In a non-generative approach to artificial intelligence in an artistic practice, this work looks at mapping processing data from artificial neural networks (NNs) onto sound and visuals. One aim of this practice-based piece of research is to paradoxically offer insights into how these ubiquitous, yet notoriously opaque algorithms operate, by exposing the audience to the intrinsic unintelligibility of their processes. The other is to use these vast amounts of abstract data as a creative starting point for audiovisual artworks, referring to aesthetic traditions that have emerged from the need to make use and potentially make sense of such extensive masses of information, and from ones that have developed sounds that have gradually become associated with digital and post-digital worlds and other exterior and abstract concepts. At the heart of the whole work is a link and cross-fertilisation between the use of sounds and visuals aesthetically associated with errors and digital malfunction and the use of actual ‘waste’ data (from NN training), which acts as a trace of their operation.
