The Networked Image

I first picked up on
Google’s Image Labeler
two days ago (via).
In a nutshell, Image Labeler addresses a semiotic problem: the indexing of
hundreds of thousands of images based on semantic assignments in the visual
field of each image. Indexing an image depends upon the assignment of
keywords that correspond to the objects represented. Google Image Labeler
makes this process into a game of peer review: in this two person game, a player
win points by registering a descriptor that also appears on the other person’s
list.

Tracing

a

few
links (succumbing,
that is, to the beckoning of a surprising curiosity), I briefly started to follow the life
of this conversation in computer science and art. Most intriguing in this
regard was the talk embedded below, a talk called “Human Computation” given by Luis von Ahn at Carnegie
Mellon.


I find von Ahn’s talk fascinating on several levels. He explains gaming
(i.e., "Games with a Purpose") as a solution to the labor-production dilemma of
developing a gargantuan repository of indexed images, of reconciling the gap,
using the most basic set of terms, between image and word. Implicitly, he
sets up a way of thinking about "writing the image" as collaboration, as writing
that connects (in the allure of consensus, agreeing, that is, on the
equivalence of Bush’s photo and "yuck" (around 21:00 in the video)) and
produces
. In keeping with the title of his talk, he explains human
computation
–"Running a computation in people’s brains instead of silicon
processors."–premised upon "anonymous intimacy," the pleasure of coming to
terms with strangers about the verbal evoked in the encounter with the visual. He also refers to his
impressive research projects The ESP Game
and PeekaBoom, predecessors, it would
seem, to Google’s Image Labeler.

Is it going too far to invoke Barthes here? If not the studium, exactly,
there is something studium-like in Google’s Image Labeler. The image-index
undertaking is a project akin to establishing studium as a database. The
collaboration between a labeler and a validator (partners in the game) devalues
the intense singularity and instead reduces the image to human-generated
language agreements, its lexical mutuality. There is a networked quality to the
image in the way it is treatment here. Enigmatic thinking won’t win in this
game. The image is domesticated by the process and submitted into the most
generic realm of culture, which, as Barthes puts it, "is a contract
arrived at between creators and consumers" (28). It’s useful for image
searching, but is also has implications for habituated seeing and collaborative
image work.