Ahna Girshick

Instagram: ahna_girshick

Website: http://lightdark.org

Bio: Ahna Girshick is an interdisciplinary artist and research scientist, who investigates the primal beauty and connections between human and AI visual perception. Working with algorithms, photography, paint, and installation, Ahna reimagines her scientific research through materiality and reclaims it through her personal lens as a female computer scientist/neuroscientist. Ahna holds a PhD from UC Berkeley in Vision Science, was a postdoctoral fellow at the Center for Neural Science at NYU and at UC Berkeley’s Department of Computer Sciences, holds a BS and MS in Computer Science from the University of Minnesota, and has published over 20 peer-reviewed publications and five patents. She is the recipient of an NIH NRSA three-year postdoctoral fellowship, a DOE Computational Sciences four-year graduate fellowship, and was named by the AI conference RE•WORK to their list of “30 Influential Women Advancing AI" in 2019. As an artist and producer in the early 2010s, she created interactive musical data visualization experiences in collaboration with musicians Philip Glass and Björk and the new media artist Scott Snibbe. These works were exhibited at the Museum of Modern Art (NY), The Contemporary Jewish Museum (SF), and The Barbican Centre (London). Since 2021, she has focused on illuminating the invisible inner perpetual states of humans and AIs, which she has exhibited through Southern Exposure (SF), Gearbox Gallery (Oakland), Hera Gallery (Rhode Island), Ely Center for Contemporary Art (New Haven), and ARC Gallery (Chicago).

Statement: At the core of our perceptual experiences lies the search for pre-determined visual patterns — archetypal forms learned by both human or artificial brains — which both optimize and bias perception. I imagine the experience of looking through the eyes of an AI and wonder about intermediate interior states never seen by humans. Using algorithms, paint, photography and installations, I use overlays of synthetic neurons to ask the viewer to reflect on the internal opaque processes of both AIs and humans. 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 — machine-learned forms that reference visual bias in its primal form. Each transparent window is painted with the initial visual filters in AI vision, revealing AI’s otherwise opaque technical processes and reflecting on their interconnection with human vision. “Cabinets of curiosities” emerged in the 16th century as means to systematically organize and preserve an era’s scientific learnings. In my “Convolutions” series I collect the technological specimens of our time: Synthetic neurons that have attuned themselves to the visual fragments found in millions of Internet photos. Landscape imagery is the original “training dataset” for human vision and, indirectly, training input for AIs as they seek to mimic human vision. In my “Machine Seeing Tree” photos I contemplate how machines see the natural environment through overlaying synthetic neurons, reminiscent of bullet holes — constant reminders of the ubiquitous AI-processing of our lives’ pixels.