Artist and programmer Gene Kogan discusses how artists and musicians are using deep learning for creative experimentation.
Over the last two years, deep learning has made inroads into domains of interest to artists, designers, musicians, and the like.
Combined with the appearance of powerful open source frameworks and the proliferation of public educational resources, this once esoteric subject has become accessible to far more people, facilitating numerous innovative hacks and art works. The result has been a virtuous circle, wherein public art works help motivate further scientific inquiry, in turn inspiring ever more creative experimentation.
This Whitehead Lecture Series talk will review some of the works that have been produced, present educational materials for how to get started, and speculate on research trends and future prospects.
Gene Kogan is an artist and a programmer who is interested in generative systems, artificial intelligence, and software for creativity and self-expression. He is a collaborator within numerous open-source software projects, and leads workshops and demonstrations on topics at the intersection of code, art, and technology activism.
Gene initiated and contributes to ml4a, a free book about machine learning for artists, activists, and citizen scientists. He regularly publishes video lectures, writings, and tutorials to facilitate a greater public understanding of the topic.
http://www.genekogan.com / ml4a.github.io / @genekogan
Whitehead Lecture Series
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