Saturday, June 01, 2019


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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