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This is an image from the Snowden files. It is labeled “secret.”¹ Yet one cannot see anything on it. This is exactly why it is symptomatic.

Not seeing anything intelligible is the new normal. Information is passed on as a set of signals that cannot be picked up by human senses. Contemporary perception is machinic to a large degree. The spectrum of human vision only covers a tiny part of it. Electric charges, radio waves, light pulses encoded by machines for machines are zipping by at slightly subluminal speed. Seeing is superseded by calculating probabilities. Vision loses importance and is replaced by filtering, decrypting, and pattern recognition. Snowden’s image of noise could stand in for a more general human inability to perceive technical signals unless they are processed and translated accordingly.

But noise is not nothing. On the contrary, noise is a huge issue, not only for the NSA but for machinic modes of perception as a whole.

Signal v. Noise was the title of a column on the internal NSA website running from 2011 to 2012. It succinctly frames the NSA’s main problem: how to extract “information from the truckloads of data”: “It’s not about the data or even access to the data. It’s about getting information from the truck-loads of data … Developers, please help! We’re drowning (not waving) in a sea of data—with data, data everywhere, but not a drop of information.”²

Analysts are choking on intercepted communication. They need to unscramble, filter, decrypt, refine, and process “truckloads of data.” The focus moves from acquisition to discerning, from scarcity to overabundance, from adding on to filtering, from research to pattern recognition. This problem is not restricted to secret services. Even WikiLeaks’s Julian Assange states: “We are drowning in material.”³

Apophenia

But let’s return to the initial image. The noise on it was actually decrypted by GCHQ technicians to reveal a picture of clouds in the sky. British analysts have been hacking video feeds from Israeli drones since at least 2008, a period which includes the recent IDF aerial campaigns against Gaza.⁴ But no images of these attacks exist in Snowden’s archive. Instead, there are all sorts of abstract renderings of intercepted broadcasts. Noise. Lines. Color patterns.⁵ According to leaked training manuals, one needs to apply all sorts of massively secret operations to produce these kinds of images.⁶

But let me tell you something. I will decrypt this image for you without any secret algorithm. I will use a secret ninja technique instead. And I will even teach you how to do it for free. Please focus very strongly on this image right now.

Doesn’t it look like a shimmering surface of water in the evening sun? Is this perhaps the “sea of data” itself? An overwhelming body of water, which one could drown in? Can you see the waves moving ever so slightly?

I am using a good old method called apophenia.

Apophenia is defined as the perception of patterns within random data.⁷ The most common examples are people seeing faces in clouds or on the moon. Apophenia is about “drawing connections and conclusions from sources with no direct connection other than their indissoluble perceptual simultaneity,” as Benjamin Bratton recently argued.⁸

One has to assume that, sometimes, analysts also use apophenia.

Someone must have seen the face of Amani al-Nasasra in a cloud. The forty-three-year-old was blinded by an aerial strike in Gaza in 2012 while sitting in front of her TV:

“We were in the house watching the news on TV. My husband said he wanted to go to sleep, but I wanted to stay up and watch Al Jazeera to see if there was any news of a ceasefire. The last thing I remember, my husband asked if I changed the channel and I said yes. I didn’t feel anything when the bomb hit—I was unconscious. I didn’t wake up again until I was in the ambulance.” Amani suffered second degree burns and was largely blinded.⁹

What kind of “signal” was extracted from what kind of “noise” to suggest that al-Nasasra was a legitimate target? Which faces appear on which screens, and why? Or to put it differently: Who is “signal,” and who disposable “noise”?

Pattern Recognition

Jacques Rancière tells a mythical story about how the separation of signal and noise might have been accomplished in Ancient Greece. Sounds produced by affluent male locals were defined as speech, whereas women, children, slaves, and foreigners were assumed to produce garbled noise.¹⁰ The distinction between speech and noise served as a kind of political spam filter. Those identified as speaking were labeled citizens and the rest as irrelevant, irrational, and potentially dangerous nuisances. Similarly, today, the question of separating signal and noise has a fundamental political dimension. Pattern recognition resonates with the wider question of political recognition. Who is recognized on a political level and as what? As a subject? A person? A legitimate category of the population? Or perhaps as “dirty data”?

What is dirty data? Here is one example:

Sullivan, from Booz Allen, gave the example the time his team was analyzing demographic information about customers for a luxury hotel chain and came across data showing that teens from a wealthy Middle Eastern country were frequent guests.

“There were a whole group of 17-year-olds staying at the properties worldwide,” Sullivan said. “We thought, ‘That can’t be true.’”¹¹

The data was dismissed as dirty data—messed up and worthless sets of information—before someone found out that, actually, it was true.

Brown teenagers, in this worldview, are likely to exist. Dead brown teenagers? Why not? But rich brown teenagers? This is so improbable that they must be dirty data and cleansed from your system! The pattern emerging from this operation to separate noise and signal is not very different from Rancière’s political noise filter for allocating citizenship, rationality, and privilege. Affluent brown teenagers seem just as unlikely as speaking slaves and women in the Greek polis.

On the other hand, dirty data is also something like a cache of surreptitious refusal; it expresses a refusal to be counted and measured:

A study of more than 2,400 UK consumers by research company Verve found that 60% intentionally provided wrong information when submitting personal details online. Almost one quarter (23%) said they sometimes gave out incorrect dates of birth, for example, while 9% said they did this most of the time and 5% always did it.¹²

Dirty data is where all of our refusals to fill out the constant onslaught of online forms accumulate. Everyone is lying all the time, whenever possible, or at least cutting corners. Not surprisingly, the “dirtiest” area of data collection is consistently pointed out to be the health sector, especially in the US. Doctors and nurses are singled out for filling out forms incorrectly. It seems that health professionals are just as unenthusiastic about filling out forms for systems designed to replace them as consumers are about performing clerical work for corporations that will spam them in return.

In his book The Utopia of Rules, David Graeber gives a profoundly moving example of the forced extraction of data. After his mom suffered a stroke, he went through the ordeal of having to apply for Medicaid on her behalf:

I had to spend over a month … dealing with the ramifying consequences of the act of whatever anonymous functionary in the New York Department of Motor Vehicles had inscribed my given name as “Daid,” not to mention the Verizon clerk who spelled my surname “Grueber.” Bureaucracies public and private appear—for whatever historical reasons—to be organized in such a way as to guarantee that a significant proportion of actors will not be able to perform their tasks as expected.¹³

Graeber goes on to call this an example of utopian thinking. Bureaucracy is based on utopian thinking because it assumes people to be perfect from its own point of view. Graeber’s mother died before she was accepted into the program.

The endless labor of filling out completely meaningless forms is a new kind of domestic labor in the sense that it is not considered labor at all and assumed to be provided “voluntarily” or performed by underpaid so-called data janitors.¹⁴ Yet all the seemingly swift and invisible action of algorithms—their elegant optimization of everything, their recognition of patterns and anomalies—is based on the endless and utterly senseless labor of providing or fixing messy data.

Dirty data is simply real data in the sense that it documents the struggle of real people with a bureaucracy that exploits the uneven distribution and implementation of digital technology.¹⁵ Consider the situation at LaGeSo (the Health and Social Affairs Office) in Berlin, where refugees are risking their health on a daily basis by standing in line outdoors in severe winter weather for hours or even days just to have their data registered and get access to services they are entitled to (for example money to buy food).¹⁶ These people are perceived as anomalies because, in addition to having had the audacity to arrive in the first place, they ask that their rights be respected. There is a similar political algorithm at work: people are blanked out. They cannot even get to the stage of being recognized as claimants. They are not taken into account.

On the other hand, technology also promises to separate different categories of refugees. IBM’s Watson AI system was experimentally programmed to potentially identify terrorists posing as refugees:

IBM hoped to show that the i2 EIA could separate the sheep from the wolves: that is, the masses of harmless asylum-seekers from the few who might be connected to jihadism or who were simply lying about their identities …

IBM created a hypothetical scenario, bringing together several data sources to match against a fictional list of passport-carrying refugees. Perhaps the most important dataset was a list of names of casualties from the conflict gleaned from open press reports and other sources. Some of the material came from the Dark Web, data related to the black market for passports; IBM says that they anonymized or obscured personally identifiable information in this set …

Borene said the system could provide a score to indicate the likelihood that a hypothetical asylum-seeker was who they said they were, and do it fast enough to be useful to a border guard or policeman walking a beat.¹⁷

The cross-referencing of unofficial databases, including dark-web sources, is used to produce a “score,” which calculates the probability that a refugee might be a terrorist. The hope is for a pattern to emerge across different datasets, without actually checking how or if they correspond to any empirical reality. This example is actually part of a much larger subset of “scores,” credit scores, academic ranking scores, scores ranking interaction on online forums, etc., which classify people according to financial interactions, online behavior, market data, and other sources. A variety of inputs are boiled down to a single number—a superpattern—which may be a “threat” score or a “social sincerity score,” as planned by Chinese authorities for every single citizen within the next decade. But the input parameters are far from being transparent 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