Artist Registry
The White Columns Curated Artist Registry is an online platform for emerging and under-recognized artists to share images and information about their respective practices. The Registry seeks to create a context for artists who have yet to benefit from wider critical, curatorial or commercial support. To be eligible, artists cannot be affiliated with a commercial gallery in New York City.
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STATEMENT OF WORK
My art explores the urgent yet timeless qualities of human and AI visual perception. The urgency is due to AI’s pervasiveness, rapid technological evolution, and potential for real-world consequences. The timelessness is due to its evolutionary entwinement with human visual perception — both synthetic and biological neural networks share common mechanisms and emergent properties.
Despite AI’s new prevalence, its inner workings are hidden, due both to technical complexity and corporate policy. In contrast to the recent trend of generative AI, I extract and expose synthetic neurons from landmark public AI models. My art visually and materially reveals AI’s digital interior states, positioned in the context of human perception and humans’ evolutionary relationship with nature. Using paint, photography, computer code, digital manipulations and installations, I reveal the archetypal visual forms that synthetic neurons have become attuned to, often using grid overlays in reference to AI technical processes.
To look through one of my Windows of Perception is to experience the initial moment of AI visual perception — the scanning for simple edges and forms. These markings are machine-learned forms that increase both perceptual efficiency and perceptual bias. Said another way, by learning what to look for, we (and AIs) become biased to look for it. Each transparent window — which can be hung or free standing — is painted with an AI’s initial visual filters, chosen because of their similarity with human perceptual filters.
In my Convolutions series, I create collections of technological specimens: synthetic neurons that have attuned themselves to the visual fragments found in millions of Internet photos. Each of the pixelated patterns represents a distilled technological percept, together creating an AI visual grammar. Similar to the primal motifs learned by our brains, I collected the abstract archetypes from the landmark open-source neural networks — as AlexNet (2012) and Inception v1 (2014) — to capture seminal moments in AI history.
In my Machine Seeing Tree photos I contemplate how machines see the natural environment — the original “training dataset” for human vision — through bullet-hole-like AI “form detectors”.