<p>In this article, I discuss the implications of generative AI in future of creative work in the music industries through an ethnographic study of music technologists in Melbourne, Australia. Drawing on visual ethnography methods, I explore how researchers, developers, as well as professional and aspiring artists, across sectors in Melbourne’s ecosystem, value the role of mistakes, glitches and uncertainty in the creative process with music technology. This stands in opposition to the controlled processes within generative AI models, based on the management of uncertainty as an input value. Thus, I reframe the concept of creative uncertainty which emerges as a shaping force in the creative process, and in opposition to the controlled outputs of generative AI. Furthermore, this article shows the limitations and misalignment of dominant visions about the impact of generative AI, on creative workflows and practices, by highlighting the experiences, motivations and expectations of the research participants in this multi-sited ethnography.</p>

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Emerging paradigms in music technology: valuing mistakes, glitches and uncertainty in the age of generative AI and automation

  • Miguel Loor Paredes

摘要

In this article, I discuss the implications of generative AI in future of creative work in the music industries through an ethnographic study of music technologists in Melbourne, Australia. Drawing on visual ethnography methods, I explore how researchers, developers, as well as professional and aspiring artists, across sectors in Melbourne’s ecosystem, value the role of mistakes, glitches and uncertainty in the creative process with music technology. This stands in opposition to the controlled processes within generative AI models, based on the management of uncertainty as an input value. Thus, I reframe the concept of creative uncertainty which emerges as a shaping force in the creative process, and in opposition to the controlled outputs of generative AI. Furthermore, this article shows the limitations and misalignment of dominant visions about the impact of generative AI, on creative workflows and practices, by highlighting the experiences, motivations and expectations of the research participants in this multi-sited ethnography.