In recent years, literature has been getting attention from an unusual quarter: mathematics. Alongside statistical physicists analysing the connections between characters in the Icelandic sagas, and computer scientists exploring the life an
Hard-working artisan, solitary genius, credentialed professional—the image of the artist has changed radically over the centuries. What if the latest model to emerge means the end of art as we have known it?
Guy Debord’s (1931–1994) best-known work, La société du spectacle (The Society of the Spectacle) (1967), is a polemical and prescient indictment of our image-saturated consumer culture. The book examines the “Spectacle,” Debord’s term for the everyday manifestation of capitalist-driven phenomena; advertising, television, film, and celebrity.
Technology has long been considered a resource-liberating mechanism, granting us better access to resources like information, food and energy. Yet what is often overlooked is the revolutionary impact technology can have on our ability to create art. Many artists are reacting to a world of accelerating change and rapid digitization through their work. Emerging artistic …
Sam Kronick has a bunch of rocks arrayed in front of him on a raised desk in his Oakland studio. He’s an artist and his plan is to sketch the rocks, but not with pen and paper. He and his artistic partner Tara Shi are going to do a 3D scan of them so that an artificial intelligence program can map their contours, learn to recognize rocks and then start generating its own craggy depictions.
The project is deceptively simple: trying to get artificial intelligence to make nature art. But it’s also a way of figuring out the limits of computational creativity. Kronick and Shi are using a neural net, a computer program loosely modeled on biological neural systems like the human brain. A given neural net needs to be trained on data; that could be the shape of a lot of rocks, a massive trove of Google Images, or hundreds of thousands of search terms, depending on what the neural net will be used for. Then, basically, it thinks in layers, with each layer working on a different aspect of whatever it is the network is analyzing (for instance, if a network were learning to identify rocks, one algorithm may try and find the texture of a rock, another different colors on its surface, and so on).
The immediate task has quickly become tricky. When a software program is doing face detection, it’s pretty obvious whether it’s working or not. But asking a computer to make rocks is more complex, says Kronick, because it raises ontological questions. “What is a rock? What matters about a rock?” says Kronick. “Why is that as much of a rock as these are according to this model that we’ve built?”
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