Machine Dreaming

Slowness is intentionally built into the interaction. Subtle movements cause the work to produce more vivid transformations, deeper sound, and increasingly intricate generative visuals. This creates a responsive, embodied feedback loop where presence, intention and generative AI co-determine the overall aesthetic. As viewers slow their movements, the work unfolds fully, the live video image progressively transforms into the AI’s interpretation constrained by prompts (i.e. botanical forms, spatial depth, and aesthetic detail) and training data. In the final stage of the work, botanical forms appear to grow from the viewer’s form, beginning to animate and extend further into the surrounding space. In this process, participants encounter themselves as fluid, data-driven forms, refracted through the training sets of the model. The work invites viewers to experience being both subject and co-creator within a relational perceptual system, prioritising embodied meaning-making through gesture and interaction over cognitive explanation.

Machine Dreaming highlights the conditions of being ‘seen’ by AI. The work prompts viewers to reflect on their relationship to this technology and how perception, agency, and representation are negotiated with these systems.