Data / Set / Match
On the Media Wall
Data / Set / Match is a year-long programme seeking new ways to present, visualise and interrogate contemporary image datasets.
Departing from traditional 19th and 20th century taxonomies used to organise and store images, we will be looking at the effect that digital technology has had on these systems, and how new categorisations increasingly influence the way humans and machines see and understand the world today.
The first project in this series uses ImageNet as a focus for exploration. Launched in 2009 by Stanford University Professor, Li Fei-Fei, ImageNet has become one of the most important visual datasets for machine learning and image categorisation. Throughout the year, we will question meaning making in computational culture and focus on the data that underpins the image algorithms of today, including image sets designed to help self-driving cars ‘see’; facial recognition systems that recognise and track targets, and new forms of art generation through neural networks.