SMLT3607A: painting the humaneness and mundaneness of the future smart city

Design Fiction

In the near future, cities are filled with smart infrastructures such as decentralized security cameras, self-sorting trashcans, and intelligent street lights. But who do you call when smart things break? The future smart city is not a sci-fi dystopia made out of glass, concrete, and job stealing robots. It's a 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.

Painting a picture of the smart city

In order to create a believable future, we looked at "weak signals" of present cities. We spoke to city planners and visited a street where the City of Copenhagen is testing smart city technologies. We conducted secondary research and looked at papers and articles on smart cities. All of the signals suggest that the future smart city will be filled with cameras and machine learning algorithms.

As result, we imagine that the future smart cities will be filled with surveillance cameras that are context-aware and have machine vision capabilities. Instead of relying on humans to monitor the security footage and identify issues, the surveillance camera can flag behaviours or people. For example, a surveillance camera might be trained to identify cyclists, pedestrians, etc.. Or it might be trained to identify a fist fight or someone who is littering. When the surveillance camera sees something prohibited, it would automatically alert the appropriate authorities. But because machine vision is based on robust datasets, having a well-crafted dataset is important. We imagine the future city maintenance person will be asked to maintenance both the infrastructure and the integrity of the dataset.

How does the SMLT-3607A work?

City infrastructures break all the time. The smart surveillance camera is no exception. The SMLT-3607A helps the city maintenance person fix and retrain misbehaving surveillance cameras. To use the SMLT-3607A a city maintenance person plugs the kit into the camera. He/she would monitor the footage and see what the camera is miss labeling. Then the maintenance person needs to select the class that is being mislabeled. He/she pressed the record example button every time the camera sees the correct person or object or action. The recorded example is then added to the dataset of the algorithm. Eventually, after many correct examples in the dataset, the surveillance camera will be able to identify correctly.

The Design of the SMLT-3607A

The interface and operation of the SMLT-3607A are deliberately simple and repetitive. We believe that in the future, machine learning technology is going to be normalized. The repetitive task of dataset capturing and labeling is going to be a new blue collar job. The SMLT-3607A will be a tool that would allow anyone to interface and work with machine learning technology.

At the moment the SMLT-3607A exists as a prop with an embedded Arduino. The screen is an iPhone controlled by a laptop. To continue with the project, we would hook up all of the buttons and knobs to a WIFI enabled Arduino. The Arduino would send all of the input signals to the machine learning software Wekinator. The Wekinator allows us to access machine learning capabilities and output to some kind of Java or Processing sketch.

As designers, we have the ability to craft a piece of the future so that people can touch and feel it. We can make "Knotty Objects" that represent and hint at larger systems. We created the SMLT-3607A so that we can shift the conversation around automation and jobs. We want to people to talk about how we can co-exist with automation and machine learning and how we can embrace a future with artificial intelligence.

The project was carried out in team with Benedict Hubener and James Zhou as a project for the Working Intelligence course. Mentored by Simone Rebaudengo, Josh Noble, and Bjorn Karmann.

ROLE

Prototyping, Storytelling