On the altered states of machine vision: Trevor Paglen, Hito Steyerl, Grégory Chatonsky

The landscape of contemporary visual culture and contemporary artistic practices is currently undergoing profound transformations caused by the application of technologies of machine learning to the vast domain of networked digital images. The impact of such technologies is so profound that it leads us to raise the very question of what we mean by “vision” and “image” in the age of artificial intelligence. This paper will focus on the work of three artists – Trevor Paglen, Hito Steyerl, Grégory Chatonsky – who have recently employed technologies of machine learning in non-standard ways. Rather than using them to train systems of machine vision with their different operations (face and emotion recognition, object and movement detection, etc.) and their different fields of application (surveillance, policing, process control, driverless vehicle guidance, etc.), they have used them in order to produce entirely new images, never seen before, that they present as altered states of the machine itself