenges exceed the capacity of human individual or collective effort alone. As AI evolves, its role within human collectives is poised to vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, when synergized, can achieve a level of collective intelligence that surpasses the collective capabilities of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This narrative review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. The interplay among these agents shapes the overall structure and dynamics of the system. We explore how agents' diversity and interactions influence the system's collective intelligence. Furthermore, we present an analysis of real-world instances of AI-enhanced collective intelligence. We conclude by addressing the potential challenges in AI-enhanced collective intelligence and offer perspectives on future developments in this field.

翻译：当前的社会挑战已超出人类个体或集体努力所能独立应对的范畴。随着人工智能的发展，其在人类集体中的角色预计将从辅助工具演变为参与成员。人类与人工智能具备互补的能力，当二者协同作用时，能够实现一种超越单独人类或人工智能集体能力的集体智能水平。然而，人机系统中的交互本质上是复杂的，涉及错综复杂的流程与相互依赖关系。本文通过叙事性综述，融合复杂网络科学的视角，提出了一个包含认知层、物理层与信息层的多层人机集体智能表征框架。在此多层网络中，人类与智能体表现出不同的特征：人类在从表层到深层的属性上具有多样性差异，而智能体则在功能性与拟人化程度上存在差异。这些智能体间的相互作用塑造了系统的整体结构与动态特性。我们探讨了智能体的多样性与交互如何影响系统的集体智能。此外，我们对现实世界中人工智能增强集体智能的实例进行了分析。最后，我们阐述了人工智能增强集体智能可能面临的挑战，并对该领域的未来发展提出了展望。