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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.

Nevertheless, these images have aesthetic implications and values which have to be taken into account. One could say that while the programmers use these images to help us better understand the programs' algorithms, we need the knowledge of artists to better understand the aesthetic forms of AI. As Steyerl has pointed out, such visualizations are generally understood as "true" representations of processes, but we should pay attention to their respective aesthetics, and their implications, which have to be viewed in a critical and analytical way.
In 2017, the artist Trevor Paglen created a project to make these invisible AI algorithms visible. In Sight Machine, he filmed a live performance of the Kronos Quartet and processed the resulting images with various computer software programs used for face detection, object identification, and even for missile guidance. He projected the outcome of these algorithms, in real time, back to screens above the stage. By demonstrating how the various different programs interpreted the musicians' performance, Paglen showed that AI algorithms are always determined by sets of values and interests which they then manifest and reiterate, and thus must be critically questioned. The significant contrast between algorithms and music also raises the issue of relationships between technical and human perception.

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