or verifiable. And while it may be seriously desirable to identify Daesh moles posing as refugees, a similar system seems to have worrying flaws.
The NSA’s SKYNET program was trained to find terrorists in Pakistan by sifting through cell-phone customer metadata. But experts criticize the NSA’s methodologies. “There are very few ‘known terrorists’ to use to train and test the model,” explained Patrick Ball, a data scientist and director of the Human Rights Data Analysis Group, to Ars Technica. “If they are using the same records to train the model as they are using to test the model, their assessment of the fit is completely bullshit.”18
The Human Rights Data Analysis Group estimates that around 99,000 Pakistanis might have ended up wrongly classified as terrorists by SKYNET, a statistical margin of error that may have had deadly consequences given the fact that the US is waging a drone war on suspected militants in the country, and between 2,500 and 4,000 people are estimated to have been killed since 2004: “In the years that have followed, thousands of innocent people in Pakistan may have been mislabelled as terrorists by that ‘scientifically unsound’ algorithm, possibly resulting in their untimely demise.”19
One needs to emphasize strongly that SKYNET’s operations cannot be objectively assessed, since it is not known how its results were utilized. It was most certainly not the only factor in determining drone targets.20 But the example of SKYNET demonstrates just as strongly that a “signal” extracted by assessing correlations and probabilities is not the same as an actual fact, but is determined by the inputs the software uses to learn, and the parameters for filtering, correlating, and “identifying.” The old engineer wisdom “crap in—crap out” seems still to apply. In all of these cases—as completely different as they are technologically, geographically, and also ethically—some version of pattern recognition was used to classify groups of people according to political and social parameters. Sometimes it is as simple as, we try to avoid registering refugees. Sometimes there is more mathematical mumbo jumbo involved. But many of the methods used are opaque, partly biased, exclusive, and—as one expert points out—sometimes also “ridiculously optimistic.”21
Corporate Animism
How to recognize something in sheer noise? A striking visual example of pure and conscious apophenia was recently demonstrated by research labs at Google:22
We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want. The network typically consists of 10–30 stacked layers of artificial neurons. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network’s “answer” comes from this final output layer.23
Neural networks were trained to discern edges, shapes, and a number of objects and animals and then applied to pure noise. They ended up “recognizing” a rainbow-colored mess of disembodied fractal eyes, mostly without lids, incessantly surveilling their audience in a strident display of conscious pattern overidentification.
Google DeepDream images.
Source: Mary-Ann Russon, “Google DeepDream robot: 10 weirdest images produced by AI ‘inceptionism’ and users online,” ibtimes.co.uk, July 6, 2015.
Google researchers call the act of creating a pattern or an image from nothing but noise “inceptionism” or “deep dreaming.” But these entities are far from mere hallucinations. If they are dreams, those dreams can be interpreted as condensations or displacements of the current technological disposition. They reveal the networked operations of computational image creation, certain presets of machinic vision, its hardwired ideologies and preferences.
One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana. By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.24
In a feat of genius, inceptionism manages to visualize the unconscious of prosumer networks: images surveilling users, constantly registering their eye movements, behavior, preferences, aesthetically helplessly adrift between Hundertwasser mug knockoffs and Art Deco friezes gone ballistic. Walter Benjamin’s “optical unconscious” has been upgraded to the unconscious of computational image divination.25
By “recognizing” things and patterns that were not given, inceptionist neural networks eventually end up effectively identifying a new totality of aesthetic and social relations. Presets and stereotypes are applied, regardless of whether they “apply” or not: “The results are intriguing—even a relatively simple neural network can be used to over-interpret an image, just like as children we enjoyed watching clouds and interpreting the random shapes.”26
But inceptionism is not just a digital hallucination. It is a document of an era that trains smartphones to identify kittens, thus hardwiring truly terrifying jargons of cutesy into the means of production.27 It demonstrates a version of corporate animism in which commodities are not only fetishes but morph into franchised chimeras.
Yet these are deeply realist representations. According to György Lukács, “classical realism” creates “typical characters” insofar as they represent the objective social (and in this case technological) forces of our times.28
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