Machine Readable Photography
Today images are published by millions on the Internet daily. And these constitute only a fraction of all the images that are produced and archived. Computer Vision algorithms are designed to make sense of this sheer mass of visual content. They play a central role in the management of image traffic on the networks, as well as in the preparation of diagnosis in medicine or in analysing never ending footage of surveillance imagery (1). They rank, filter, predict, decide (2). They censor (3) and dream (4). They have learnt to recognise faces (5) and generate them (6) or avoid them (7). In driving systems, they learn to recognise pedestrians, avoid obstacles and solve moral dilemmas (8). Vision systems allow robots to interact with the environment (9). Embedded in phones, cameras, webcams and spectacles, they influence how images are shot and processed. The new photographer's studio is made of algorithms (10) as well as spotlights and tripods. Algorithmic curators share their tastes with amateurs of photography (11), compose their catalogues (12) and their conservative ideas of beauty cause an uproar in beauty contests (13).
Algorithms learn from humans what images are and translate this knowledge into new images (14). In Unthinking Photography, we explore machine vision through digital art commissions (15), interviews (16), debates (17), workshops (18), performances, geekenders (19), etc. We invite artists, photographers, scientists, thinkers and makers to challenge, criticise, play with vision algorithms and software. As machines are learning how to see, UTP proposes activities for ourselves relearning how to see in their company.
— Nicolas Malevé
Nicolas Malevé, visual artist, computer programmer and data activist, lives and works between Brussels and London. Nicolas is currently working on a PhD thesis on the algorithms of vision at London South Bank University. He is a member of Constant and the Scandinavian Institute for Computational Vandalism. In the Active Archives project, with Michael Murtaugh, he is experimenting with techniques to engage with large collections of visual materials and explore different ways to navigate and question them.