Taking Stock of Generative “AI”: Systematic Work of Michael Mandiberg, Penelope Umbrico, and Trevor Paglen
BCNM alum Trevor Paglen's generative art is spotlighted in the Brooklyn Rail.
From the article:
Paglen went spelunking into various data sets he created, like “OMENS AND PORTENTS” that includes categories like “rainbows” and “black cats.” The discriminator gets trained on many such images and then the generator aims to produce a rainbow or black cat that the discriminator will accept as such; the discriminator gets better and better at producing primitives that have not been expressed (tagged, described, identified) in the generator’s data set in order to generate a satisfactory image. Paglen, however, intervened by targeting a specific data point (the neuron, as many still call it) in the latent space and instructing the generator to do something with it. The project shows how the “algorithmic rationalization of data … can pick up on patterns in data that simply do not exist except, that is, within the preserve of a computational illusion,” as Downey clarifies in his essay for the book.
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