Photography at a Standstill

What has replaced the still photograph are dynamic and distributed image-assemblages that unsettle received notions of space-time — no longer limited to traditional representation and not necessarily even visual. When it comes to computer vision for example, the descriptor photography seems largely redundant (despite deep learning computer vision systems being trained on large datasets of photographic images), and so too the tired metaphor of the eye that once supported its theories and practices. What is at stake here, as ever, is a kind of ‘seeing’ (if we continue to call it that) that makes clear what is visible, sensible and knowable, and crucially also what is not. We might call this seeing algorithmically, or seeing like a dataset, or perhaps even seeing like an infrastructure, comprised of fake images that render fake history. The logic of this invokes the complex notion of ‘image is dialectics at a standstill’, encapsulating a constellation of possible outcomes. To what extent is the radical potential that Benjamin once foresaw in montage applicable to image-based AI given that it seems less an instrument to imagine a qualitatively different future but simply more of the same. What can be seen is not so much representational nor photographic but latent traces of material relations and infrastructures that render historical experience in compromised form.

Part of Special Issue: Seeing Photographically