Still searching for an (un)stable equilibrium: visualising the process of training generative neural networks without data
Abstract
(un)stable equilibrium is an ongoing series of works that is based on a practice of training generative neural networks without data. This paper introduces the second series of (un)stable equilibrium works, in which the process of training without data is visualised in a series of video pieces. These works show a generative network attempting to converge to a fixed point that is undefined, caught in an endless, unresolvable search for equilibrium. The video pieces described in this paper guide the viewer toward an understanding of AI through aesthetic experience rather than technical exposition, and strive to give a conceptual understanding of what AI could be, rather than a restatement of what it currently is. This project sits within a broader set of artistic practices that serve as an alternative and critical modes of explainability for AI.
