Using software he designed, Jason Salavon has
pulled nearly a quarter million images from the ImageNet dataset, a picture
collection used by researchers and corporations to train artificial
intelligence systems. By sampling each of the dataset’s 20,000 categories and
arranging those images from darkest to lightest within each category, Salavon
presents a cascade of photographs suggesting the flood of imagery that
inundates our daily lives. Exposing ImageNet’s odd inclusions and notable
absences in each category, Little Infinity (v.MFAH) poses essential
questions about the nature of images and the ways in which they sway any
understanding of the world.