At the core of our perceptual experiences lies the concept of iterative visual abstractions, which has fascinated artists and scientists for centuries as they strive to access and understand their inner visual realities. While biological and artificial neural networks possess distinct differences, they share a common initial usage of monochrome “edge detection” filters. I contemplate these filters as visual overlays to allude both to a technical process while emphasizing the inseparability between human and artificial worlds. 

As I work with my digital photographs, I think about the billions of Internet photographs upon which AIs are trained. The landscape imagery is the original “training dataset” for human vision and, indirectly, training input for AIs as they seek to mimic human vision. The overlaid filters are constant reminders, reminiscent of bullet holes, of the ubiquitous AI-processing of our lives’ pixels.