#art
Participants were to compute, by hand, all the single steps required by a Perceptron to complete its goal: identify and separate two classes of objects. The participants were embodying the neural network through all the necessary steps toward the completion of a single data point and the overall architecture. This provided an opportunity to understand and deconstruct the underlying logic behind Laborious Computing. Participants used simple objects, for instance chairs and tables, decided collectively of the metrics to elaborate the two distinct classes, calculated the separator until the two classes were separated, and injected new objects with unconventional forms and dimensions, to get some errors from the system. During the process, participants understood the reductionism of the real which is at stake in computational logic, and the inherent biases that infuse the process. Each object has to be classified using a very limited number of parameters, which doesn’t do justice to their inherent complexity. The workshop addresses also the importance of training, annotating, and cleaning data, the importance of clean data. By going through all the steps, the possibility of an error that could challenge the process become obvious, due to the world greater complexity and unpredictability. Last, doing everything by hand, underlines the laborious nature of the work, the division at stake.
For us from an artistic perspective, using the body as a recording medium was the most interesting part. Fatigue and repetition inscribed directly the experience onto the body of the participants. The never-ending duration taken by a single operation of classification, made it even more memorable. Like the naming of Laborious Computing, the workshop aims to re-embody the computational logic.
Reacting to these developments we set up another artistic system in 2019, that took advantage of Amazon Mechanical Turk, a reenactment of Kempelen’s Mechanical Turk, where human players were invited to play against a worker on Amazon MTurk. We called it: AAI Chess.[58] When we made our MTurk Chronomatograph [Fig. 8, shown above], we were using the platform as workers, executing tasks. But this time for AAI Chess, we used it as a ‘requester,’ meaning, as an employer.
During a residency in Pact Zollverein, Essen, while working on the project ‘Human Computers’, RYBN subscribed to the Amazon MTurk platform and recorded the movement of the keyboard and the mouse of several tasks, visualizing them using a method inspired by Gilbreth’s chronocyclegraph.