Generative AI in urban science and practice

IF 15.3
Yunke Zhang, Fengli Xu, Qi R. Wang, Esteban Moro, Yang Yue, Huandong Wang, Bin Chen, Yu Liu, Dongping Fang, Peng Gong, Luís M. A. Bettencourt, Michael Batty, Yong Li
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Abstract

Cities are changing faster than the tools used to understand them. A new generation of AI can now synthesize images, model aspects of human behavior and reason across heterogeneous data—capabilities that are beginning to reshape how researchers and practitioners observe, model and support decisions about urban life. Here, in this Review, we explore what these tools can genuinely offer, where the evidence is strong and where it remains thin. The answer depends less on technical novelty than on whether generative AI can be evaluated honestly, validated in context and deployed in ways that keep human judgment and accountability at the center. Understanding cities has never been more important. This Review considers the capabilities of generative AI, synthesizing insights into the importance of honestly evaluating AI, validating it in context and deploying it to center human judgment and accountability.
城市科学与实践中的生成式人工智能
城市的变化速度比用来了解城市的工具要快。新一代人工智能现在可以合成图像,模拟人类行为的各个方面,并在异构数据中进行推理——这些能力开始重塑研究人员和实践者观察、建模和支持城市生活决策的方式。在这篇综述中,我们探讨了这些工具真正能提供什么,哪些证据有力,哪些证据薄弱。答案与其说取决于技术的新颖性,不如说是取决于生成式人工智能是否能够被诚实地评估、在环境中得到验证,并以保持人类判断和问责制为中心的方式进行部署。了解城市从未像现在这样重要。这篇综述考虑了生成式人工智能的能力,综合了对诚实评估人工智能的重要性的见解,在上下文中验证人工智能,并将其部署到人类判断和问责制的中心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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