X-rays are a well-established tool to help analyze and restore valuable paintings because the rays' higher frequency means they pass right through paintings without harming them. X-ray imaging can reveal anything that has been painted over a canvas or where the artist may have altered his (or her) original vision. But the technique has its limitations, and that's where machine learning can prove useful. Two papers this fall illustrated the use of AI to solve specific problems in art analysis and conservation: one to reconstruct an underpainting in greater detail, and the other to make it easier to image two-sided painted panels. Picasso's The Old Guitarist is one of the best-known works from the artist's so-called "Blue Period."

Neural style transfer was developed in 2015 by Leon Gatys and colleagues at the University of Tubingen in Germany. It comes about from a fascinating insight into the way neural networks learn to recognize images of different kinds. Neural networks consist of layers that analyze an image at different scales. The first layer might recognize broad features like edges, the next layer sees how these edges form simple shapes like circles, the next layer recognizes patterns of shapes, such as two circles close together, and yet another layer might label these pairs of circles as eyes. This kind of network would be able to recognize eyes in paintings in a wide variety of styles, from Leonardo da Vinci to Van Gogh to Picasso.

Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.