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Was This Psychedelic Image Made by Man or Machine?

A leaked image making the rounds on Reddit has people asking whether a robot could draw these twisted squirrels.
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Was this image created by man or machine? Curious and disturbed users on Reddit are currently up in arms about the source of this leaked picture, allegedly generated by a trained convolutional neural network (CNN). Accompanied only by the short caption “this image was created by a computer on its own”, the picture has been going viral on Twitter and Tumblr since it was leaked roughly a week ago.

The image features a hybrid panoply of squirrels, slugs, dogs and tiny horse legs as well as fractal sequences of houses, cars, and streets—and a lot of eyes. Currently, convolutional neural networks are trained primarily for facial recognition purposes—once algorithmically calculated to a specific degree, the CNN can match up similar images in a database with a suggested vector input.


Since being released, the image has been met with skepticism on Reddit. Users are weighing in with polarized comments; some are convinced that the image is simply an elaborate hoax by a visual (human) artist. Others argue that the multiplicity of the eyes and patterns in robotically logical but visually discordant structures are typical of an algorithm making sense of a command, supplementing their arguments with CNN image classification papers with previous, similar visual examples.

Google Research and MIT Computer Science and AI Laboratory

“The human mind can easily recognize edges and shapes because it's something we're trained to do since birth,” says Matt LePage, a visual psychologist and UX developer in Brooklyn who has analyzed the image in question. “But as far as a program knows it's just a grid of pixels of varying colors. CNNs aim to identify telltale blocks of an image, whether it be a large general shape or something tiny and highly specific. What we're seeing here could be the result of taking this process in the opposite direction: instead of taking an image and looking for hints, the algorithm is taking a set of hints its learned to recognize and creating an image that it thinks perfectly fits that internal definition.”

Users were also frantically feeding the image back into Google searches to dig up the source, but to no avail. Deeper into the Reddit and subreddit conversation threads (it has already popped up on /r/creepy and others), questions of autonomous creativity and the definition of ‘creator’ arises; if a computer ‘made’ the image, but the computer was in turn made by a human and programmed by a human, ergo was the image fundamentally created by a human?


“Making something like this is entirely possible in Photoshop by hand, but it could just as well be the output of something entirely generative—software that uses machine learning, a complex filter — think a more souped up version of Instagram— or a series of happy digital accidents,” says Tim Froehlich, web developer and digital artist in Brooklyn. “Whatever it is, it's neat.”

An inside source states that the image was actually a personal joke project by a single engineer (not the leaker) who programmed an algorithm to replace human features for animal ones and either ran it multiple times or backwards, each time focusing on smaller and smaller regions to pattern-match. (Similar to when Berlin artist Sebastian Schmieg’s took a blank image and repeatedly ran it through Google search.) A Google research paper titled "Inceptionism: Going Deeper into Neural Networks" (released June 17) reveals processes that closely resemble how a CNN could have created this image; in it, a hallucinogenic-esque gallery of "Inceptionism" images reveal worlds that perhaps only computers could dream up.

Google Research and MIT Computer Science and AI Laboratory

Google Research and MIT Computer Science and AI Laboratory

Google Research and MIT Computer Science and AI Laboratory

Google Research and MIT Computer Science and AI Laboratory

Check out more images in the official "Inceptionalism" photo gallery, and read about CNNs on the Google Research Blog.


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