Machine Learning Art & Creativity Talk

In this talk, I will argue that teaching the machine how to look at art is not only essential for advancing artificial intelligence, but also has the potential to help address the division between the arts and sciences. I will present results of recent research activities at the Art and Artificial Intelligence Laboratory at Rutgers University. We investigate perceptual and cognitive tasks related to human creativity in visual art. In particular, we study problems related to art styles, influence, and the quantification of creativity. We develop computational models that aim at providing answers to questions about what characterizes the sequence and evolution of changes in style over time.  The talk will cover advances in automated prediction of style, how that relates to art history methodology, and what that tells us about how the machine sees art history.  The talk will also delve into our recent research on quantifying creativity in art in regard to its novelty and influence, as well as computational models that simulate the art-producing system.