Tag: archontic production

Mueller (Report + Manafort Response) Redactions Printed With Invisible Ink

Smoke, burn scars, lemon juice, hope on paper with all-over large-format laser debossing. Open set of 3 with “Enso/Zero” burn monoprints one white print, on 300g/msq white velvet somerset paper, 30 by 44 inches (760mm x 1120mm). With Emily York and Courtney Sennish.

2022

In 2019, a heavily-redacted version of the Mueller Report was made public. On the thirteenth of May this year, another heavily-redacted addendum was made public as part of the Paul Manafort sentencing, the Mueller Manafort Response.

If you take both of these documents, add them together, and shrunk every page down such that 486 pages fit on a 44 x 30 inch sheet of paper, it’d look like the black and white image below. Reading left to right, row one to two, etc. Every raised bar is redacted text.

Hey Bob Mueller, I cannot read this.

We stand on the shoulders of giants.
Profound thanks to Alex Thompson at Pagoda Arts, Emily York and Courtney Sennish at Crown Point Press, Kate Randall (invisible ink, burn art history, burn safety), Ian Roxbourough (combustion).

Agnes Martin,
John Cage(1, 2),
Nam June Paik,
Birgit Skiöld,
Nance O’Banion,
Jenny Holzer,
Cai Guo-Qiang

Marihuana Smokes Herself

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Three LED displays, wall mounts, 3 media files, 2 lights, 2 framed photos, 2 cloth pots, one rug, one sofa, two freestanding tumbleweed sculptures. Dimensions H x L x W (9′ x 9′ x 9′).

2016

Three channel video installation featuring a supercut of clips extracted from film and television depicting cannabis use, production, and distribution. My practice is to watch media and note any cannabis on the screen, as if I were a media censor in a time and space of total cannabis prohibition. Over time,this process has created a growing archive of clips that depict cannabis as a character in the larger war on drugs. The sequenced clips in this video installation use this archive, but only the parts that contain a female character on the screen. This subset is further  shaped by new searches gathered from exit interviews, and then using these terms to create subsequent loops with suggested searches such as “giggling,” power and money, dealing, actual medical use, and others.

Artist Statement
Loop Component Detail/Audit

All the Uhuras

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Inkjet, lapis and silvertone wash, bamboo paper, paste. Dimensions for each are H x L (68 cm x 86.5 cm).

2014

Two prints, composed of 288 cropped images of Uhura from the television show Star Trek. Each frame of the first season is analyzed with facial recognition software, and found Uhura faces are either inscribed with tattoo-like circles representing individual facial detection algorithms, or scaled, cropped, and center-aligned via sophisticated image-processing routines. To offset the explicitly computed nature of this work, the images are aligned on broken grids, and floated on an organic background of silvertone metallic or lapis mineral pigments. With Emily York.

Machine is Learning
Equal Weight Uhuras

Equal Weight Uhuras

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Two to six channel video art installation. Two to six 34 inch LED televisions, wall-mounted adjacent to each other in landscape orientation, synchronized 19:44 minute 1080p loops.

2015

Samples six characters from two seasons of Star Trek episodes, and re-constructs two seasons in a condensed form, where all the characters have the same amount of “screen time” as the character Uhura, with the added proviso that all the characters are shown talking equally to each other in a random fashion, or conversing alone with a representation of space.

Asama Loops

Benjamin De Kosnik - Asama Loops

Asama Loop 6 Roji (left), 2015
3:15 minutes, 1920 x 1080

Asama Loop 6 Vsync 8 (right), 2015
5:13 minutes, 1920 x 1080

Asama Loop 6 Vband 4 (left), 2015
1:00 minutes, 1920 x 1080

Asama Loop 6 Pink Buddah Dot (right), 2015
5:13 minutes, 1920 x 1080

Two-channel video art installation, size variable. One or two 34 inch LED televisions, wall-mounted in portrait orientation to be adjacent or line-of-sight, two 54:00 minute 1080p synchronized loops.

2015

A site-specific video art installation, inspired by artist books produced by foreign artists visiting Japan: Kathan Brown (Paradise, 1982), John Cage (Silence, 1961), and Birgit Skiold (Zen Gardens, 1973). This work combines the transnational print and video aesthetics of three countries into a new hybrid form. Not wishing to be bound to a fixed form on paper, a visual machine constantly creates new video combinations from three main sources: the garden landscapes posted on social media at the Nezu Museum in Japan, three pure colors, and multiplications of pure color with printing plate engraving marks and noise. The generated images are re-inscribed from paper to a video screen with the carousel swipes of the contemporary mobile web mixed with simulated vertical rolls and glitch found in experimental video art by Nam June Paik and Andy Warhol. The result is a combinatoric, ever-changing iconography machine spanning contemporary San Francisco, Tokyo, and London.

Asama OG

The Machine Is Learning The Man Trap

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Week One

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Week Two

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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