Hy-Breed: Growing a Responsive Organo-Mechanical Agent

One of the considerations in the use of AI in the last years is its progression towards deeper, more opaque handling of data that are often hidden beneath increasingly smooth and ‘user-friendly’ interfaces. With its increasing use as a tool for automated decision making at the level of the consumer but also in the way sites of intense vulnerability such as detention centres and refugee camps have become sites for experimental testing of these new technologies (Molnar 2020), the pervasive reach of Artificial Intelligence within our socio-political infrastructures are raising our consciousness to the ways machines digitally represent our lives, activities, contours and movements, and the ways these are translated computationally as intent. Information theorist Philip Agre (1994) has considered the way these activities have formed a language in itself through “representation schemes” that use “linguistic metaphors and formal languages for representing... activities”. (Anderson & Pold 2018). Such examples from motion tracking, migration and the collecting of micro-movements of the head, torso, hands etc through VR headsets encourage the inference of limited amounts of movement as intent. These reframe the reverberations of activity as a form of utterance, movement as digital conversations.