Over the past several years, two trends in machine learning have converged to pique the curiosity of artists working in code: the proliferation of powerful open source deep learning frameworks like TensorFlow and Torch, and the emergence of data-intensive generative models for hallucinating images, sounds, and text as though they came from the oeuvres of Shakespeare and Picasso. This talk will review these developments, and present a survey of artworks and interactive demos made by the speaker and others active in this area over the past few years, along with an introduction to a set of interdisciplinary tools and learning resources for artists and data scientists alike, if ever there was a difference to begin with.
Gene Kogan (Twitter (https://twitter.com/genekogan), Website (https://genekogan.com)) 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.