Dan Moore’s practice examines authorship in an era of machine intelligence. Working across AI-driven systems, robotics, and traditional media, he transforms generative output into carefully composed works that challenge the boundaries between signal and noise, original and derivative.

His work begins with fragments produced by computational systems: unstable images, partial forms, and emergent patterns that carry the traces of their underlying datasets and processes. Rather than treating these outputs as finished images, Moore approaches them as raw material. Through processes of selection, translation, and refinement, he stabilizes these fragments, moving them from ephemeral digital states into physical and time-based forms.

Across painting, textile, and installation, Moore reconstitutes machine-generated artifacts into enduring objects. In doing so, he collapses distinctions between tool and collaborator, positioning generative systems not as instruments of control but as active participants in the production of form. Authorship becomes distributed, unfolding between human intention, algorithmic structure, and material constraint.

His practice foregrounds instability as both an aesthetic and conceptual condition. Images are never fixed; they drift, recombine, and resolve into new configurations over time. What might traditionally be dismissed as error or noise becomes central, revealing the underlying logic and bias of the systems that produce it.

By translating generative processes into material form, Moore situates machine output within a broader art historical lineage, linking contemporary AI systems to earlier traditions of abstraction, chance, and procedural art. His work ultimately asks how meaning is constructed when images are no longer authored in isolation, but emerge through continuous negotiation between human and machine.