While much AI art is made using open-source resources, such as Google’s TensorFlow and Facebook’s Torch environment, Fjeld says artists who create their own algorithms (elements 2 and 3), as White does, own those, too.
“The artist could sell the code as a work, though I’m not aware of that having happened yet,” she says. It’s an interesting idea, though, and one that may appeal to collectors, who could then use an AI artist to create their own, previously unseen outputs.
Preserving the means to execute code as it was intended—especially where it interacts with proprietary software or hardware—could be challenging, however.
“One of the main maintenance issues is that of software frameworks updating very rapidly, making trained neural network models redundant over time,” says Harshit Agrawal, a participating artist in “Gradient Descent” who lives and works in Bangalore.
Akten especially worries about works that integrate web technologies—“things like Google Translate, or sending a query to Microsoft’s face recognition cloud API, or using Amazon Cloud services, or even works that live in the now-defunct Vine.”
“Already, I know of quite a few works that have ‘died’ because a cloud API changed or was retired,” he says. Conceiving of AI works as performances presents a solution. “They run for as long as the technology allows them to, and then they end. And we’re left with the documentation, and the memories.”