Over the past several years, two trends in machine learning have converged to pique the curiosity of artists working with 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 oeuvre of Shakespeare, Picasso, or just a gigantic database of digitized cats.
This talk will review these developments through the lens of creative, exploratory research. Artistic metaphor helps clarify that which is otherwise shrouded by layers of academic jargon, making these highly specialized subjects more accessible. A selection of experimental projects at the intersection of AI and new media art will be shown, including several real-time interactive demos. It will conclude with a survey of interdisciplinary tools and learning resources for artists and data scientists alike, offering an accessible introduction to this field.