Read more
"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"--
List of contents
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
About the author
Sofian Audry
Summary
An examination of machine learning art and its practice in new media art and music.
Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. 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.
