While artificial intelligence has advanced to the point where it’s able to generate creative content, the technology’s output is still very…experimental.
Breakthroughs in neural networks—a type of machine learning that vaguely imitates the structure of neurons in the brain—have given rise to a form of the technology called generative AI that can do everything from imitate photorealistic images and abstract art to composing music or writing.
While these tools have raised concerns over their potential use for fabricated news footage and circumventing copyright laws, the vast majority of content produced by this type of AI still has a slightly off-kilter quality that betrays its non-human creator.
As the cultural debate around AI-fueled art begins to heat up, we’re looking back on what kind of work has actually come out of the initial experiments in this space.
Here are six examples of AI’s use in creative processes that offer a sense of the current state of the technology and a hint at its larger potential:
1. Google’s DeepDream neural network
Google’s DeepDream computer vision software, first released in 2015, turns any image into an abstract hallucinogenic version of itself by finding and enhancing certain patterns within the image. While the system might have little practical use for creative professionals on its face, it represented an early foray into the type of AI-generated art that has come to proliferate the open-source community.
One of the most important breakthroughs in AI art also came from then-Google AI researcher Ian Goodfellow in a 2014 paper in which he formalized the structure for something called a generative adversarial network, a key tool in AI-created content.
2. Chelsea gallery show of AI art
Christie’s Auction House’s sale of its first AI-generated art piece for nearly half a million dollars has given rise to a rush among artists and coders to create the next AI art sensation. One of the most interesting manifestations was an all-AI art exhibit on display in Chelsea earlier this year. These types of pieces are not without controversy; one major question is who gets credit for a piece that’s essentially an amalgam of a huge trove of input artwork.
3. When AI tried to write the perfect Lexus ad
Last November, Lexus and agency The&Partnership commissioned a neural network to write a Lexus TV ad in what the brand claimed was the first AI-generated commercial directed by an Oscar winner (Kevin Macdonald, who won Best Documentary for 2000’s One Day in September). While the ad isn’t the best piece of work from a creative standpoint, the AI’s uncanny abilities tease an unsettling future.
4. AKQA used AI to invent a sport
Portland design agency AKQA recently fed rules and data from 400 sports into a neural network that then formulated a game of its own–a cross of sorts between Ultimate Frisbee, soccer and croquet. “Speedgate” has already gained certification from the Oregon Sports Authority and is attracting the attention of college intramural organizers.
5. GumGum pitted AI against real art
Computer vision firm GumGum teamed with Rutgers University’s art and AI lab to commission five artists to create paintings based on the same data set used by a Generative Adversarial Network. The team then put the human-created results side-by-side with the work generated by the AI to demonstrate how eerily similar machines have come to matching our talent—and see how many people could spot the difference.
6. JWT’s ‘Next Rembrandt’
JWT used facial recognition algorithms and data from Rembrandt’s 346 known paintings to recreate the 17th-century Dutch artist’s style for a new painting in an award-winning but somewhat unsettling campaign for financial firm ING. The brand and agency rolled out a callback campaign in January when it re-engineered Rembrandt’s voice with the help of Carnegie Mellon University.