Computation has now been reconfigured by machine learning: those technical processes and operations that yoke together statistics and computer science to create artificial intelligence (AI) by furnishing vast datasets to learn tasks and predict outcomes. In DeepAesthetics, Anna Munster examines the range of more-than-human experiences this transformation has engendered and considers how those experiences can be qualitative as well as quantitative. Drawing on process philosophy, Munster approaches computational experience through its relations and operations. She combines deep learning—the subfield of machine learning that uses neural network architectures—and aesthetics to offer a way to understand the insensible and frequently imperceptible forms of nonlinear and continuously modulating statistical function. Attending to the domains and operations of image production, statistical racialization, AI conversational agents, and critical AI art, Munster analyzes how machine learning is operationally entangled with racialized, neurotypical, and cognitivist modes of producing knowledge and experience. She approaches machine learning as events through which a different sensibility registers, one in which AI is populated by oddness, disjunctions, and surprises, and where artful engagement with machine learning fosters indeterminate futures.

Anna Munster is Professor in the School of Art and Design at the University of New South Wales and author of An Aesthesia of Networks: Conjunctive Experience in Art and Technology and Materializing New Media: Embodiment in Information Aesthetics.

Introduction: Deep Machines and Surfaces of Experience
1. Heteropoietic Computation: Category Mistakes and Fails as Generators of Novel Sensibilities
2. The Color of Statistics: Race as Statistical (In)visuality
3. Could AI Become Neurodivergent?
4. Machines Unlearning: Toward an Allagmatic Arts of AI
Postscript. On Models of Control and (Their) Modulation
Acknowledgments
Notes
References
Index