**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.
