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Tag: big data

Object Hierophanies and the Mode of Anticipation

As I posted earlier, I am participating in a panel on data natures at the International Symposium on Electronic Art [ISEA] in Hong Kong. My paper is titled Object Hierophanies and the Mode of Anticipation, and discusses the transition of bid data-driven IoT objects such as the Amazon Echo to a mode of operation where they appear as a hierophany – after Mircea Eliade – of a higher modality of being, and render the loci in which they exist into a mode of anticipation.

I start with a brief section on the logistics of the IoT, focusing on the fact that it involves physical objects monitoring their immediate environments through a variety of sensors, transmitting the acquired data to remote networks, and initiating actions based on embedded algorithms and feedback loops. The context data produced in the process is by definition transmitted to and indexed in a remote database, from the perspective of which the contextual data is the object.

The Amazon Echo continuously listens to all sounds in its surroundings, and reacts to the wake word Alexa. It interacts with its interlocutors through a female sounding interface called the Alexa Voice Service [AVS], which Amazon made available to third-party hardware makers. What is more, the core algorithms of AVS, known as the Alexa Skills Kit [ASK] are opened to developers too, making it easy for anyone to teach Alexa a new ‘skill’. The key dynamic in my talk is the fact that human and non-human agencies, translated by the Amazon Echo as data, are transported to the transcendental realm of the Amazon Web Services [AWS] where it is modulated, stored for future reference, and returned as an answering Echo. In effect, the nature of an IoT enabled object appears as the receptacle of an exterior force that differentiates it from its milieu and gives it meaning and value in unpredictable ways.

Objects such as the Echo acquire their value, and in so doing become real for their interlocutors, only insofar as they participate in one way or another in remote data realities transcending the locale of the object. Insofar as the data gleaned by such devices has predictive potential when viewed in aggregate, the enactment of this potential in a local setting is always already a singular act of manifestation of a transcendental data nature with an overriding level of agency.

In his work on non-modern notions of sacred space philosopher of religion Mircea Eliade conceptualized this act of manifestation of another modality of being into a local setting as a hierophany. Hierophanies are not continuous, but wholly singular acts of presence by a different modality. By manifesting that modality, which Eliade termed as the sacred, an object becomes the receptacle for a transcendental presence, yet simultaneously continues to remain inextricably entangled in its surrounding milieu. I argue that there is a strange similarity between non-modern imaginaries of hierophany as a gateway to the sacred, and IoT enabled objects transducing loci into liminal and opaque data taxonomies looping back as a black-boxed echo. The Echo, through the voice of Alexa, is in effect the hierophanic articulator of a wholly non-human modality of being.

Recently, Sally Applin and Michael Fischer have argued that when aggregated within a particular material setting sociable objects form what is in effect an anticipatory materiality acting as a host to human interlocutors. The material setting becomes anticipatory because of the implied sociability of its component objects, allowing them to not only exchange data about their human interlocutor, but also draw on remote data resources, and then actuate based on the parameters of that aggregate social memory.

In effect, humans and non-humans alike are rendered within a flat ontology of anticipation, waiting for the Echo.

Here is the video of my presentation:

And here are the prezi slides:

Thinking the value of social data

I have been thinking a lot lately about the underlying dynamics of big data, and how most discussions around online privacy and surveillance are functions of absent or simplistic taxonomies for social data. This is a lecture I gave last week to a 100-level convergent media class, where I tried to synthesize these ideas in a more or less coherent package illustrating the dynamics. I start with a list of Pompeii graffiti, which look surprisingly similar to tweets, illustrating two features of social data: we generate a lot of it on the go, and it tends to outlast the context for which it was generated. I then move through artifacts such as Raytheon’s RIOT software, the numbers on big data and and the way they bring forward the notion of flow management, the FinFisher spy software, and the Camover anti-surveillance game. I end with Bruce Schneier’s proposal for a working social data taxonomy.

Disruptive Media

This is a Prezi from a guest lecture on Disruptive Media I gave last week. The argument is organized around four concept provocations – artificial scarcity, big data, iFeudals, and hive-mind. Each provocation in turn is centered around a digital artifact, where my artifact zero is raw footage from the war in Syria, featuring a tank column dash-cam and a blurry recording from a rebel group attacking the same tank column. I then disassemble this artifact into the four conceptual threads structuring the provocations – the deluge of raw data, the absence/irrelevance of gatekeepers, the inability of content farms [legacy media] to deal with raw data, the power of the hivemind [reddit/4chan] to aggregate and process raw data. These being provocations I don’t provide any summary or an encapsulating framework; instead my coda is a quote from Gabe Newell that really resonated with me. It is from his keynote at the D.I.C.E. summit this year in which he charted a future for gaming built around open auction houses, free-to-play, and user generated content [watch it here], but it equally applies to the scenarios I describe: ‘We can’t compete with our own customers. Our customers have defeated us, not by a little, but by a lot [and that’s a good thing].’

Towards a Taxonomy of Social Networking Data

Bruce Schneier has posted over at his blog the following draft of a social networking data taxonomy:

  • Service data is the data you give to a social networking site in order to use it. Such data might include your legal name, your age, and your credit-card number.
  • Disclosed data is what you post on your own pages: blog entries, photographs, messages, comments, and so on.
  • Entrusted data is what you post on other people’s pages. It’s basically the same stuff as disclosed data, but the difference is that you don’t have control over the data once you post it — another user does.
  • Incidental data is what other people post about you: a paragraph about you that someone else writes, a picture of you that someone else takes and posts. Again, it’s basically the same stuff as disclosed data, but the difference is that you don’t have control over it, and you didn’t create it in the first place.
  • Behavioral data is data the site collects about your habits by recording what you do and who you do it with. It might include games you play, topics you write about, news articles you access (and what that says about your political leanings), and so on.
  • Derived data is data about you that is derived from all the other data. For example, if 80 percent of your friends self-identify as gay, you’re likely gay yourself.

Why is this important? Because in order to develop ways to control the data we distribute in the cloud we need to first classify precisely the different types of data and their relational position within our digital footprint and the surrounding ecology. Disclosed data is of different value to Behavioral or Derived data, and most people will likely value their individual content such as pictures and posts much more than the aggregated patterns sucked out of their footprint by a social network site’s algorithms. Much to think about here.