CLASP
The Centre for Linguistic Theory and Studies in Probability

New Models for Art: Machine Epistemologies

Abstract: This presentation will address AI image analysis and generation in relation to the study of art and visual culture. Techniques in computer vision and machine learning today mirror or relate to traditional formalist methods in the discipline of art history. I will discuss how the categorization of artworks in large datasets affect the interpretation of images. The release of text-to-image models has made this issue even more pressing, as those same terms that once were used to categorize images, such as style, are now used to generate images that imitate different artists or artistic styles. Popular AI text-to-image creation tools like DALL-E invite everyone to try their hand at creating a work of fine art similar to the most famous works from art history. AI systems like this are of pressing interest to humanists interested in how such tools shape culture and cultural practice. For both old and new formalisms, issues of categorization are of paramount importance. The distinctiveness that categories, such as style, imply can only be identified by comparison to other distinctive manners of making, meaning that is a highly relative. It is therefore an unstable and slippery foundation upon which to peg mathematical ‘certainty’ in datasets.