Creative Adversarial Networks: GANs that make art

    Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model.

    Rutgers comp sci prof Ahmed Elgammal runs an Art and AI Lab where they use "Creative Adversarial Networks" to produce new artworks: CANs use a "discerner" that seeks out "novelty," not fidelity to the statistical predictions of the model. The underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set."

    Elgammal recent exhibited a show called Faceless Portraits Transcending Time at Chelsea's HG Contemporary gallery; and his choice of portraiture as a means of showcasing the capabilities of CANs has proven to be controversial: as art historian John Sharp says, "You can’t really pick a form of painting that’s more charged with cultural meaning than portraiture." Portraits use extensive, coded symbology to say something about their subjects, and CANs do not, by themselves, understand or correctly use these symbols in the works they create.

    Despite the controversy, a large number of Elgammal's prints have sold (admittedly at rock-bottom-for-Chelsea prices of $6k-$18k).

    “You can’t really pick a form of painting that’s more charged with cultural meaning than portraiture,” John Sharp, an art historian trained in 15th-century Italian painting and the director of the M.F.A. program in design and technology at Parsons School of Design, told me. The portrait isn’t just a style, it’s also a host for symbolism. “For example, men might be shown with an open book to show how they are in dialogue with that material; or a writing implement, to suggest authority; or a weapon, to evince power.” Take Portrait of a Youth Holding an Arrow, an early-16th-century Boltraffio portrait that helped train the AICAN database for the show. The painting depicts a young man, believed to be the Bolognese poet Girolamo Casio, holding an arrow at an angle in his fingers and across his chest. It doubles as both weapon and quill, a potent symbol of poetry and aristocracy alike. Along with the arrow, the laurels in Casio’s hair are emblems of Apollo, the god of both poetry and archery.

    A neural net couldn’t infer anything about the particular symbolic trappings of the Renaissance or antiquity—unless it was taught to, and that wouldn’t happen just by showing it lots of portraits. For Sharp and other critics of computer-generated art, the result betrays an unforgivable ignorance about the supposed influence of the source material.

    The AI-Art Gold Rush Is Here [Ian Bogost/The Atlantic]

    (via Beyond the Beyond)

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