Art in the Age of Machine Learning

Go inside the artistic movement that draws on machine learning as both inspiration—and medium—for creating new media art and music.

In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. 

Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.1 Introduction
I TRAINING
2 Optimizing Art
3 Curbing the Training Curve
4 Aesthetics of Adaptive Behaviors
II MODELS
5 Beyond Human Understanding
6 Evolutionary Learning
7 Shallow Learning
8 Deep Learning
III DATA
9 Data as Code
10 Deep Remises
11 Watching and Dreaming
12 Conclusion
Glossary 
Notes
Bibliography
Name Index
Subject Index“Audry puts machine learning in historical context and provocatively argues for its unique artistic potential. This book is tremendously useful to scholars and artists alike as a source of theoretical footholds and methodological guidance.”
—Allison Parrish, Assistant Arts Professor, NYU ITP/IMA
 
“Sofian Audry powerfully demonstrates that artists’ imagination, labor, and acute social-political awareness is alive and well in our age of machine learning.”
—Chris Salter, Professor of Computation Arts, Concordia University, Montreal; author of Entangled
 
“The capacity to learn, adapt, even innovate or ‘create’ has become a deep issue in machine learning art. Audry plumbs the theoretical and ethical dimensions of these matters in this deep dive into behavior, adaptivity, and metamorphosis in computational systems.”
—Simon Penny, Professor, University of California, Irvine; author of Making SenseSofian Audry is an artist, scholar, and Professor of Interactive Media within the School of Media at Université du Québec à Montréal.