Art history is a lot like archaeology — we here in the present day get artifacts and records, but the gaps between them are enormous, and the questions that they beg loom large. Historians need to be able to investigate and interpret, to unpack the meanings and the methods of a given work of art — but even for the best, the act of reconstruction is a trying test. Can we program computers to decipher the backstory of a painting — analyzing light and shadow to guess at how a piece was made? And, even more ambitiously, can AI learn to see and tell the stories rendered in an image’s symbolic content? Recent innovations yield surprising insights and suggest a cyborg future for art scholarship, in which we teach machines to not just recognize a set of objects, but to grok their context and relationships — shining light on messages and narratives once lost to time, and deepening our study of the world of signs.
Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.
This week we speak with David Stork, who has held full-time and visiting faculty positions in Physics, Mathematics, Computer Science, Electrical Engineering, Statistics, Neuroscience, Psychology, and Art and Art History variously at Wellesley and Swarthmore Colleges and Clark, Boston, and Stanford Universities…as well as holding corporate positions as Chief Scientist at Ricoh Innovations and Fellow at Rambus, Inc. We talk about the what happens when computers look at art — and the implications for art history and connoisseurship.
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Podcast theme music by Mitch Mignano.
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David’s bio at International Academy, Research, and Industry Association