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This chapter examines the alignment between next-generation AI (AI 2.0AI 2.0) technology and the requirements of urban environments, with a focus on the application of AI 2.0 across ten key urban functions: nature, governanceGovernance, housing, mobility, commerce, healthcare, education, industry, infrastructure, and innovation. By developing a matrix model that maps technological capabilities to urban needs, this chapter proposes a suite of AI-driven urban solutions designed to establish a robust feedback loop between technology and demand. The distinguishing characteristics of AI 2.0—such as big data-driven learning, swarm intelligence, cross-media intelligence, hybrid augmented intelligence, and intelligent unmanned systems—have significant potential in terms of enhancing various aspects of smart cities. Specific applications encompass optimizing energy consumption through advanced data analytics, optimizing resource allocation via swarm intelligence, assessing urban conditions using cross-media intelligence, improving human–machine collaboration efficiency through hybrid intelligence, and advancing urban automation and convenience through intelligent unmanned systems, all of which contribute to fostering sustainable urban development.

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Matching Between New Generation AI Technologies and Urban Demands

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Siegfried Zhiqiang WU

摘要

This chapter examines the alignment between next-generation AI (AI 2.0AI 2.0) technology and the requirements of urban environments, with a focus on the application of AI 2.0 across ten key urban functions: nature, governanceGovernance, housing, mobility, commerce, healthcare, education, industry, infrastructure, and innovation. By developing a matrix model that maps technological capabilities to urban needs, this chapter proposes a suite of AI-driven urban solutions designed to establish a robust feedback loop between technology and demand. The distinguishing characteristics of AI 2.0—such as big data-driven learning, swarm intelligence, cross-media intelligence, hybrid augmented intelligence, and intelligent unmanned systems—have significant potential in terms of enhancing various aspects of smart cities. Specific applications encompass optimizing energy consumption through advanced data analytics, optimizing resource allocation via swarm intelligence, assessing urban conditions using cross-media intelligence, improving human–machine collaboration efficiency through hybrid intelligence, and advancing urban automation and convenience through intelligent unmanned systems, all of which contribute to fostering sustainable urban development.