Recognizing Ourselves in AI-Generated Art

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It’s easy to say that AI art is becoming indistinguishable from human creations, but there are tell-tale signs that give the game away.

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Amelia Winger-Bearskin, an artist working with AI and an Associate Professor of Artificial Intelligence and the Arts at the University of Florida, thinks she’s spotted a set of aesthetic conventions commonly seen in AI-generated imagery.

She breaks them down into four categories in a series of illuminating blog posts published on Medium.

Particle Systems

For as long as computer graphics have existed, particle systems have been a big part of the CG in films, data art and live performances, and “artists and designers cannot get enough of them,” Winger-Bearskin comments. Nor, it seems, can AI.

“Particle systems in game engines are beautiful,” she says, describing a style of art that perhaps attracts the Gen Z artists most likely to be working with AI today.

Dada 3D

Dada 3D is the look popularized by filters to augmented images on your mobile phone. Cool 3D World, FeltZine, Vaporwave, and Instagram filters “all form part of a movement I term Dada 3D, which is something like a surrealist parlor game, a dada manifesto, and Cinema 4D got put into a blender,” Winger-Bearskin says. “This aesthetic style uses AI as mocap, detuned shaders, generative sounds, and code manipulation of game engines.”

Hyperreal

The ultra-realistic and sometimes uncanny valley rise of digital humans and deepfakes fall into the hyperreal category, which is most likely to have been created by algorithm. “I realize revenge porn is using this technique,” she caveats, “but I feel this is a foul form of harassment and not an aesthetic.”

Nightmare Corp.

Artworks in the “Nightmare Corp.” category include images created by DeepDream, Dall-E, Wombo apps, Midjourney, “and all the 1000000s of copycats we use until Dall-E is out of beta or until we can afford it.”

These images look close to something someone could make by hand, she says, but are rendered by a computer algorithm (most usually OpenAI) in 30 seconds or less. They have a unifying aesthetic in that there are smears, colors, and glitches that are still ubiquitous to each algorithm.

Winger-Bearskin delves deeper into why AI-generated images sometimes look like the stuff of nightmares.

An infamous example is the bizarre “puppy-slug” generated by Google’s DeepDream AI in 2015. A text prompt for images of dogs and the application of “dogness” to images that did not contain dogs resulted in images that are so far from puppified as to be “repulsive.” Yet DeepDream’s convolutional neural networks was trained to recognize dogs by being fed millions of pictures of dogs. So what happened?

“Many people assumed that a computer’s imagination, if you could call it that, would be precise, literal, and maybe even a little bit boring,” she says. “We were not expecting to see such vivid hallucinations and organic-seeming shapes.

“The reason some of these images look so frightening is [that] these models don’t actually ‘know’ anything. These images are products of computationally advanced algorithms and calculators that can track and compare pixel values. They’re able to spot and reproduce trends from their training data, but they aren’t equipped to make sense of what they’re given.”

You could be forgiven for thinking otherwise, especially given the impressive results that have been generated recently with OpenAI’s Dall-E.

But when interpreting these Dall-E pieces as art, it’s helpful to keep the old Arthur C. Clarke adage in mind: “Any sufficiently advanced technology is indistinguishable from magic.”

The magic of Dall-E involves a tremendous amount of mathematics, computer science, processing power, and countless hours of work from the researchers that produced it. But the imagery produced by it and other AIs should give us a clue as to what is going on under the hood.

As Winger-Bearskin explains, Dall-E, and tools like it, work by matching words and phrases to vast stores of image data, which are then used to train generative models. The process of matching text input to the correct images requires that someone make decisions about how to sort and define the images.

The people who make these decisions are the untold millions of low-wage data entry professionals around the world, content creators optimizing images for SEO, and anyone who has ever used a Captcha to access a website. That would include you.

“Like the artisans who worked on the great cathedrals of the middle ages, these people could live and die without ever receiving credit for their work, even though the project would literally not exist without their contributions.”

She goes on to conclude that images generated in this manner are “less like paintings than they are like mirrors, reflecting our own views and values back to us, albeit through a very elaborate prism.”

For this reason, we need to be wary when we look at these pictures of the limits and prejudices contained that these models show.