Descendent: AI and the Body Beyond Hybridization

Research output: Chapter in book/report/conference proceedingChapterpeer-review

Abstract

Postmodern discussions of the body in performance-related studies have been usually framed through Simondonian perspectives, often stemming from partial readings of Deleuze’s oeuvre. In this chapter, an initial selective research genealogy serves to both expose and challenge the pervasiveness of the Simondonian model, pointing to the current disintegration of its fragilely contingent body. The Descendent project, which employs machine-learning to deploy a real-time two-way interactive synthesis mechanism translating sound into 3D dance motion, works as a case study to explore and contest through a non-anthropomorphic perspective the linked concept of body hybridization —bodies as organic-mechanical assemblages. Such an approach aims to transcend discussions of subject- and self-hood and Cartesian dualisms (e.g., mind/body, subject/object) via the concepts of unqualified matter, virtuality, and our reading of Karen Barad’s take on Quantum physics. It also addresses AI’s acceleration to a non-reversible terminus of such processes of increasingly complex bodily contingency, framed here as a transition from hybrid to vapor bodies. Through a practice-oriented approach, we show how Descendent may articulate such a vapor state through specific tactics of embodiment, a new choreographic milieu, a new creative praxeology of data↔somatic metaphors, and the resultant understandings of performance and performativity in AI-mediated regimes. This section dovetails into Peter Nelson’s chapter, which introduces an illuminating discussion of the algorithmic interpolation and AI-inference processes involved in Descendent’s motion capture and visualization pipelines.
Original languageEnglish
Title of host publicationChoreomata: Performance and Performativity After AI
EditorsRoberto Alonso Trillo, Marek Poliks
Place of PublicationLondon; New York
PublisherChapman and Hall/CRC
Chapter18
Pages435-466
Number of pages32
Edition1st
ISBN (Electronic)9781003312338
ISBN (Print)9781032319919, 9781032319988
DOIs
Publication statusPublished - 20 Dec 2023

Scopus Subject Areas

  • Music
  • Artificial Intelligence
  • Visual Arts and Performing Arts

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