Manipulations

2020 - present

This work explores the hypothesis that AI neural networks see differently than humans, creating images embedded with patterns invisible to human perception. Through individual digital manipulation that coaxes machine-visible structures into human-readable form, the work reveals what artificial vision perceives but humans cannot directly see. The resulting images function as archaeological evidence of non-human intelligence, exposing the systematic differences between human and artificial pattern recognition.

Individual digital manipulation becomes an interpretive process between computational and human visual systems. Each intervention reveals hidden patterns, exposing latent information embedded within machine-generated imagery — structural relationships that algorithms recognize but human perception cannot directly access. Artificial intelligence constructs coherent visual worlds according to its own logical framework, one that requires deliberate intervention to become legible within human perceptual boundaries.

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