Machine Vision

Machines are becoming intelligent agents that read, organise and valorise images while taking decisions and performing actions that mirror the human. In ‘Machine Vision’, we examine ways of seeing generated through the computational apparatus.

From the mechanical eyes of drones, GoPro cameras or the indexed options of search engines, the ways that we conceive the world today are commonly affected by a machine’s decisive moment. But what are the politics behind machine optics and what are the challenges for photographic culture under conditions of algorithmic governance?

"A Google Street View car in Los Angeles once captured a picture of Leonard Cohen. It happened a couple of years before he died. He was sitting with an acquaintance on lawn chairs outside his modest home in the Mid-Wilshire neighbourhood. The driver was an accidental paparazzi. Cohen didn’t even notice him. (...) Google Street View isn’t …
For the past six years Heather Dewey-Hagborg has been researching, writing and producing artwork engaging the methodology of ‘forensic DNA phenotyping’. In this essay, she explores a different aspect of this technology and questions: is forensic DNA phenotyping a photographic process?
These (in)security cameras are all around us. In our streets, shops, buses, restaurants, homes. []
“The truth is written all over our faces” was a tagline for Lie to Me, a procedural drama on network television several years ago.
“The relation between what we see and what we know is never settled.”
A net-based work created entirely by algorithms that have been automatically collecting images of six surveillance cameras placed on the US/Mexico Border from 2011 until 2014.
The steadiness and endurance of the camera’s gaze produces the strong sense that the camera is something other than an extension of the eye: it is a sensor, a monitor, a machine for being with and in the world.
Photography of the Internet extends to unprecedented social fields and challenges of social norms for questioning cultural, economic and ethical values of photos circulating within the networks.
Our shares and likes, our annotations and social metadata are training a generation of AI agents. Everyday, we are already all teaching bots and algorithms how to look at images. If we consider the extent of our relationship with algorithms, we realise the magnitude of the effort of teaching and learning that is taking place.