What Algorithmic Art Can Teach Us About Artificial Intelligence, We live in a world that’s increasingly controlled by what might be called “the algorithmic gaze.” As we cede more decision-making power to machines in domains like health care, transportation, and security, the world as seen by computers becomes the dominant reality. If a facial recognition system doesn’t recognize the color of your skin, for example, it won’t acknowledge your existence.If a self-driving car can’t see you walk across the road, it’ll drive right through you. That’s the algorithmic gaze in action.
This sort of slow-burning structural change can be difficult to comprehend. But as is so often the case with societal shifts, artists are leaping headfirst into the epistemological fray. One of the best of these is Tom White, a lecturer in computational design at the University of Wellington in New Zealand whose art depicts the world, not as humans see it, but as algorithms do.
White started making this kind of artwork in late 2017 with a series of prints called “The Treachery of ImageNet.” The name combines the title of René Magritte’s famous painting of a pipe that isn’t a pipe, and ImageNet, a database of pictures that’s used across the industry to train and test machine vision algorithms. “It seemed like a natural parallel for me,” White tells The Verge. “Plus, I can’t resist a pun.”
To humans, the pictures look like haphazard arrangements of lines and blobs that lack any obvious immediate structure. But to algorithms trained to see the world on our behalf, they leap off the page as specific objects: electric fans, sewing machines, and lawnmowers. The prints are optical illusions, but only computers can see the hidden image. (In the video above you can see how White develops these images, plus how Verge staffers did at guessing what they show.)
White’s work has attracted a lot of attention in the machine learning community, and it’s getting its first major gallery show this month as part of an exhibition of AI artwork in India at Delhi’s Nature Morte gallery. White says he designs his prints to “see the world through the eyes of a machine” and make “a voice for the machine to speak in.”
That “voice” is actually a series of algorithms that White has dubbed his “Perception Engines.” They take the data that machine vision algorithms are trained on — databases of thousands of pictures of objects — and distill it into abstract shapes. These shapes are then fed back into the same algorithms to see if they’re recognized. If not, the image is tweaked and sent back, again and again, until it is. It’s a trial and error process that essentially ends up reverse-engineering the algorithm’s understanding of the world.
White compares the process to a “computational ouija board,” where neural networks “simultaneously nudge and push a drawing toward the objective.” He tells The Verge that this method gives him the control he wants out of the output, though it can take days to create a single image in this way, and he admits that the process is “kind of tedious.”
Unlike some artists who work with machine learning, White doesn’t pretend that his prints are the product of a some autonomous AI (a disingenuous narrative sometimes pushed by artists and promoters in order to create a feeling of technological mysticism). Instead, he’s up front about his role: he sets a number of starting parameters for his perception engines, like the colors and thickness of lines, and winnows the output, rejecting prints that he doesn’t find aesthetically pleasing. Although he is giving his algorithms a voice to speak in, he’s also making sure the results are pleasant to hear. “I think I am trying to free the algorithm so it can express itself, so people can relate to what it’s saying,” he says.
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