{"id":8589,"date":"2017-08-05T08:45:49","date_gmt":"2017-08-05T16:45:49","guid":{"rendered":"http:\/\/www.palada.net\/index.php\/2017\/08\/05\/news-2362\/"},"modified":"2017-08-05T08:45:49","modified_gmt":"2017-08-05T16:45:49","slug":"news-2362","status":"publish","type":"post","link":"https:\/\/www.palada.net\/index.php\/2017\/08\/05\/news-2362\/","title":{"rendered":"The Neural Net That Recreated \u2018Blade Runner\u2019 Has the Movie Stuck in Its Memory"},"content":{"rendered":"

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Credit to Author: Marissa Clifford| Date: Sat, 05 Aug 2017 16:00:00 +0000<\/strong><\/p>\n

Artist and machine learning engineer Terence Broad’s Auto-Encoding Blade Runner<\/a> is the project Philip K. Dick would have made if he were a scientist. <\/p>\n

In his presentation at SIGGRAPH 2017, a computer graphics and animation conference, Broad detailed how he trained a Convolutional Autoencoder\u2014a type of neural network\u2014to recognize patterns of data in Blade Runner <\/i>and then reconstruct it, scene by scene. What results is an eerily-accurate full-length film that was so convincing that Warner Bros. issued a DMCA takedown<\/a> notice to Vimeo when Broad first uploaded the footage in 2016. <\/p>\n

Since then, many speculated that<\/a> Broad had found a loophole around copyright (it’s not). The real question behind Broad’s Blade Runner<\/i> parallels the themes of Philip K Dick’s legendary novel Do Androids Dream of Electric Sheep<\/i>: Where does one draw the line between human and machine, the real and the seemingly real? <\/p>\n

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