Alaz Okudan

Our Collective Noise (OCN): A Tactical Response to Computer Vision, Surveillance, and Noise

Our Collective Noise (OCN) is a research-based tactical media project. As an attempt to transform the top-down pervasive qualities of machine learning (ML), computer vision (CV), and surveillance technologies into a bottom-up tactical tool, it plays around the concepts of noise, de-identification, accidental aesthetics, and human-machine collaboration. 

OCN, as an offline system, uses live webcam feed, ML, and CV to detect people and simultaneously turn them into coarse pixels to replace the common aim of precise identification in surveillance technologies with anonymity. Coarse pixels are constantly stitched together to create collective abstract human-machine interaction patterns that people are collectively and unidentifiably part of. 

In a world where thriving ML, CV, and AI (artificial intelligence) technologies increasingly rely on cleaner datasets, higher processing capacities, precise labels and categories, OCN turns the technology against itself, in pursuit of revealing the latent potential in noise, anonymity, and collective action.
https://github.com/AlazOkudan/Our-Collective-Noise-OCN-

Biography

Alaz Okudan is a researcher and an artist with a background in photography, media, and visual studies. Alaz is interested in hidden and neglected stories from the history of visual technologies. 

His current interests are in slow media and poor images. He enjoys looking at infra-ordinary aspects of life that lie beneath the threshold of everyday attention. His ongoing PhD investigation at the University of Galway’s Centre for Creative Technologies focuses on accidents and failures of visual generative AI. He experiments with analogue and digital forms of image-making. 

He lives in Galway, Ireland and roams the streets of the city.