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Artificial Killing Machine – Visualizing U.S. military drone strikes

Created by Jonathan Fletcher Moore and Fabio Piparo, Artificial Killing Machine is an autonomous mechanical installation that uses the public database on U.S. military drone strikes to visualise deaths of individuals that would otherwise be represented purely as statistical data. When a drone strike occurs, the machine activates, and fires a children’s toy cap gun for every death that results. The raw information used by the installation is then printed. The materialised data is allowed to accumulate in perpetuity or until the life cycle of either the database or machine ends. A single chair is placed beneath the installation inviting the viewers to sit in the chair and experience the imagined existential risk.

This project consists of 15 toy cap guns and servo motors. Motor mounts were fabricated using laser cut acrylic and connected with custom hardware. The motors are driven by a 16-channel servo controller that is connected to a Raspberry Pi micro-controller using I2C serial connection. Three 7.4v lithium ion batteries and DC/DC step down converters deliver three regulated 5v outputs for the printer and controllers.

The control program was written as a web server, and the main logic was written in Python. The development took place in NY, and it is deployed remotely through the git DVCS. The software stack is: nginx, apache, and flask, and all the hardware was interfaced (by/for/with) Adafruit libraries. A publicly available database of U.S. drone strikes is being queried within a set interval of time, and when a new entry has been detected in the database, the motor control functions activate.

The data was collected, vetted, and organized by the Bureau of Investigative Journalism who have documented the U.S. covert drone war since 1999. Josh Begley created an open-source API in 2013 which makes this information available to artists, researchers, and the general public.

Project PageJonathan Fletcher Moore