Shallow Learning, Aaron Hegert (2017)
Shallow Learning is a book that compares the way people see photographs to the way algorithms see photographs. “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. This book is titled Shallow Learning as a contrast or opposition to that term.
Two commonplace yet sophisticated digital tools that recognize or “see” photographs were central to this work. Each of the images in this book is a composite that started as a single, unpublished photo from the authors archive. The author then searched for these photos, which had never been exhibited or posted on the internet, in Google’s “search by image” feature. This feature of the search engine is typically used to track down the provenance of an online image, but because the images he was searching for did not exist online, the search engine could not find them and instead offered a selection of visually similar images-- algorithmic guesses at what these pictures showed. He 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. This same function was used in the layout of the book.
Hegert was motivated to make this book because the digital tools he uses in his practice as an artist have begun to do more of their own thinking. And just as he has always wondered what we can learn about the world by looking at images, we must now also consider what images are learning about the world by looking at each other.
Included in the Joan Flasch Artists' Book Collection at the School of the Art Institute of Chicago.
8.5 x 11 inches (215.9 x 279.4 mm)
Digial Offset Printed
Edition of 300
Out of stock