ABSTRACT
Postmodern discussions of the body in performance-related studies have usually been 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.
