If
the answer is yes, should it be mandatory?
Can
tracking people through phone-call data improve lives?
After
an earthquake tore through Haiti in 2010, killing more than 100,000
people, aid agencies spread across the country to work out where the
survivors had fled. But Linus Bengtsson, a graduate student studying
global health at the Karolinska Institute in Stockholm, thought he
could answer the question from afar. Many Haitians would be using
their mobile phones, he reasoned, and those calls would pass through
phone towers, which could allow researchers to approximate people’s
locations. Bengtsson persuaded Digicel, the biggest phone company in
Haiti, to share data from millions of call records from before and
after the quake. Digicel replaced the names and phone numbers of
callers with random numbers to protect their privacy.
Bengtsson’s
idea worked. The analysis wasn’t completed or verified quickly
enough to help people in Haiti at the time, but in 2012, he and his
collaborators reported that the population of Haiti’s capital,
Port-au-Prince, dipped by almost one-quarter soon after the quake,
and slowly rose over the next 11 months1. That result aligned with
an intensive, on-the-ground survey conducted by the United Nations.
… At
least 20 mobile-phone companies have donated their proprietary
information to such efforts, including operators in 100 countries
that back an initiative called Big Data for Social Good, sponsored by
the GSMA, an international mobile-phone association. Cash to support
the studies has poured in from the UN, the World Bank, the US
National Institutes of Health and the Bill & Melinda Gates
Foundation in Seattle, Washington. Bengtsson co-founded a non-profit
organization in Stockholm called Flowminder
that
crunches massive call data sets with the aim of saving lives.
Yet
as data-for-good projects gain traction, some researchers are asking
whether they benefit society enough to outweigh their potential for
misuse.
Resource.
Privacy
and Cybersecurity June 2019 Events
June
25-26 National Association of College and University Attorneys
Bret
Cohen and Stephanie Gold are presenting at the annual conference of
the National Association of College and University Attorneys on the
panel, “Focus on GDPR and Other Privacy Laws: How to Develop and
Implement a Practical Approach to Compliance.” Bret is also
presenting on the panel, “Navigating GDPR Compliance for Research.”
Location:
Denver, Colorado
They
certainly don’t want to be caught violating
privacy.
How
the CIA is Working to Ethically Deploy Artificial Intelligence
As
the Central Intelligence Agency harnesses machine learning and
artificial intelligence to better meet its mission, insiders are
aggressively addressing issues around bias and ethics intrinsic to
the emerging tech.
“We
at the agency have over 100 AI initiatives that we are working on and
that’s going to continue to be the case,” Benjamin Huebner, the
CIA’s privacy and civil liberties officer said Friday at an event
hosted by the Brookings Institution in Washington.
… “One
of the interesting things about machine learning, which is an aspect
of our division of intelligence, is [experts] found in
many cases the analytics that have the most accurate results, also
have the least explainability—the least ability to
explain how the algorithm actually got to the answer it did,” he
said. “The algorithm that’s pushing that data out is a black box
and that’s a problem if you are the CIA.”
The
agency cannot just be accurate, it’s also got to be able to
demonstrate how it got to the end result. So if an analytic isn’t
explainable, it’s not “decision-ready.”
Interesting
look at the economic impact of AI.
Artificial
intelligence, the future of work, and inequality
by
Daniele Tavani, Colorado
State University
… One
of the most important economic thinkers of all time, John Maynard
Keynes, wrote in his 1930 essay "The Economic Possibilities for
our Grandchildren" that by the 21st century we could fulfill our
needs and wants with a 15 hours workweek and devote the rest of our
lives to non-monetary pursuits. Fast-forward to 2014, when the late
physicist Stephen Hawking told the BBC that "artificial
intelligence could spell the end of the human race."
… Economists
have debated the effect of technology and automation on jobs for a
long time. The first set of questions regards labor displacement and
whether there is any future for work at all. The second set of
questions has to do with how automation impacts income and wealth
inequality.
… According
to the MIT economist David Autor, between 1989 and 2007 job creation
has occurred mostly in low-paying and high-paying jobs, while
middle-class jobs were affected by job destruction on net.
We
need to think about “things.”
The
Internet Of Things Is Powering The Data-Driven Fourth Industrial
Revolution
The
Fourth Industrial Revolution is data-driven. And a primary reason
for this is the rise of the internet of things (IoT). Connected
devices from the consumer level to the industrial are creating—and
consuming—more data than ever before. Last year, IoT
devices outnumbered the world's population for
the first time, and by 2021, Gartner
predicts
that one million new IoT devices will be purchased every
hour.
In
this Extreme Data Economy, businesses, governments, and organizations
need to analyze and react to IoT data simultaneously, in real time.
This requires continuous analysis of streaming and historical data,
location analysis, and predictive analytics using AI and machine
learning.
Good
definitions.
Top
5 Programming Languages For Machine Learning
Machine
learning has
been defined
by Andrew Ng,
a computer
scientist
at Stanford University, as “the
science of getting computers to act without being explicitly
programmed.”
It was first conceived in the 1950s, but experienced limited
progress until around the turn of the 21st century. Since then,
machine learning has been a driving force behind a number of
innovations, most notably artificial
intelligence.
Machine
learning can be broken down into several categories, including
supervised,
unsupervised,
semi-supervised
and
reinforcement
learning.
While supervised learning relies on labeled
input data in
order to infer its relationships with output results, unsupervised
learning detects patterns among unlabeled
input data.
Semi-supervised learning employs a combination of both methods, and
reinforcement learning motivates programs to repeat or elaborate on
processes with desirable outcomes while avoiding errors.
[The
languages are: Python, R, JavaScript, C++, and Java.
Another
delivery option. (Also reducing unemployment in Columbia?)
Kiwibots
win fans at UC Berkeley as they deliver fast food at slow speeds
… Four-wheeled,
cooler-size Kiwibots are a familiar sight at UC Berkeley as they
ferry burritos, Big Macs and bubble tea to students. They’re
social media stars, their pictures posted on Instagram, Snapchat and
Facebook. Some students dressed up as them for Halloween. After one
caught fire due to a battery issue, students held a candlelight vigil
for it.
… The
Kiwibots do not figure out their own routes. Instead, people in
Colombia, the home country of Chavez and his two co-founders, plot
“waypoints” for the bots to follow, sending them instructions
every five to 10 seconds on where to go.
As
with other offshoring arrangements, the labor savings are huge. The
Colombia workers, who can each handle up to three robots, make less
than $2 an hour, which is above the local minimum wage.
Another
cost saving is that human assistance means the robots don’t need
pricey equipment such as lidar sensors to “see” around them.
Manufactured in China and assembled in the U.S., Kiwibots cost only
about $2,500 each, Iatsenia said.
I
really have trouble understanding the “big is evil” mindset. I’m
much more an “evil is evil” kind of guy.
The
Justice Department is preparing a potential antitrust investigation
of Google
… The
exact focus of the Justice Department’s investigation is unclear.
The department began work on the matter after brokering an agreement
with the government’s other antitrust agency, the Federal Trade
Commission, to take the lead on antitrust oversight of Google,
according to the people familiar with the matter, who spoke on the
condition of anonymity because the deliberations are confidential.
… Its
expansive, data-hungry footprint increasingly has drawn the attention
of Democrats and Republicans on Capitol Hill, who say that Google —
and some of its peers in Silicon Valley — have become too large and
should potentially be broken up. [Would
that reduce data collection? Do anything for consumers? Bob]
A
package for our Web Development students.
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