Vision
We work with small datasets and energy-conscious systems. Our installations are designed to operate with minimal computational overhead, offering an alternative to resource-intensive generative pipelines and the increasingly uniform aesthetics that accompany them
We develop unique visual and sonic languages that retain character across media, resisting homogenising aesthetics of popular AI generators.
Our approach treats data as an active medium. We construct narratives through data and with data, allowing it to be continuously reworked, reintroduced, and reinterpreted across the system rather than fixed as a finite resource.
Outputs of the fine-tuned AI environment initially clustered based on similarity
(t-SNE grouping).
Outputs of the experimental AI environment Polymorph first dispersed through the standard t-SNE similarity-based grouping, then re-clustered through flocking behaviour, resisting the narratives of data finitude.