Machine Learning Aesthetics and Applications

3 credits

This course will look to early cybernetics as well as contemporary artists to explore the ontological, cultural, and practical implications of artworks produced with machine learning. Creative applications will include building and tagging datasets, training generative image networks (GANs), language models (GPT-3), custom deepfakes, and more. Readings will provide historical and technical context while helping students question if contemporary AI systems are tools, collaborators, or creative agents in their own right. Topics will include AI embodiment, the ethics of “thinking” machines, glitch in the age of machine learning, AI copyright law, and data as an aesthetic force. We will use ML as a chance to hybridize biology and technology, considering the applications of genetic algorithms, notions of evolved aesthetics, and the simulation of neurons as a basis for art making. Class time will be used for discussion, technical demos, and group exercises applying ML tools to concept art, brand design, creative writing, music making, and more. While we will take a broad view of ML’s place in culture, our focus will be on practical applications for artists today. No coding experience is required for this course.