An article by Hug March that was recently published at the Journal of Cleaner Production. Find the full article here. "The 21st century has been hailed as the urban century and one in which ICT-led transformations will shape urban responses to global environmental change. The Smart City encapsulates all the desires and prospects on the transformative and disruptive role technology will have in solving urban issues both in Global North and Global South cities. Critical scholarship has pointed out that private capital, with the blessing of technocratic elites, has found a techno-environmental fix to both reshuffle economic growth and prevent other alternative politico-ecological transitions to take root in urban systems. Against this bleak outlook, the paper argues that these technological assemblages might be compatible with alternative post-capitalist urban transformations aligned with Degrowth.
We asked smart city experts the following question: "What do smart cities teach us about sustainability?" Though the experts agreed that smart cities can make urban living more efficient, they also highlighted concerns about various negative consequences associated with smart cities. Chief among these negative consequences are energy consumption required to manage smart city infrastructure, corporate interests having excessive influence, loss of privacy, and a focus on only managing factors that are amenable to computation. More people are moving to urban areas around the globe. The United Nations estimates that by 2050, two thirds of world's total population will be city dwellers. In 1960, just a third of all people lived in cities. Rapid urbanization stresses the Earth system and makes it imperative for policy planners to consider how to make cities sustainable.
AI developments on smart cities, if not critical, risk making a flawed urban model more efficient. Instead, we suggest that AI should challenge the mainstream techno-optimistic approach to solving urban problems by dialoguing with other academic fields, questioning the dominant urban paradigm, and creating transformative solutions. We claim that doing differently, rather than doing better, may be smarter for cities and the common good. This article is part of the special track on AI and Society.
Assuming computational technologies as a dominant factor in forming new scientific methods during the last century, we review the field of computational urban modeling based on the ways different approaches deal with evolving computational and informational capacities. We claim that during the last few years, due to advancements in ubiquitous computing the flow of unstructured data streams have changed the landscape of empirical modeling and simulation. However, there is a conceptual mismatch between the state of the art in urban modeling paradigms and the capacities offered by these urban data streams. We discuss some alternative mathematical methodologies that introduce an abstraction from the traditional urban modeling methodologies.
We now take it for granted that our machines can sense almost any space in the world, from deep sea trenches to the chambers of the human heart. Building on thousands of years of research in physics, war, and natural history, doctors in the 1940s began using ultrasound to scan human and animal bodies. Taking cues from dolphins and bats and Leonardo da Vinci's early echolocation experiments, naval scientists in the early 20th century learned how to detect mines and submarines with sonar. Early cathode ray studies by Wilhelm Röntgen, Nikola Tesla, and Thomas Edison led to the development of x-ray photography, which enabled radiologists to see broken bones, art historians to read the layers of an oil painting, and physicists to study crystalline structures. Revolutions in machine sensing have transformed fields like medicine and engineering and creative production, several times over. Now, finally, these technologies are reaching their apotheosis, converging in -- sound of balloon deflating -- the self-driving car! Sorry if I sound disappointed.