Page 156 - Artificial Intelligence for the Internet of Everything

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performance and that we feed the resulting data streams to machine-learning
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algorithms designed to make the bridge “smart.”
To realize this vision, I and my colleagues built a simple prototype using
an existing bridge in our workshop in San Francisco. We applied sensors to
that structure, channeled the data output through the cloud, and brought to
bear a variety of computer vision and other advanced capabilities. The result
is a bridge that reports on its own use, publishing the number and, in an
upcoming v. 2, the position of its occupants, moment to moment.
Heroicallyassumingweovercomethemanyadditionaltechnicalchallenges
entailedinapplyingthesameapproachtoavastlymoreheavilytraffickedbridge
placed in an exposed, uncontrolled, outdoors setting largely populated by rois-
tering tourists, we will soon have a similarly capable bridge live in Amsterdam.
Thatcityofbridgeswillthenhaveunprecedentedinsightintohowoneofthose
bridges is used;the beginning, wepropose,ofa systemof data captureand anal-
ysis to be operated at city scale. We envision this “Internet of Big Things” as a
network of smart bridges generating status reports—n occupants at a point in
time t for bridge #1, bridge #2, bridge #3, and so on—to be interpolated into
staticandlongitudinalviewsshowingchangesinpatternsofusageforindividual
bridges and for entire neighborhoods or collections of neighborhoods. We
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anticipate an enthusiastic response from traffic engineers and city planners,
as well as private sector actors such as real estate investors.
This is a classic “smart cities” narrative, and like all such narratives there
is, or should be, an accompanying story of unintended consequences. We
are concerned about the possibility of this system being used for the ubiq-
uitous social and commercial surveillance of individuals: a high density of
sensors and suitable AI might be able to identify not just that people are using
a piece of infrastructure but also who those people are, what they are doing
and when, and even something about their physical attributes or personal
character, for example, using their gait data to determine if they have Par-
kinson’s disease, or analyzing tagged selfie capture to flag tourists versus local
users. These unintended consequences could scale to entire cities. Further-
more, our growing ability to squeeze more data out of systems of this sort,
for example, by oversampling the sensors or combining data streams, means
it is impossible to predict at what point precisely a system could be misused.
For an “open” project such as ours—by mandate and design, our bridge will

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For a quick background on the project see https://mx3d.com/smart-bridge/.
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I wrote on the topic of data sources for transportation demand modeling in 2013 (Shuldiner, A.T., &
Shuldiner, P.W. (2013). Transportation, 40: 1117. https://doi.org/10.1007/s11116-013-9490-5).