In her recent article for e-flux “A Sea of Data: Apophenia and Pattern (Mis-)Recognition”, Hito Steyerl takes on the subject of data. Or more precisesly she focuses on the ‘truckloads of data’ and ‘sea of data’ produced as a result of our daily use of information technologies and collected through marketing, spying or other data tracking progammes. The need to translate all life into signals that can be en/de/coded, and carried by machines is emblematic of our times. However, as Steyerl suggests, all this dataification does not make extracting of meaningful information easier but rather it produces new conditions where ‘the focus moves from acquisition to discerning, from scarcity to overabundance, from adding on to filtering, from research to pattern recognition.‘ Her provocation is not that data analysis is difficult in these conditions, but that in order to make sense of this sea of data its extraction into meaning takes place through apophenia, that is a tendency to derive meaning from seeing/recognising patterns in random data. As we constantly struggle to distinguish signal from noise and to make sense of terrabytes of data, the only way to deal with it is to look for patterns. To visualise this we are given the image of data titled The Secret extracted from Snowden files while being challenged to see in it the sea of data itself: ‘An overwhelming body of water, which one could drown in? Can you see the waves moving ever so slightly?’ asks Steyerl.
Apophenia is a sensation related to delusion and feeling of paranoia. According to Wikipedia definition, aphophenia was first defined in research on schizophrenia and psychotic behaviour where delusion is experienced as revelation. What Steyerl suggests is that the information we are able to extract from data is in fact only a perception and not the real thing connected to other real things (other than the brain and its individual ability to see patterns that no one else can see, for example). Apophenia and related to it pattern recognition, is an attempt at predicting the world that is seen as unpredictable, and out of control, and where the signal is accompanied by noise.
The connection between signal and noise in data, where the latter has to be separated as so called dirty data, is described by making a reference to what and how has been defined as public or private. In antiquity it was men whose voices that were worth listening to; their value confirmed by their status of citizens. Other voices of women, children, slaves, foreigners were ‘irrelevant, irrational and potentially dangerous nuisances’ that were considered noise in the life of a citizen. And so Steyerl argues, ‘pattern recognition resonates with the wider question of political recognition.’
Dirty data is what does not fit in algorithmic operations where the desired result is to distinguish between a refugee and a terrorist, or even in such mundane operation as finding a consumer interested in buying a fridge from that interested in buying a washing machine. Dirty data is what is not welcome and what has been already excluded. Yet such segregations do not get rid of dirty data but produce freaks and deep dreaming monsters, and Steyerl gives example of Google inceptionism (creating pattern out of dirty data or noise) as manifesting ‘current technological disposition’ where ‘the networked operations of computational image creation, certain presets of machinic vision, its hardwired ideologies and preferences’ are revealed.
And so the question is how does it relate to intimacy? What about data and intimacy? Intimacy assumes a certain degree of knowledge of, and closeness to another (body, thing,..). Can we remain intimate with our data once our affective relations with the world are quantified and networked in the sea of data. Can we stay close to it, by accessing it if and when we want? And how can we read it, derive meaning from it? The question of intimacy remains. Can we develop intimate relations with deep dreaming monsters, with our dirty data?