{"id":1176,"date":"2025-02-03T10:22:46","date_gmt":"2025-02-03T09:22:46","guid":{"rendered":"https:\/\/aksioma.org\/ayasu\/?p=1176"},"modified":"2026-04-08T20:56:27","modified_gmt":"2026-04-08T18:56:27","slug":"rgbfaq","status":"publish","type":"post","link":"https:\/\/aksioma.org\/becomingimage\/symposium\/rgbfaq\/","title":{"rendered":"RGBFAQ"},"content":{"rendered":"\n
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RGBFAQ <\/em>traces the trajectory of computer graphics from WW2 to Bell Labs in the 1960s, from the visual effects studios of the 1990s to the GPU-assisted algorithms of the latest machine learning models. The story culminates with the emergence of the synthetic dataset: computer-generated images used as \u201cground truth\u201d for training computer vision algorithms. Synthetic data is increasingly sought after as a clean alternative to real world data sets, which are often biased, unethically sourced, or expensive to create. And while CGI data seems to avoid many of these pitfalls, RGBFAQ <\/em>aims from the outset to consider whether the virtual world is as clean and steady as we think. Cataloguing the \u201chacks\u201d used to construct the foundations of simulated worlds, it suggests that the expedient solutions of early computer graphics may be less than ideal material on which to build the foundations of another generation of technology.  

Commissioned by arebyte, with support from Arts Council England. Audio by David Kamp. Animation support from Tom Pounder and Nikolas Diamant. Intro music by Jacob Samuel. Projection mapping and lighting by Gorka Cortazar & Blanca Regina  Installation photography by Max Colson. Research was developed through work at Carnegie Mellon\u2019s Frank-Ratchye STUDIO for Creative Inquiry and Birkbeck\u2019s Vasari Research Centre for Art and Technology.


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RGBFAQ traces the trajectory of computer graphics from WW2 to Bell Labs in the 1960s, from the visual effects studios of the 1990s to the GPU-assisted algorithms of the latest machine learning models. The story culminates with the emergence of the synthetic dataset: computer-generated images used as \u201cground truth\u201d for training computer vision algorithms. Synthetic […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":true,"footnotes":""},"categories":[181,1],"tags":[],"class_list":["post-1176","post","type-post","status-publish","format-standard","hentry","category-screenings","category-symposium"],"acf":[],"yoast_head":"\nRGBFAQ - Tactics&Practice #17: Becoming image<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aksioma.org\/becomingimage\/symposium\/rgbfaq\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RGBFAQ - Tactics&Practice #17: Becoming image\" \/>\n<meta property=\"og:description\" content=\"RGBFAQ traces the trajectory of computer graphics from WW2 to Bell Labs in the 1960s, from the visual effects studios of the 1990s to the GPU-assisted algorithms of the latest machine learning models. 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