Machine Learning and Remix: Self-Training Selectivity in Digital Practice
My Essay “Machine Learning and Remix: Self-Training Selectivity in Digital Practice” has been published in the anthology Studies in the Arts, edited by and . The essay was initially written in 2019, and I am very happy to finally see the publication released. I thank the editors and peer reviewers for their generous feedback during the long peer review process. You can find a direct link to my essay as part of the publication’s website.
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 reflected upon as the paradigm of metacreativity continues to be established.
