Shardcore writes, "The Tate recently released a 'big data' set of the 70k artworks in their collection. I've been playing with it and finding all sorts of fun to be had. The latest experiment uses the Tate data as a springboard to algorithmically imagine new artworks - 88,577,208,667,721,179,117,706,090,119,168 to be precise."
(that's eighty-eight nonillion, five hundred seventy-seven octillion, two hundred eight septillion, six hundred sixty-seven sextillion, seven hundred twenty-one quintillion, one hundred seventy-nine quadrillion, one hundred seventeen trillion, seven hundred six billion, ninety million, one hundred nineteen thousand, one hundred sixty-eight possible artworks...)
We can imagine machines which spot the items within a representational work (look at Google Goggles, for example) but algorithms which spot the ‘emotions and human qualities’ of an artwork are more difficult to comprehend. These categories capture complex, uniquely human judgements which occupy a space which we hold outside of simple visual perception. In fact I think I’d find a machine which could accurately classify an artwork in this way a little sinister…
The relationships between these categories and the works are metaphorical in nature, allusions to whole classes of human experience that cannot be derived from simply ‘looking at’ the artwork. The exciting part of the Tate data is really the ‘humanity’ it contains, something absolutely essential when we’re talking about art – after all, culture cannot exist without culturally informed entities experiencing it.
It struck me that these are not only representations of existing artworks, but actually the vocabulary and structure required to describe new, as yet un-made, artworks.
Machine Imagined Artworks (2013)
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Eric Hashbargar’s weird, laser-engraved dice are a tour-de-force: a pair of D6s for figuring out where to go for dinner in NYC; another D6 to figure out which die you should roll; an all-20s critical hit D20; Sicherman D6s that have different faces to a normal D6 pair, but the same probability distribution; punctuation mark […]
Tech anthropologist Genevieve Bell (previously) delivered one of the keynotes at last week’s O’Reilly AI conference in New York City, describing how you could do anthropology fieldwork on an AI — specifically, how you could do an ethnographic interview with one.
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