Visible / Invisible The artist Paul Klee often talked about art as “making the invisible visible.” In computer technology, most algorithms work invisibly, in the background; they remain inaccessible in the systems we use daily. But lately there has been an interesting comeback of visuality in machine learning. The ways that the deep-learning algorithms of AI are processing data have been made visible through applications like Google’s DeepDream, in which the process of computerized pattern-recognition is visualized in real time. The application shows how the algorithm tries to match animal forms with any given input. There are many other AI visualization programs that, in their way, also “make the invisible visible.” The difficulty in the general public perception of such images is, in Steyerl’s view, that these visual patterns are viewed uncritically as realistic and objective representations of the machine process. She says of the aesthetics of such visualizations: For me, this proves that science has become a subgenre of art history. ... We now have lots of abstract computer patterns that might look like a Paul Klee painting, or a Mark Rothko, or all sorts of other abstractions that we know from art history. The only difference, I think, is that in current scientific thought they’re perceived as representations of reality, almost like documentary images, whereas in art history there’s a very nuanced understanding of different kinds of abstraction. What she seeks is a more profound understanding of computer-generated images and the different aesthetic forms they use. They are obviously not generated with the explicit goal of following a certain aesthetic tradition. The computer engineer Mike Tyka, in a conversation with Steyerl, explained the functions of these images: Deep-learning systems, especially the visual ones, are really inspired by the need to know what’s going on in the black box. Their goal is to project these processes back into the real world. Nev