AI Art: How artists are using and confronting machine learning    
AI Art: How artists are using and confronting machine learning    
For the latest episode of our How to See series, we spoke with three artists—Kate Crawford, Trevor Paglen, and Refik Anadol—who engage with the ways that AI and machine learning algorithms are demanding new approaches to artmaking. “I think we are at a crucial inflection point right now,” says Kate Crawford, professor, artist, and author of "Atlas of AI." “I've been calling it the generative turn. It's a moment where what we previously understood as how everything from illustration to film directing to publishing works is all about to change very rapidly.” Trevor Paglen has been mining data sets that are used to train the machine learning systems that surveil our daily lives. He investigates the dangerous oversimplification inherent to these processes and the ethics of the intentions behind them. “Artists, what we bring to the party is thousands…of years of thinking about what the hell an image is,” he says. “The kind of engineering computer science tradition does not have that. This is a place where artists are bringing voices to the conversation that I think are quite urgent.” It’s in that spirit that Refik Anadol sees AI as a tool available to artists. His interest is in machine learning algorithms that aren’t strictly monitored by humans. For Unsupervised, he asked how a machine, if it had only MoMA’s collection data for knowledge, would parse the history of modern art on its own. And, as an autodidact, what kind of art would it create? These three prescient thinkers are joined by curators Paola Antonelli and Michelle Kuo, who give historical context to the existential questions at play in this emerging landscape and share insights into where art might bring AI next.
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