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Full papers / Towards a Theory of Machine Learning and the Cinematic Image
- Abstract: This paper addresses state-of-the art and prehistory of machine learning technologies as they pertain to the moving image. It discusses the challenges and potential hazards of these technologies, before showing how artists and image-makers are using these techniques to create a new relationship to the space of the cinematic in the age of big data. This paper strives to interrogate and overcome technologically deterministic accounts of this new technology. Accordingly, it situates examples within the discursive fields from which they emerge and favors diachrony over synchrony in outlining four clusters of media-archaeological inquiry that intersect in the application of machine learning techniques to the moving image. I believe that this approach is a necessary intervention into the hegemonic discourse on machine learning that has emerged from the technology sector which obscured deep ideological problems within the algorithms themselves, and also overlooks the potential of these technologies.
- Biography: Owen Lyons is an Assistant Professor at Ryerson (X) University, Toronto.
His work addresses media archaeology, documentary, digital media, and critical theory and he holds a PhD from the Institute of Comparative Studies in Literature, Art, and Culture at Carleton University as well as an MA in Film and Media Studies from the University of Amsterdam. His forthcoming book with Amsterdam University Press examines representations of finance during the Weimar Republic and the emergence of the idea of the world economy. His current research projects include Liquid Screens: The Media Landscape of Financial Markets, which addresses the visual culture of 21st-century financial markets and their reflection in digital media, as well as an examination of the application of artificial intelligence to cinema entitled Machine Learning and the Moving Image.
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