Shallow Learning 42, 2022

“Deep learning” is a branch of artificial intelligence that allows a computer to learn to identify and categorize data without human supervision, a kind of technology commonly used in image recognition, facial detection, computer vision, and natural language processing software, among many other things. Hegert titled this series ‘Shallow Learning’ in contrast or opposition to that term.

Two commonplace yet sophisticated digital tools that recognize or “see” photographs were central to this work. Each image was  used as search criteria in Google’s “search by image” feature, which in turn offered a selection of visually similar images-- algorithmic guesses at what these pictures showed. Hegert then selected one of these “best guesses” from Google, placed it next to the original image on a blank canvas in Photoshop, and filled in the area between the two images using the “content aware fill” function. Just as Hegert wonders what he can learn about the world by looking at images, he uses this body of work to ask what images are learning about the world by looking at each other.

Aaron Hegert, b. 1982, lives and works in Texas. He is currently the Assistant Professor of Photography at Texas Tech University. His work has been exhibited and published widely with recent exhibitions at Utah Museum of Contemporary Art; Aperture Foundation in New York City; and University Art Gallery at the University of California, San Diego. He is a Fulbright Scholar and a founding member of Everything Is Collective.

Inkjet print on high gloss metallic paper
Edition of 25

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