Art in the Age of Machine Learning

Length: 214 pages
Edition: 1
Language: English
Publisher: The MIT Press
Publication Date: 2021-11-23
ISBN-10: 0262046180
ISBN-13: 9780262046183
Sales Rank: #731427 (See Top 100 Books)

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.

Cover
Series Page
Title Page
Copyright
Dedication
Table of Contents
List of Figures
Series Foreword
Foreword
Acknowledgments
1. Introduction
    Myths and Misconceptions
    Understanding Machine Learning Art
    Why Machines Should Learn
    Supervised, Unsupervised, and Reinforcement Learning
    Components of a Machine Learning System
    From Cybernetics to Deep Learning
    A Shift in Paradigm
    Chapter Breakdown
I: Training
    2. Optimizing Art
        Art, Purpose, Teleology
        The Best Art
        Computational Creativity
        The Imitation Game
        Learning in Real Time
        Conclusion
    3. Curbing the Training Curve
        Emergence versus Authorship
        Subjective Functions
        Interactive Genetic Algorithms
        Artificial Curiosity
        Chasing Agents
        Shake, Rattle, and Roll
        Conclusion
    4. Aesthetics of Adaptive Behaviors
        Aesthetics of Behavior
        Degrees of Behavior
        Behavior Morphologies
        Adaptive Couplings
        Conclusion
II: Models
    5. Beyond Human Understanding
        The Body Electric
        Black Boxing
        Getting to Know
        The Best Audience
        Baking Models
        A Menagerie of Models
        Conclusion
    6. Evolutionary Learning
        Parametric Systems
        Nonparametric Systems
        Genetic Programming
        Ecosystems
        Conclusion
    7. Shallow Learning
        Neural Networks
        Early Connectionism
        Connectionist Rennaissance
        Music and Connectionism
        Connectionism Meets Artificial Life
        Connectionist Visions
        Emergent Representations
        Context Machines
        Conclusion
    8. Deep Learning
        From Connectionism to Deep Learning
        Corporate Dreams
        Neural Aesthetics
        GAN Art
        Latent Space
        Re-articulating the Latent Space
        Neural Glitches
        Recurrent Writing
        Conclusion
III: Data
    9. Data as Code
        Programming by Example
        Interactive Machine Learning
        Knowing and Listening
        Sympoietic Drawing
        Bring Your Own Data
        Viral Collections
        Crowdsourcing the Everyday
        Found Data
        Not the Only One
        Conclusion
    10. Deep Remixes
        Remix Culture
        Open-Source Cultures
        Machine Learning Remixes
        Exploring Pretrained Models
        Alternative Faces
        Remixing the Generative
        An AI Opera
        Conclusion
    11. Watching and Dreaming
        Inductive Biases
        Technocultural Jamming
        Beyond Human Writing
        Learning and Generating
        Invisible Images
        Exploring the Collective Imaginary
        Conclusion
    12. Conclusion
        Zooming Out
        Plugging the Gap
        Beyond Metacreation
        Human-Machine Relationships
        Taming the Unknowable
        Paradigm Shift in the Art World
        Final Thoughts
Glossary
Bibliography
Name Index
Subject Index