Machine Learning and the Mediating Tendencies of the Image

Images exist at the interstices between human perceptual experience and its technological mediation. This is especially relevant as the development and implementation of technologies offers new possibilities to produce visualisations from data. In so doing, technological mediation tangibly augments relations between how images are produced, experienced, and interpreted.

The present incorporation of machine learning into various forms of visual media offers insight into this issue by enabling images to be produced as the result of the statistical analysis of datasets. Computational relations which are extracted and inferred between features within images help to construct learned representations which are in turn used to generate images. This results in a form of computationally determined representation which is informed by the interpretive processes performed by machines.

Existing notions of technically-produced images often prove inadequate for the description of the visual artefacts of machine learning, leaning heavily on historical narratives regarding the technical production of images and even perpetuating inaccuracies. These tend to misconstrue images either as accurate reflections of reality or as the product of artificial perception and genius by virtue of their engagement with technological processes.

Through a media archaeological approach, this paper addresses how current notions of image production remain tied to historical ideas which anthropomorphise and which overestimate the role played by machines, while minimising the role played by humans therein. It seeks to clarify the mediating role played by visual technologies and to demonstrate how images produced using machine learning offer new ways of approaching theories of the image.