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?

Domestic (in)security

These (in)security cameras are all around us. In our streets, shops, buses, restaurants, homes. They are looking, listening and recording. Their omnipresent gaze pierces, protects us, keeps us safe. Through them we are delivered from unknown evils, from the nameless things that might creep up on us if we don’t watch out. Through their panopticon gaze we are quantified, verified, analysed, documented, mapped, filtered and judged.

Listening to a Face

“The truth is written all over our faces” was a tagline for Lie to Me, a procedural drama on network television several years ago.

Ways of Machine Seeing

“The relation between what we see and what we know is never settled.”

AZ: MOVE AND GET SHOT

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.

Through Non-Human Eyes

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.

Internet Photography

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.

“The cat sits on the bed”, Pedagogies of vision in human and machine learning.

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.