[...] As citizens lived their lives, data was continuously harvested and funnelled into city hall and private sector partners where it was analysed for insight into how the city could be run more efficiently — or used to develop services and products for sale.
Now, that data infrastructure is being repurposed. “We are reversing the smart city paradigm,” says Bria. “Instead of starting from technology and extracting all the data we can before thinking about how to use it, we started aligning the tech agenda with the agenda of the city.”
The ACLU and a coalition of civil rights groups are calling on Amazon chief Jeff Bezos to stop offering Rekognition facial detection system to government customers after learning that the company is actively helping law enforcement implement the potentially invasive technology. Police in multiple regions have partnered with Amazon on surveillance projects, including an Orlando proof-of-concept that lets Amazon search for "people of interest" through city cameras as well a Washington County, Oregon initiative that lets officers scan people to see if they turn up in a mugshot database.
The new ubiquity of these devices [wearable devices] has “raised concerns,” as the social scientists Gina Neff and Dawn Nafus write in their recent book Self-Tracking—easily the best book I’ve come across on the subject—“about the tremendous power given to already powerful corporations when people allow companies to peer into their lives through data.” But the more troubling sorts of wearables are those used by companies to monitor their workers directly. This application of ubiquitous computing belongs to a field called “people analytics,” or PA, a name made popular by Alex “Sandy” Pentland and his colleagues at MIT’s Media Lab.
A Portland family contacted Amazon to investigate after they say a private conversation in their home was recorded by Amazon's Alexa -- the voice-controlled smart speaker -- and that the recorded audio was sent to the phone of a random person in Seattle, who was in the family’s contact list.
Since last year I’ve had a smart speaker in my living room—an Echo Dot. My family uses it mostly to ask Amazon’s digital assistant, Alexa, to play music. But after I saw a report that an Alexa-enabled speaker owned by a family in Portland, Oregon, had recorded a conversation and sent it to a contact, I started wondering: what is it picking up on at my house when we’re not talking to it directly?
So I checked my Alexa history (you can do that through the “settings” portion of the Amazon Alexa smartphone app) to see what kinds of things it recorded without my knowledge.
That’s when the hairs on the back of my neck started to stand up.
People actually pay to have spy devices at home, and then they're surprised when they spy on them.
If you find it hard to predict which songs are destined for pop-chart success and which will flop, try asking a computer.
After analyzing the attributes of more than half a million songs released over a period of 30 years, a computer algorithm was able to sort the successful songs from also-rans with an accuracy of up to 86%.
A team of mathematicians from UC Irvine described how — and why — it accomplished this feat in a study published in Wednesday's edition of the journal Royal Society Open Science.
As China’s online population has boomed, human censors have been overwhelmed with an ever-growing wave of online content. So companies such as iQiyi, a top video-streaming platform, are turning to machine learning to filter content Beijing wants to ban.
The California bill, B.O.T. Act of 2018 (S.B. 1001), would make it unlawful for any person to use a social bot to communicate or interact with natural persons online without disclosing that the bot is not a natural person.
The Department of Defense is funding a project that will try to determine whether the increasingly real-looking fake video and audio generated by artificial intelligence might soon be impossible to distinguish from the real thing—even for another AI system.
Data Links is a periodic blog post published on Sundays (specific time may vary) which contains interesting links about data science, machine learning and related topics. You can subscribe to it using the general blog RSS feed or this one, which only contains these articles, if you are not interested in other things I might publish.
Have you read an article you liked and would like to suggest it for the next issue? Just contact me!