A “Boy, I wish someone would do something about this” App.
The Rise of
the Urban Tattletale
Say
you spot a truck blocking a bike lane in San Francisco’s Mission
District. Using a new app called Safe
Lanes,
you can snap a picture of the offending vehicle’s license plate,
and beam it up to a constantly refreshing, GPS-coded map. Meanwhile,
Safe Lanes will take your image and run it through a license-plate
reader. Then, it will use the ID to automatically
fill out a complaint form
and submit it directly to the city’s non-emergency 311 service. If
you’re lucky, officials will respond swiftly, and the vehicle will
be towed, or the driver will be given a citation. You’ll
also get a list of all the previous traffic tickets and citations the
offending vehicle has earned.
The bike lane will be clear, someone will (hopefully) learn a
lesson, and you’ll get some satisfying closure that leaves a
positive—and potentially lifesaving—mark on society.
… But
the pictures of people’s license plates, and their location on a
geo-tagged map, don’t disappear once justice has been served: They
live on for anyone on the internet to see. And as other kinds of
citizen-powered crime-tracking form an ever-widening panopticon over
our urban space, the act of recording and displaying those data has
become more fraught.
Easy to get started, very hard to get right.
The shift
toward open source conversational AI
In
July, Uber
released a
new open source AI library called the Plato research dialogue system.
A couple of months ago, Cisco
open-sourced its MindMeld conversational AI platform,
after acquiring
the company of
the same name in 2017 for $125 million.
… In
fact, the whole field of AI has seen a strong shift toward open
source infrastructure in the past few years. The initial spark may
have been Google’s decision to open-source TensorFlow
in
2015. At that point, many businesses started paying attention.
Google Search data shows that interest in open source libraries like
TensorFlow and PyTorch
has
grown at the expense of closed platforms such as IBM Watson and
Amazon’s Sagemaker.
The
market is driving this shift. Companies
are increasingly deciding that many of the AI capabilities they need
are strategically important and should be developed in-house.
By using open source tools, they can build
up their own training data sets and other IP, such as
custom integrations with their backend systems. By developing the
talent, data, and software to ship AI themselves, these companies
control their own AI destiny.
… The
market for conversational AI is starting to mature, especially in the
banking, insurance, and health care industries. Bank of America’s
Erica
and
Capital One’s Eno
are
examples from leading banks that have built large teams to develop
conversational AI. Challenger startups like Lemonade
and
N26
are
on the path to building autonomous organizations, which becomes
possible as the industry moves from level
three conversational AI to level five.
Another
chapter
The
Ethics of Artificial Intelligence in Law: Basic Questions
22 Pages Posted: 26 Aug 2019 Harry
Surden University of Colorado Law School
The
only cheating I encourage my students to try.
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