Let me give you one example. 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.
Recognition by Manuel Fernández.
Manuel Fernández has created Recognition, an ongoing gif series which uses the face recognition software as pretext to generate a series of animated gif images emulating the process, trying to recognize faces on common objects without getting it, moving the subject away from its conventional use and deviating the attention to other kind of images.
“We have no idea, now, of who or what the inhabitants of our future might be. In that sense, we have no future. Not in the sense that our grandparents had a future, or thought they did. Fully imagined cultural futures were the luxury of another day, one in which ‘now’ was of some greater duration. For us, of course, things can change so abruptly, so violently, so profoundly, that futures like our grandparents’ have insufficient ‘now’ to stand on. We have no future because our present is too volatile. … We have only risk management. The spinning of the given moment’s scenarios. Pattern recognition.”
- William Gibson, Pattern Recognition
Too much information can be very dangerous because it can lead to a situation of meaninglessness; that is, people not having any basis for knowing what is relevant, what is irrelevant, what is useful, what is not useful… they live in a culture that is simply committed through all of its media to generate tons and tons of information every hour.
Limit your data-input to creators who are diligently seeking the whole truth: they are the people who are addressing issues from multiple angles, interacting with a wide variance of view points, and willing to admit when they are wrong. When they speak their opinions, they speak from a posture of informed and critical analysis–not controversy for the sake of page hits. Those are the people you and I need to follow. Those are the creators that deserve our attention.