Using face detection and a psuedo-genetic algorithm (not strictly GA because there is no crossover), software engineer Louis Brandy (who now works for Facebook) created a script that detects faces in grey-scale noise and selects the best results leading towards a more face-like image.
To me, it seems that to begin charting a course forward, we have to develop an expanded definition of what we mean when we’re talking about “photography.” With a nod to Paul Virilio, I propose a simple definition that has far-reaching consequences: seeing machines.
Seeing machines is an expansive definition of photography. It is intended to encompass the myriad ways that not only humans use technology to “see” the world, but the ways machines see the world for other machines. (…) Crucially, the definition of photography I’m proposing here encompasses imaging devices (“cameras” broadly understood), the data (“images” being one possible manifestation of that data) they produce, and the seeing-practices with which they are enmeshed.
Last week’s workshop on Algorithmes Créatifs in Stereolux (Nantes) was based on the development of two systems complex enough to display rich and unexpected phaenomena. One was based in Vectorfields and the mathematical notions of rotational and divergence. The other used a spring-based DLA system to “grow” and complete the drawings made with the mouse. Step-by-step code can be found here:
"When we realize that coding is a creative act, we not only value that part of the coder’s labor, but we also realize that the technologies in which we swim have assumptions and ideologies behind them that, perhaps, we should challenge."
Michael Widner in “First Stanford code poetry slam reveals the literary side of computer code”, Mariana Lage (2013)