Machine Learning and Remix: Self-training Selectivity in Digital Art Practice

Abstract

In this essay, I focus on the emerging role of machine learning as an integral part of the elements of selectivity and remix in art and music. I first discuss how selectivity forms part of communication, to then consider its increasing importance in creativity. I then evaluate how machine learning is implemented by artists for the production of works in ways that revisit questions of authorship as an individual and collective practice in terms of metacreativity – a delegation of workmanship from humans to automation. In closing, emerging artificial intelligence’s agency is ref lected upon as the paradigm of metacreativity continues to be established.