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The article envisions a future by 2050 where AI assistants are in every home, education is personalized, medical treatments are advanced, cities are smart, and human-AI collaboration is widespread.
Shenzhen Traffic Police are using drones to enforce traffic rules, suggesting such technology may become widespread in major cities worldwide.
This paper presents an AI-driven framework for energy-efficient environmental monitoring in smart cities using edge intelligence and TinyML, which dynamically activates sensors based on spatiotemporal conditions to reduce energy consumption and extend sensor lifespan.
A robot autonomously parked and docked a Citi Bike in NYC, showcasing AI's ability to interact with the physical world.
This paper investigates using large vision-language models for built environment reasoning tasks, such as design suggestions and risk identification, leveraging remote sensing imagery. It evaluates models like InternVL and Qwen, highlighting their potential for supporting smart city decision-making and quantitative reasoning.
This research introduces the Housing Potential Common Data Model (HPCDM) to integrate diverse datasets for housing analysis and demonstrates its application through a City Digital Twin pilot.