The past is woven into it

Hito Steyerl interviewed by Marvin Jordan

Read the whole interview on Dis Magazine

[…] A while ago I met an extremely interesting developer in Holland. He was working on smart phone camera technology. A representational mode of thinking photography is: there is something out there and it will be represented by means of optical technology ideally via indexical link. But the technology for the phone camera is quite different. As the lenses are tiny and basically crap, about half of the data captured by the sensor are noise. The trick is to create the algorithm to clean the picture from the noise, or rather to define the picture from within noise. But how does the camera know this? Very simple. It scans all other pictures stored on the phone or on your social media networks and sifts through your contacts. It looks through the pictures you already made, or those that are networked to you and tries to match faces and shapes. In short: it creates the picture based on earlier pictures, on your/its memory. It does not only know what you saw but also what you might like to see based on your previous choices. In other words, it speculates on your preferences and offers an interpretation of data based on affinities to other data. The link to the thing in front of the lens is still there, but there are also links to past pictures that help create the picture. You don’t really photograph the present, as the past is woven into it.

The result might be a picture that never existed in reality, but that the phone thinks you might like to see. It is a bet, a gamble, some combination between repeating those things you have already seen and coming up with new versions of these, a mixture of conservatism and fabulation. The paradigm of representation stands to the present condition as traditional lens-based photography does to an algorithmic, networked photography that works with probabilities and bets on inertia. Consequently, it makes seeing unforeseen things more difficult. The noise will increase and random interpretation too. We might think that the phone sees what we want, but actually we will see what the phone thinks it knows about us. A complicated relationship — like a very neurotic marriage. I haven’t even mentioned external interference into what your phone is recording. All sorts of applications are able to remotely shut your camera on or off: companies, governments, the military. It could be disabled for whole regions. One could, for example, disable recording functions close to military installations, or conversely, live broadcast whatever you are up to. Similarly, the phone might be programmed to auto-pixellate secret or sexual content. It might be fitted with a so-called dick algorithm to screen out NSFW content or auto-modify pubic hair, stretch or omit bodies, exchange or collage context or insert AR advertisement and pop up windows or live feeds. Now lets apply this shift to the question of representative politics or democracy. The representational paradigm assumes that you vote for someone who will represent you. Thus the interests of the population will be proportionally represented. But current democracies work rather like smartphone photography by algorithmically clearing the noise and boosting some data over other. It is a system in which the unforeseen has a hard time happening because it is not yet in the database. It is about what to define as noise — something Jacques Ranciere has defined as the crucial act in separating political subjects from domestic slaves, women and workers. Now this act is hardwired into technology, but instead of the traditional division of people and rabble, the results are post-representative militias, brands, customer loyalty schemes, open source insurgents and tumblrs.

Zooming and travelling are two sides of the same coin

GG: […] Everything has to be precise in book-printing, you see. Now everything’s simply flat. Electronics is flat as a pancake: there are merely assertions that space exists.

EP: On the website of the archive, you have nevertheless managed to stretch this ‘flat’ space a great deal, particularly by using extreme photographic enlargements. The public is able to go so deeply into the object depicted that it appears to be something else, something different.

GG: This movement into the image is, for me, precisely the same as travelling out into the world. Zooming and travelling are two sides of the same coin. It’s a question of seeing through the conventions, of seeing one’s own culture from the outside – or trying to go as far as possible into it.

The Graphical Interface of the Archive, An Interview with Guttorm Guttormsgaard, Ellef Pretsaeter, in Forms of Modern Life, p210.

 

Out of focus

An actor begins to embody the distortions of his medium.

Interrogating the image

The spoken word interface emphasizes the active exploration and *interrogation* of the image. The noises and visual flash underscore how each step in the exploration results in the creation of a new image.

Image gradients

imagegradient
Image gradients are a fundamental transformation used in image processing, search indexing, and computer vision. Like a weather map showing the direction and strength of the wind, an image gradient depicts the strength and direction of changes in intensity over the surface of the image.

(more…)

Bindings

gguttorm_books-cut
Many books are depicted on the photos of the Guttormsgaard’s collection. Their view is standardized. The book is displayed wide-open. The binding occupies the centre of the image. The binding is the invisible element that distributes the symmetry between two pages.

This probe extracts the bindings of the books and display them side by side.

Canonical scale-space

gaussian-scales-wikipedia

 

The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. This implies that real-world objects, in contrast to idealized mathematical entities such as points or lines, may appear in different ways depending on the scale of observation. For example, the concept of a “tree” is appropriate at the scale of meters, while concepts such as leaves and molecules are more appropriate at finer scales. For a computer vision system analysing an unknown scene, there is no way to know a priori what scales are appropriate for describing the interesting structures in the image data. Hence, the only reasonable approach is to consider descriptions at multiple scales in order to be able to capture the unknown scale variations that may occur. Taken to the limit, a scale-space representation considers representations at all scales.[8]

Another motivation to the scale-space concept originates from the process of performing a physical measurement on real-world data. In order to extract any information from a measurement process, one has to apply operators of non-infinitesimal size to the data. In many branches of computer science and applied mathematics, the size of the measurement operator is disregarded in the theoretical modelling of a problem. The scale-space theory on the other hand explicitly incorporates the need for a non-infinitesimal size of the image operators as an integral part of any measurement as well as any other operation that depends on a real-world measurement.

There is a close link between scale-space theory and biological vision. Many scale-space operations show a high degree of similarity with receptive field profiles recorded from the mammalian retina and the first stages in the visual cortex. In these respects, the scale-space framework can be seen as a theoretically well-founded paradigm for early vision, which in addition has been thoroughly tested by algorithms and experiments.

Why a Gaussian filter?

When faced with the task of generating a multi-scale representation one may ask: could any filter g of low-pass type and with a parameter t which determines its width be used to generate a scale space? The answer is no, as it is of crucial importance that the smoothing filter does not introduce new spurious structures at coarse scales that do not correspond to simplifications of corresponding structures at finer scales. In the scale-space literature, a number of different ways have been expressed to formulate this criterion in precise mathematical terms.

The conclusion from several different axiomatic derivations that have been presented is that the Gaussian scale space constitutes the canonical way to generate a linear scale space, based on the essential requirement that new structures must not be created when going from a fine scale to any coarser scale.

http://en.wikipedia.org/wiki/Scale_space

Perception

“Perception begins where sensation changes,
whence the necessity of travel.” André Gide, Paludes