In this talk, I will argue that data science and computation have the potential to help address the division between the arts and sciences.
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 aim to 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. The talk will cover advances in the domain of automated prediction of style, genre, and the identification of artists. The talk will also delve into our recent research on quantifying creativity in art in regard to its novelty and influence. We also try to answer questions about what characterizes the sequence and evolution of changes in style over time and which factors drive changes in art styles over time. Art historians have come up with different theories and methodologies to answer these questions over the last century. However, without science, it is difficult to validate these conjectures, and they remain as conjectures. With the availability of vast, digitized art collections, we are now well positioned to study these conjectures and, hopefully, validate or nullify them through computational methods. Along these lines, I will present recent results of our computational models that aim to simulate the art-producing system over time in search for answers for the aforementioned questions.