Following from a shared interest in livecoding and real-time algorithmic performance, Joana Chicau and Jonathan Reus begin a research project into techniques for in-situ dissections of machine learning algorithms. We seek to better understand the habitual and fixed objects of machine learning as well as their terminologies, and provide counter-techniques for conditions of emergence and movement. In our processual approach, we aim to develop an online repository of terminology and techniques for a critical examination of the “anatomy” of learning and prediction processes, data corpus and models of machine learning algorithms. And explore, through performance practice, how such a toolkit can confront the idealized bodies of artificial intelligence.
This project is produced as a co-production with V2_ Lab for the Unstable Media
About the artists
Joana Chicau [PT/NL] is a graphic designer, coder, researcher — with a background in dance. Her trans-disciplinary research interweaves media design and web environments with performance and choreographic practices. In her practice she researches the intersection of the body with the constructed, designed, programmed environment, aiming at in widening the ways in which digital sciences is presented and made accessible to the public.
Jonathan Reus [US/NL] is a musician and artist who explores expanded forms of music-making and improvisational performance through technological artefacts. His practice is cross-disciplinary and research-based, involving open and iterative processes of collaboration with practitioners from across the arts, sciences and humanities. His work tries to confront and challenge the representational capacities of mathematical-logistical systems, algorithms, and infrastructure through a practice of invasive intuition and trust in the diversity of lived experiences.
Image Credits: Lavinia Xausa