By Tristan Greene
Facebook volunteers and work-at-home moms might be making city planning decisions, thanks to AI research conducted by MIT scientists. Researchers from MIT’s media lab have been feeding computers a steady stream of data for the last four years to build an AI capable of determining why some cities grow and others decay.
The data the researchers are using has been compiled from people, regular Joes and Janes, who choose between two randomly selected pictures to determine which one seems less dangerous or more appealing. Currently it’s all common-sense driven: most of us would agree a typically beautiful environment will foster growth better than a landscape of derelict buildings.
Finally, with enough data, the AI has been returning results — which have been compared with human responses to the same image pairings. The researchers ‘proofed’ their data by comparing responses from Amazon Mechanical Turk workers. According to MIT the robots got it right a little more than 70% of the time, which was better than expected. In the future MIT researchers plan on increasing the number of people contributing data, going so far as to say they may need to advertise on Facebook to draw more participants.
At first-glance it doesn’t sound very impressive – they’re just feeding data into an algorithm by hand based on thousands of different human interpretations. People decide which Google Map image in a pair looks like a nicer neighborhood, scientists determine if the machine agrees, and vice-versa.
The ultimate goal is for us to glean insight into our problems by learning what the machines can teach us about ourselves. Professor César Hidalgo, the director of the Collective Learning group at the MIT Media Lab, told Co.Design:
I do hope that this research starts helping us understand how the urban environment affects people and how it’s affected by people so that when we do policy in the context of urban planning, we have a more scientific understanding of the effect different designs have in the behaviors of the populations that use them
Until the machines learn to define beauty for themselves – which is a scary thought – we’ll need to explain to them why one city’s streets are lined with despair and another shows the promise of growth and renewal. Once AI is up to speed, however, we’ll be able to start saving dying communities with machine-learning. Computers can draw exponentially more patterned-based conclusions than humans.
The better we can understand an issue, the more connections we can determine — and the better our solutions will be. Thanks to MIT we may be on the verge of solving decades-old urban rejuvenation problems.
Tristan Greene is a sailor gleefully writing about living on dry land. He’s been known to brag about his Rockband 2 scores.
Featured Image Credits: Unsplash.
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