Seeing Like a Dataset: Notes on AI Photography

T 5,000 images for a dataset, cropping those images, creating variations (reversing, rotating, etc.), and training for a few thousand epochs to create even more extensions of those 500 to 5,000 images. In turn, these GANs will make a study of the pixel arrangements of those natural patterns, assign them coordinates and weights, and then reconstruct these clusters and patterns into new, unseen compositions. The aesthetic value of this output as photography is questionable. The output of these GANs is to produce endless variations of the dataset—in other words, to produce even more of these 5,000 images. The practice is instead a form of artistic research, a way of bringing the affordances of GANs into embodiment through my motions: scanning the landscapes, moving my body toward whatever dataset I am trying to construct. It is the act of constructing this dataset through a blend of artistic eye and mechanical detachment that forms the true body of the work. As a result of this practice, my vision as a photographer has shifted. The rules of photographic composition are pointless to an AI eye. Just as the camera and norms of photography The camera began to train photographers in 1816. Photography developed a set of rules, and a photographer may follow those rules when scouring the landscape for images, or else work with the camera to produce new ways of recording the world. Through repetition, the practices become instincts or habits. The camera, as a tool to capture what we see, changes how we see. As the philosopher Vilém Flusser writes, “Photographers have power over those who look at their photographs, they program their actions; and the camera has power over the photographers, it programs their acts” [1]. As an AI artist working on training generative adversarial networks (GAN) on my own datasets, I often take photographs of natural patterns found on walks along beaches or forest trails. These are for building a dataset of that outing, training a model, and then generating an extended, simulated wandering. I take a few hundred of these photographs at a time, the minimum required to train a GAN, or rather to extend the training of StyleGAN2 to produce images based on my own photographs. GAN photography is the practice of going into the world with a camera, collecting 500 to Seeing Like a Dataset Notes on AI Photography