privacy – BENJAMIN DE KOSNIK

Alpha60 Archival Forms

a60-3-end a60-1-open a60-0-summer-stack

Installation, variable size. With six 8.5 x 11″ bound objects (100-600 pages), a combination of maps on washi (22 x 17″), and 1080p video (1-4 minutes).

2017, 2018, 2019, ongoing

An installation of artifacts from a research project exploring internet piracy as an instantiation of Nam June Paik’s Video Common Market. Due to the numerous technical and legal obstacles in studying peer to peer sharing behavior, the experimental data is anonymized and then materialized in archival art forms. These objects encapsulate the ephemeral nature of BitTorrent phenomena on the internet, guarding the privacy of individual internet users while providing media and communication scholars with accurate records of television circulation on the internet.

Alpha60
Nam June Paik Timeline
Alpha60 3D-Gun Files One Year (pdf)
Alpha60 Detective Chinatown 3 First 15 Weeks (pdf)

Valentine Homography

Two Channel Video Art Installation. Two 34 inch LED televisions, wall-mounted to be touching in landscape orientation, two 19:44 minute 1080p synchronized loops playing a metadata composition generated by combining portrait photography, tumblr and pinterist social media image scrapes, and computer vision software.

2015

Experiments with color contour detection in images, using the SURF homography algorithm in the Open Source Computer Vision (OpenCV) software library. I apply this algorithm as a Trevor Paglen-defined seeing machine to social media images: profile portraits, liked images, and disliked images. This computational lens simulates computer matchmaking in a visual form that is but one instance in a sea of many thousand “known-good” or positive test cases that the machine learning behind websites such as Facebook, Tindr, Grindr, OkCupid, Match.com, Jdate.com, etc. must crunch to create a single user’s match.

To create this positive test case, I collaborated with my real-life partner to stage ten normcore portraits counter to the prevailing profile portraiture aesthetic, collected forty images from each of our preferred social medias that were positive, and ten that were negative. These input images were used by the machine learning system to simulate what a positive match “looks like” when using its “match the humans” algorithm.

Why Algorithm

The Machine Is Learning The Man Trap

Week One

Week Two

Week Three

3 weeks, 2 LED displays, articulating wall mounts, pink-filtered sunlight, 8 media files. Dimensions H x L x W (6′ x 6′ x 1′).

2014

Durational video installation. Displayed videos are generated from a computational media project that uses facial and object recognition technology to create new characters and situations in classic media texts.

Two screens are mounted on a wall, slightly pointed inwards so that the shadows of the screens create eye-shaped shadows. Each screen shows a loop, which is changed every week. As the weeks progress, “the machine” leans more about detecting and recognizing faces: simultaneously recognizing more characters with greater accuracy, and yet at the same time negating the human form with an algorithmic representation of the covered face. By the last week, all faces are replaced with faceless humans with an accumulation of algorithmic depictions.

Ten Thoughts on Detection and Recognition
All the Uhuras on Paper
Notes on The Machine Is Learning
The Disquiet Artist Rant
Machine Learning Initial Statement