Popular Press

The Tech That Tracks Your Movement at Work
Ryan Derousseau. 6/14/2017. “The Tech That Tracks Your Movement at Work.” BBC. Publisher's VersionAbstract

Do you feel as if there’s always someone watching you at work?

You might be right: the way companies monitor employees has broadened beyond simply requiring workers to tap in and out of an office building. Advances in technology and a hunger for data have now created a market for devices that can measure workers’ movements, fitness and even sleep – all in the name of productivity.

Take Humanyze, for example, a start-up based in Boston, Massachusetts, which supplies companies with employee ID badges replete with inbuilt biometric measuring capabilities.

A plethora of tech within the badges tracks everything from movements and interactions around the office, to lengths of conversations, and even voice tone. CEO Ben Waber told Techworld earlier this year that microphones within the badges can process vocal information to detect whether a person dominates conversations, as well as tone, volume and speed of speech.

With these, the company aims to change the traditional role of management consultants in the workplace. According to Humanyze, these “people analytics,” can help measure everything from how often workers are disrupted, to the effectiveness of diversity and inclusion programmes.

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Inside Google’s culture of relentless self-surveying
Tim Fernholz. 6/26/2013. “Inside Google’s culture of relentless self-surveying.” Quartz. Publisher's VersionAbstract

When Google recently admitted that the baffling brainteasers it posed to interviewees were utterly useless at predicting which ones would make good employees, it was another example of the power of what Google calls “people analytics”—the mixing of Big Data with management science to come up with smarter ways to work.

The company’s obsession with human data is perhaps best known for producing the rule that no employee should sit more than 150 feet (46 meters) away from a micro-kitchen, and that in those kitchens the chocolate M&Ms be kept in opaque jars while healthier food is in clear containers, to encourage healthy eating habits. Google’s often controversial culture of omniscience about its users is mirrored, inside its posh campuses, by a team of industrial-organizational psychologists, behavioral economists, consultants and statisticians who survey and experiment with Google’s staff.

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There will be little privacy in the workplace of the future
3/28/2018. “There will be little privacy in the workplace of the future”.Abstract

Walk up a set of steep stairs next to a vegan Chinese restaurant in Palo Alto in Silicon Valley, and you will see the future of work, or at least one version of it. This is the local office of Humanyze, a firm that provides “people analytics”. It counts several Fortune 500 companies among its clients (though it will not say who they are). Its employees mill around an office full of sunlight and computers, as well as beacons that track their location and interactions. Everyone is wearing an ID badge the size of a credit card and the depth of a book of matches. It contains a microphone that picks up whether they are talking to one another; Bluetooth and infrared sensors to monitor where they are; and an accelerometer to record when they move.

“Every aspect of business is becoming more data-driven. There’s no reason the people side of business shouldn’t be the same,” says Ben Waber, Humanyze’s boss. The company’s staff are treated much the same way as its clients. Data from their employees’ badges are integrated with information from their e-mail and calendars to form a full picture of how they spend their time at work. Clients get to see only team-level statistics, but Humanyze’s employees can look at their own data, which include metrics such as time spent with people of the same sex, activity levels and the ratio of time spent speaking versus listening.

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University of Arizona tracks student ID cards to detect who might drop out
Shannon Liao. 3/12/2018. “University of Arizona tracks student ID cards to detect who might drop out.” The Verge. Publisher's VersionAbstract

The University of Arizona is tracking freshman students’ ID card swipes to anticipate which students are more likely to drop out. University researchers hope to use the data to lower dropout rates. (Dropping out refers to those who have left higher-education entirely and those who transfer to other colleges.)

The card data tells researchers how frequently a student has entered a residence hall, library, and the student recreation center, which includes a salon, convenience store, mail room, and movie theater. The cards are also used for buying vending machine snacks and more, putting the total number of locations near 700. There’s a sensor embedded in the CatCard student IDs, which are given to every student attending the university.

“By getting their digital traces, you can explore their patterns of movement, behavior and interactions, and that tells you a great deal about them,” Sudha Ram, a professor of management information systems who directs the initiative, said in a press release.

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How the Eagles Followed the Numbers to the Super Bowl
Ben Shpigel. 2/2/2018. “How the Eagles Followed the Numbers to the Super Bowl.” The New York Times. Publisher's VersionAbstract

The most illuminating moment of the Eagles’ enchanted season was a Week 3 play ridiculed in Philadelphia but celebrated here by a small cadre of people who recognized its significance almost immediately.

What fueled the excitement among members of the EdjSports crew was not the outcome of the play — a 6-yard sack of Carson Wentz on fourth-and-8 that gifted the Giants good field position — but rather the call itself. Leading by 7-0 on the Giants’ 43-yard line a few minutes before halftime, the Eagles opted not to punt.

By keeping Philadelphia’s offense on the field in a situation almost always played safe in the risk-averse N.F.L., Coach Doug Pederson did not buck conventional wisdom so much as roll his eyes at it.

An intern at EdjSports, responding to a flurry of text messages from his colleagues about the play, ran the numbers at home. The Eagles, by going for it, improved their probability of winning by 0.5 percent. Defending his decision (again) at a news conference the next day, Pederson cited that exact statistic.

 

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Artificial Intelligence's 'Black Box' Is Nothing to Fear
Vijay Pande. 1/25/2018. “Artificial Intelligence's 'Black Box' Is Nothing to Fear.” The New York Times. Publisher's VersionAbstract

Alongside the excitement and hype about our growing reliance on artificial intelligence, there’s fear about the way the technology works. A recent MIT Technology Review article titled “The Dark Secret at the Heart of AI” warned: “No one really knows how the most advanced algorithms do what they do. That could be a problem.” Thanks to this uncertainty and lack of accountability, a report by the AI Now Instituterecommended that public agencies responsible for criminal justice, health care, welfare and education shouldn’t use such technology.

Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a “black box” — seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the “dark” about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that’s seemingly impossible — and certainly too complicated — for us to understand.

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Meet Your New Boss: An Algorithm
Sam Schechner. 12/10/2017. “Meet Your New Boss: An Algorithm.” The Wall Street Journal. Publisher's VersionAbstract

Uber Technologies Inc. and other pioneers of the so-called gig economy became some of the world’s most valuable private companies by using apps and algorithms to hand out tasks to an army of self-employed workers. Now, established companies like Royal Dutch Shell PLC and General Electric Co. are adopting elements of that model for the full-time workforce.

Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others.

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What Is Code?
Paul Ford. 6/11/2015. “What Is Code?”. Publisher's VersionAbstract

Why Are We Here?

We are here because the editor of this magazine asked me, “Can you tell me what code is?”

“No,” I said. “First of all, I’m not good at the math. I’m a programmer, yes, but I’m an East Coast programmer, not one of these serious platform people from the Bay Area.”

I began to program nearly 20 years ago, learning via oraperl, a special version of the Perl language modified to work with the Oracle database. A month into the work, I damaged the accounts of 30,000 fantasy basketball players. They sent some angry e-mails. After that, I decided to get better.

Which is to say I’m not a natural. I love computers, but they never made any sense to me. And yet, after two decades of jamming information into my code-resistant brain, I’ve amassed enough knowledge that the computer has revealed itself. Its magic has been stripped away. I can talk to someone who used to work at Amazon.com or Microsoft about his or her work without feeling a burning shame. I’d happily talk to people from Google and Apple, too, but they so rarely reenter the general population.

The World Wide Web is what I know best (I’ve coded for money in the programming languages Java, JavaScript, Python, Perl, PHP, Clojure, and XSLT), but the Web is only one small part of the larger world of software development. There are 11 million professional software developers on earth, according to the research firm IDC. (An additional 7 million are hobbyists.) That’s roughly the population of the greater Los Angeles metro area. Imagine all of L.A. programming. East Hollywood would be for Mac programmers, West L.A. for mobile, Beverly Hills for finance programmers, and all of Orange County for Windows.

There are lots of other neighborhoods, too: There are people who write code for embedded computers smaller than your thumb. There are people who write the code that runs your TV. There are programmers for everything. They have different cultures, different tribal folklores, that they use to organize their working life. If you told me a systems administrator was taking a juggling class, that would make sense, and I’d expect a product manager to take a trapeze class. I’ve met information architects who list and rank their friendships in spreadsheets. Security research specialists love to party.

What I’m saying is, I’m one of 18 million. So that’s what I’m writing: my view of software development, as an individual among millions. Code has been my life, and it has been your life, too. It is time to understand how it all works.

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They're Watching You at Work
Don Peck. 12/2013. “They're Watching You at Work.” The Atlantic. Publisher's VersionAbstract

What happens when Big Data meets human resources? The emerging practice of "people analytics" is already transforming how employers hire, fire, and promote.

in 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.

That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.

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What Google Learned From Its Quest to Build the Perfect Team.
2/25/2016. “What Google Learned From Its Quest to Build the Perfect Team.” The New York Times. Publisher's VersionAbstract
Our data-saturated age enables us to examine our work habits and office quirks with a scrutiny that our cubicle-bound forebears could only dream of. Today, on corporate campuses and within university laboratories, psychologists, sociologists and statisticians are devoting themselves to studying everything from team composition to email patterns in order to figure out how to make employees into faster, better and more productive versions of themselves. ‘‘We’re living through a golden age of understanding personal productivity,’’ says Marshall Van Alstyne, a professor at Boston University who studies how people share information. ‘‘All of a sudden, we can pick apart the small choices that all of us make, decisions most of us don’t even notice, and figure out why some people are so much more effective than everyone else.’’

Yet many of today’s most valuable firms have come to realize that analyzing and improving individual workers ­— a practice known as ‘‘employee performance optimization’’ — isn’t enough. As commerce becomes increasingly global and complex, the bulk of modern work is more and more team-based. One study, published in The Harvard Business Review last month, found that ‘‘the time spent by managers and employees in collaborative activities has ballooned by 50 percent or more’’ over the last two decades and that, at many companies, more than three-quarters of an employee’s day is spent communicating with colleagues.

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