This PhD thesis addresses how current notions of image production remain tied to historical ideas which often prove inadequate for the description of visual artefacts of machine learning (ML). ML refers to the notion of simulating the process of information acquisition computationally, and when applied to the generation of images, it enables visual content to be influenced based on the statistical analysis of data. The increasing use of ML in image production highlights several aspects which have been present in older forms of media, but which now take on new forms and relevance, especially within artistic contexts. This research seeks to clarify the mediating role played by visual technologies and to demonstrate how images produced using ML offer new ways of approaching theories of the image.
Images exist at the interstices between human perceptual experience and its technological mediation, which 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 relationships between how images are produced, experienced and interpreted. The present incorporation of ML 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 new images. This results in a form of computationally-determined representation which is informed by the interpretive processes performed by machines.
Artists have taken great interest in the potential of ML, in an aesthetic, but also a processual capacity, often considering its relation to human vision. Their productions offer insight into novel aspects of ML in the creation of images through experimental practice which is informed by theory and by art history. Using and reflecting on ML, often in novel or reactionary ways, artistic and humanistic perspectives provide vital counter-narratives to those of computer science (CS), and which facilitate cross-disciplinary understanding.
Click here to download the full text: Machine Learning and Notions of the Image
Research supported by an internal PhD fellowship from the IT-University of Copenhagen, 2017–2020.
Practical projects that contributed to the development of the thesis:
Aesthetics of Uncertainty
Proceedings of the 7th Conference on Computation, Communication, Aesthetics and X, 256–62, 2019.
Uncertainties in the Algorithmic Image
Journal of Science, Technology and the Arts, 11 no. 2 (May 2019): 36–40.
Operative Image: Automation and Autonomy
Machine Feeling, APRJA, 2019
Seeing with Machines: Decipherability and Obfuscation in Adversarial Images
ISEA 2018 Proceedings of the 24th International Symposium on Electronic Art, Durban, 2018, 321–24.
