Design Fiction
SMLT 3607A
Maintaining the future smart city with the Supervised Machine Learning Trainer 3607A
In the near future, cities are filled with smart infrastructure such as decentralized security cameras, self-sorting trashcans and intelligent street lights. But who do you call when smart things breaks? The future smart city is not a sci-fi dystopia made out of glass, concrete, and job stealing robots. It’s place much like our own and filled with the banality of everyday life and mundane jobs. Regardless of how you imagine the future smart city, someone needs to get in their white van, take out their ladder, and fix broken things.
We believe that mundane maintenance jobs are not just going to disappear when our cities adopt machine learning driven technology. Behind the algorithms and machines are human decisions and biases. We believe that a new class of blue collar jobs such as photo tagging and data set generation for machine learning algorithms will become prevalent.
The Supervised Machine Learning Trainer 3607A (SMLT-3607A) is a design fiction object aimed at exposing the humanness and mundaneness of the future smart city. Any maintenance person, regardless of familiarity with machine learning, can use the SMLT to interface with abnormally behaving smart infrastructure such as a surveillance camera. The SMLT is an industrial grade controller that allows a maintenance person to re-train the smart camera by recording new examples in real time. The future maintenance worker will teach the camera what it’s seeing and curate the training data set. He/she will help the camera learn the difference between people and objects and decide who should be classified as an upstanding citizen or a petty criminal.
Team: James Zhou, Keyur Jain, Benedict Hübener
The SMLT 3607A was a project at CIID from the course Work Intelligence taught by Simone Rebaudengo, Josh Noble, and Bjorn Karmann.
