In Nonhuman Photography, Joanna Zylinska discusses a “nonhuman photography”; the idea that photography always includes a non-human aspect, separate from human vision and agency. I remember creating my own pose-recognition project using Javascript and the ml5.js library, working in concert to form my own interpretation of the bodies in the world. Called PoseNet, this model was originally ported to TensorFlow.js and them ml5.js. The pose library detects the position of certain features human body (knee, shoulder, etc.). Working through my project, I had to negotiate with how the algorithm could see the world. I relied upon the computer’s understanding of where these body parts were, determinations occurring beyond my capabilities (at least, to estimate where parts of the body are in real-time).
In this view, the non-human in photography shows us the world occurring at different scales of time and space, traces of our earthly context beyond the scope of our view. In this sense, Zylinska argues that “photography based on algorithms, computers, and networks merely intensifies this condition, while also opening up some new questions and new possibilities.” This is true. While we create machines (for producing visual understandings of the world) with increasing complexity, that separation between human and creation of image is more visible. Still, that non-human component was always there, serving as mediator between the world and the human eye.
What else is possible, though, with the increasing complexity of our visual machinery? One possibility is humility, or what Donna Haraway calls a “wound” that decenters human ways of knowing. Complexity, and the relative ungraspability of algorithmic ways of seeing force us to appreciate those other scales of time and space, our smallness in the context of the forces of the environment.