A collaboration with Joana Chicau, following from a shared interest in livecoding and real-time algorithmic performance.
We research and develop performative techniques for in-situ dissections of machine learning algorithms. 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 research project began in late 2018, and is produced as a co-production with V2_ Lab for the Unstable Media
