Seventeenth International Conference on the Arts in Society

Abstract

Advancements in machine learning, as they relate to the human body, are structurally limited to processing two-dimensional images and, in many cases, limited to the visible edge. When bodies are computationally visualized, constructed, and characterized based on the limitations of the photographic representation, the internal condition of bodies becomes mutated beyond the structure of earlier philosophical considerations. The unconscious internal body, as it relates to computation, can be traced both conceptually and materially from as early as the 17th century to more contemporary uses of machine learning such as Convolutional Neural Networks (CNNs) and Generative Adversarial Network (GANs). The fundamental shift in epistemological literature and material fabrication can be found in the move away from the structure of the duality of embodiment and towards computational methodologies that favor disembodied thought. The reality of the unconscious body is eclipsed by the hyperreal digital body, thus shifting systems of identification. These shifts have serious implications as machine learning and computational thinking dominate social structures, medical research, surveillance, and policy in contemporary society. The inner body as it is recognized and unrecognized in computational representations poses questions beyond the role of phenomenological disembodiment and into questions of self-identification and actualization when mechanical means of representation dominate the visualization of the internal condition.