Introduction to AI-Generated Art
If you stumbled across Terence Broad’s AI-generated artwork, you might assume he’d trained a model on the works of the painter Mark Rothko. The earlier, lighter pieces, before his vision became darker and suffused with doom, share similarities with Broad’s AI-generated images. These images consist of simple fields of pure color, but they’re morphing, continuously changing form and hue. However, Broad didn’t train his AI on Rothko; he didn’t train it on any data at all.
The Creative Process
By hacking a neural network and locking elements of it into a recursive loop, Broad was able to induce this AI into producing images without any training data at all — no inputs, no influences. Depending on your perspective, Broad’s art is either a pioneering display of pure artificial creativity, a look into the very soul of AI, or a clever but meaningless electronic by-product. In any case, his work points the way toward a more creative and ethical use of generative AI beyond the large-scale manufacture of derivative slop now oozing through our visual culture.
Inspiration and Motivation
Broad has deep reservations about the ethics of training generative AI on other people’s work, but his main inspiration for his artwork wasn’t philosophical; it was a crappy job. In 2016, after searching for a job in machine learning that didn’t involve surveillance, Broad found employment at a firm that ran a network of traffic cameras in the city of Milton Keynes, with an emphasis on data privacy. His job was training these models and managing these huge datasets, but he soon got sick of managing datasets.
Overcoming Legal Threats
Legal threats from a multinational corporation pushed him further away from inputs. One of Broad’s early artistic successes involved training a type of artificial neural network called an autoencoder on every frame of the film Blade Runner (1982), and then asking it to generate a copy of the film. The result was a demonstration of the limitations of generative AI and a wry commentary on the perils of human-created intelligence. However, Warner Bros. issued a DMCA takedown notice, which led Broad to reevaluate his approach to AI-generated art.
The Eureka Moment
Broad’s eureka moment was an intuition that he could replace the training data in the GAN with another generator network, loop it to the first generator network, and direct them to imitate each other. His early efforts led to mode collapse and produced "gray blobs; nothing exciting," but when he inserted a color variance loss term into the system, the images became more complex, more vibrant. Subsequent experiments with the internal elements of the GAN pushed the work even further.
Understanding the Process
Looking at his initial results, the Rothko comparison was immediately apparent; Broad says he saved those first images in a folder titled "Rothko-esque." However, the comparison sort of misses the point; the brilliance in Broad’s work resides in the process, not the output. He didn’t set out to create Rothko-esque images; he set out to uncover the latent creativity of the networks he was working with. The question remains: did he succeed? Even Broad’s not entirely sure.
The Mystery of AI
Talking to him about his process, and reading through his PhD thesis, one of the takeaways is that, even at the highest academic level, people don’t really understand exactly how generative AI works. We know that if we feed generative AI data, a composite of those inputs will come out the other side, but no one really knows, on a granular level, what’s happening inside the black box. Broad’s explorations of inputless output shed some light on the internal processes of AI, even if his efforts sometimes sound more like early lobotomists rooting around in the brain with an ice pick rather than the subtler explorations of, say, psychoanalysis.
Conclusion
Revealing how these models work also demystifies them — critical at a time when techno-optimists and doomers alike are laboring under what Broad calls "bullshit," the "mirage" of an all-powerful, quasi-mystical AI. Broad’s work shows that we think AI is doing far more than it is, but it’s just a bunch of matrix multiplications. It’s very easy to get in there and start changing things. By understanding the process behind AI-generated art, we can unlock new possibilities for creative and ethical use of generative AI.
