Deep learning the city: the spatial imaginaries of AI

McKim, Joel (2022) Deep learning the city: the spatial imaginaries of AI. In: Rose, G. (ed.) Seeing the City Digitally: Processing Urban Space and Time. Cities and Culture. Amsterdam: Amsterdam University Press, pp. 35-56. ISBN 9789463727037.

Abstract

This chapter examines how deep learning neural networks and computer vision technologies are impacting the design, organization and occupation of cities. It begins by providing a brief history of the development of “deep learning” approaches to artificial intelligence. The chapter then focuses on the ways artists and designers have begun to engage with deep learning and computer vision in order to highlight critical questions, especially about the ethical issues surrounding the training datasets these systems depend on. The chapter discusses three art and design examples that shift focus specifically towards the city and spatial concerns, considering the ways these works explore machine learning (the opportunities it presents and the problems it raises) within a specifically architectural or urban context. Book synopsis: This book explores what’s happening to ways of seeing urban spaces in the contemporary moment, when so many of the technologies through which cities are visualised are digital. Cities have always been pictured, in many media and for many different purposes. This edited collection explores how that picturing is changing in an era of digital visual culture. Analogue visual technologies like film cameras were understood as creating some sort of a trace of the real city. Digital visual technologies, in contrast, harvest and process digital data to create images that are constantly refreshed, modified and circulated. Each of the chapters in this volume examines a different example of how this processual visuality is reconfiguring the spatial and temporal organisation of urban life.

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