My research into the technical, socio-political and environmental aspects of artificial intelligence is based on data materialism: paying attention to materialist concerns for ways in which technology automates performance and creativity, while historicizing and contextualizing methods for measurement through which data is obtained. Data materialism is also a way to refuse a ‘data idealism’. I understand the ‘proto’ in protohistory as a gesture of making a precedent in the way AI is studied. It suggests observing that which precedes AI, but also challenges causalities that can be established between automation and creativity.
I propose a protohistory of AI inspired by generative genealogy as a materialist geophilosophical practice that is critical and creative. The generative view enfolds, on the one hand, the concern for machine-based automated creation (as in generative art) while, on the other, offering a genealogical approach to material history of AI: one which traces nonlinear generation of digital artificial intelligence techniques of automation and mechanization, and provides a window into the surprising futures, to take up the words of Iris van der Tuin and Rossi Braidotti. Protohistory of AI will not aspire to an indefinite linear teleology, or a metahistorical locus of its ‘origin’, but instead proceed by a methodology of jumping generations and challenging of the linear cutting up of time into successive fragments, inspired by feminist new materialism and an interdisciplinary approach to digital humanities.
Kargın, F., King, D., & Savić, S. (Eds.) (2024). Teaching Artistic Strategies: Playing with Materiality, Aesthetics and Ambiguity. (Image; Vol. 248). transcript.