Generative Analogue
Generative Analogue Network is a practice-based research project that uses visual methods to explore the hidden processes that underpin machine vision and AI image generation systems. It demystifies the opaque technical procedures of machine-learning models by translating them into intuitable material forms, enabling a critical exploration of algorithmic perception and creativity. The research process departs from the idea, proposed by Adrian MacKenzie and Anna Munster, that algorithmic perception is “diagrammatic” – that computational systems …
