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Gary Edwards

What Google Learned From Its Quest to Build the Perfect Team - The New York Times - 0 views

  • 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 research scientist at M.I.T. 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.’’
  • ‘‘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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  • If a company wants to outstrip its competitors, it needs to influence not only how people work but also how they work together.
  • Five years ago, Google — one of the most public proselytizers of how studying workers can transform productivity — became focused on building the perfect team. In the last decade, the tech giant has spent untold millions of dollars measuring nearly every aspect of its employees’ lives. Google’s People Operations department has scrutinized everything from how frequently particular people eat together (the most productive employees tend to build larger networks by rotating dining companions) to which traits the best managers share (unsurprisingly, good communication and avoiding micromanaging is critical; more shocking, this was news to many Google managers).
  • In 2012, the company embarked on an initiative — code-named Project Aristotle — to study hundreds of Google’s teams and figure out why some stumbled while others soared. Dubey, a leader of the project, gathered some of the company’s best statisticians, organizational psychologists, sociologists and engineers. He also needed researchers. Rozovsky, by then, had decided that what she wanted to do with her life was study people’s habits and tendencies. After graduating from Yale, she was hired by Google and was soon assigned to Project Aristotle.
  • No matter how researchers arranged the data, though, it was almost impossible to find patterns — or any evidence that the composition of a team made any difference. ‘‘We looked at 180 teams from all over the company,’’ Dubey said. ‘‘We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference. The ‘who’ part of the equation didn’t seem to matter.’’
  • As they struggled to figure out what made a team successful, Rozovsky and her colleagues kept coming across research by psychologists and sociologists that focused on what are known as ‘‘group norms.’’ Norms are the traditions, behavioral standards and unwritten rules that govern how we function when we gather: One team may come to a consensus that avoiding disagreement is more valuable than debate; another team might develop a culture that encourages vigorous arguments and spurns groupthink. Norms can be unspoken or openly acknowledged, but their influence is often profound.
  • Team members may behave in certain ways as individuals — they may chafe against authority or prefer working independently — but when they gather, the group’s norms typically override individual proclivities and encourage deference to the team.
  • After looking at over a hundred groups for more than a year, Project Aristotle researchers concluded that understanding and influencing group norms were the keys to improving Google’s teams. But Rozovsky, now a lead researcher, needed to figure out which norms mattered most. Google’s research had identified dozens of behaviors that seemed important, except that sometimes the norms of one effective team contrasted sharply with those of another equally successful group. Was it better to let everyone speak as much as they wanted, or should strong leaders end meandering debates? Was it more effective for people to openly disagree with one another, or should conflicts be played down? The data didn’t offer clear verdicts. In fact, the data sometimes pointed in opposite directions. The only thing worse than not finding a pattern is finding too many of them. Which norms, Rozovsky and her colleagues wondered, were the ones that successful teams shared?
  • the researchers wanted to know if there is a collective I. Q. that emerges within a team that is distinct from the smarts of any single member.
  • What interested the researchers most, however, was that teams that did well on one assignment usually did well on all the others. Conversely, teams that failed at one thing seemed to fail at everything. The researchers eventually concluded that what distinguished the ‘‘good’’ teams from the dysfunctional groups was how teammates treated one another. The right norms, in other words, could raise a group’s collective intelligence, whereas the wrong norms could hobble a team, even if, individually, all the members were exceptionally bright.
  • As the researchers studied the groups, however, they noticed two behaviors that all the good teams generally shared.
  • First, on the good teams, members spoke in roughly the same proportion, a phenomenon the researchers referred to as ‘‘equality in distribution of conversational turn-taking.’’
  • On some teams, everyone spoke during each task; on others, leadership shifted among teammates from assignment to assignment. But in each case, by the end of the day, everyone had spoken roughly the same amount. ‘‘As long as everyone got a chance to talk, the team did well,’’ Woolley said. ‘‘But if only one person or a small group spoke all the time, the collective intelligence declined.’’
  • Second, the good teams all had high ‘‘average social sensitivity’’ — a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions and other nonverbal cues.
  • One of the easiest ways to gauge social sensitivity is to show someone photos of people’s eyes and ask him or her to describe what the people are thinking or feeling — an exam known as the Reading the Mind in the Eyes test. People on the more successful teams in Woolley’s experiment scored above average on the Reading the Mind in the Eyes test. They seemed to know when someone was feeling upset or left out.
  • People on the ineffective teams, in contrast, scored below average. They seemed, as a group, to have less sensitivity toward their colleagues.
  • But all the team members speak as much as they need to. They are sensitive to one another’s moods and share personal stories and emotions. While Team B might not contain as many individual stars, the sum will be greater than its parts.
  • Within psychology, researchers sometimes colloquially refer to traits like ‘‘conversational turn-taking’’ and ‘‘average social sensitivity’’ as aspects of what’s known as psychological safety — a group culture that the Harvard Business School professor Amy Edmondson defines as a ‘‘shared belief held by members of a team that the team is safe for interpersonal risk-taking.’’
  • Psychological safety is ‘‘a sense of confidence that the team will not embarrass, reject or punish someone for speaking up,
  • ‘‘It describes a team climate characterized by interpersonal trust and mutual respect in which people are comfortable being themselves.’’
  • Most of all, employees had talked about how various teams felt. ‘‘And that made a lot of sense to me, maybe because of my experiences at Yale,’’ Rozovsky said. ‘‘I’d been on some teams that left me feeling totally exhausted and others where I got so much energy from the group.’’
  • Rozovsky’s study group at Yale was draining because the norms — the fights over leadership, the tendency to critique — put her on guard.
  • Whereas the norms of her case-competition team — enthusiasm for one another’s ideas, joking around and having fun — allowed everyone to feel relaxed and energized.
  • For Project Aristotle, research on psychological safety pointed to particular norms that are vital to success. There were other behaviors that seemed important as well — like making sure teams had clear goals and creating a culture of dependability. But Google’s data indicated that psychological safety, more than anything else, was critical to making a team work.
  • the kinds of people who work at Google are often the ones who became software engineers because they wanted to avoid talking about feelings in the first place.
  • Rozovsky and her colleagues had figured out which norms were most critical. Now they had to find a way to make communication and empathy — the building blocks of forging real connections — into an algorithm they could easily scale.
  • They agreed to adopt some new norms: From now on, Sakaguchi would make an extra effort to let the team members know how their work fit into Google’s larger mission; they agreed to try harder to notice when someone on the team was feeling excluded or down.
  • But to Sakaguchi, it made sense that psychological safety and emotional conversations were related.
  • The behaviors that create psychological safety — conversational turn-taking and empathy — are part of the same unwritten rules we often turn to, as individuals, when we need to establish a bond. And those human bonds matter as much at work as anywhere else. In fact, they sometimes matter more.
  • What Project Aristotle has taught people within Google is that no one wants to put on a ‘‘work face’’ when they get to the office. No one wants to leave part of their personality and inner life at home. But to be fully present at work, to feel ‘‘psychologically safe,’’ we must know that we can be free enough, sometimes, to share the things that scare us without fear of recriminations.
  • We must be able to talk about what is messy or sad, to have hard conversations with colleagues who are driving us crazy. We can’t be focused just on efficiency. Rather, when we start the morning by collaborating with a team of engineers and then send emails to our marketing colleagues and then jump on a conference call, we want to know that those people really hear us. We want to know that work is more than just labor.
  • helping his team succeed ‘‘is the most meaningful work I’ve ever done,
  • He encourages the group to think about the way work and life mesh. Part of that, he says, is recognizing how fulfilling work can be.
  • Project Aristotle ‘‘proves how much a great team matters,’’ he said. ‘‘Why would I walk away from that? Why wouldn’t I spend time with people who care about me?’’
  • technology industry is not just one of the fastest growing parts of our economy; it is also increasingly the world’s dominant commercial culture.
  • The paradox, of course, is that Google’s intense data collection and number crunching have led it to the same conclusions that good managers have always known. In the best teams, members listen to one another and show sensitivity to feelings and needs.
  • Google, in other words, in its race to build the perfect team, has perhaps unintentionally demonstrated the usefulness of imperfection and done what Silicon Valley does best: figure out how to create psychological safety faster, better and in more productive ways.
  • ‘‘Don’t underestimate the power of giving people a common platform and operating language.’’
  • Project Aristotle is a reminder that when companies try to optimize everything, it’s sometimes easy to forget that success is often built on experiences — like emotional interactions and complicated conversations and discussions of who we want to be and how our teammates make us feel — that can’t really be optimized.
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    "Five years ago, Google - one of the most public proselytizers of how studying workers can transform productivity - became focused on building the perfect team. In the last decade, the tech giant has spent untold millions of dollars measuring nearly every aspect of its employees' lives. Google's People Operations department has scrutinized everything from how frequently particular people eat together (the most productive employees tend to build larger networks by rotating dining companions) to which traits the best managers share (unsurprisingly, good communication and avoiding micromanaging is critical; more shocking, this was news to many Google managers)."
Paul Merrell

Microsoft Pitches Technology That Can Read Facial Expressions at Political Rallies - 0 views

  • On the 21st floor of a high-rise hotel in Cleveland, in a room full of political operatives, Microsoft’s Research Division was advertising a technology that could read each facial expression in a massive crowd, analyze the emotions, and report back in real time. “You could use this at a Trump rally,” a sales representative told me. At both the Republican and Democratic conventions, Microsoft sponsored event spaces for the news outlet Politico. Politico, in turn, hosted a series of Microsoft-sponsored discussions about the use of data technology in political campaigns. And throughout Politico’s spaces in both Philadelphia and Cleveland, Microsoft advertised an array of products from “Microsoft Cognitive Services,” its artificial intelligence and cloud computing division. At one exhibit, titled “Realtime Crowd Insights,” a small camera scanned the room, while a monitor displayed the captured image. Every five seconds, a new image would appear with data annotated for each face — an assigned serial number, gender, estimated age, and any emotions detected in the facial expression. When I approached, the machine labeled me “b2ff” and correctly identified me as a 23-year-old male.
  • “Realtime Crowd Insights” is an Application Programming Interface (API), or a software tool that connects web applications to Microsoft’s cloud computing services. Through Microsoft’s emotional analysis API — a component of Realtime Crowd Insights — applications send an image to Microsoft’s servers. Microsoft’s servers then analyze the faces and return emotional profiles for each one. In a November blog post, Microsoft said that the emotional analysis could detect “anger, contempt, fear, disgust, happiness, neutral, sadness or surprise.” Microsoft’s sales representatives told me that political campaigns could use the technology to measure the emotional impact of different talking points — and political scientists could use it to study crowd response at rallies.
  • Facial recognition technology — the identification of faces by name — is already widely used in secret by law enforcement, sports stadiums, retail stores, and even churches, despite being of questionable legality. As early as 2002, facial recognition technology was used at the Super Bowl to cross-reference the 100,000 attendees to a database of the faces of known criminals. The technology is controversial enough that in 2013, Google tried to ban the use of facial recognition apps in its Google glass system. But “Realtime Crowd Insights” is not true facial recognition — it could not identify me by name, only as “b2ff.” It did, however, store enough data on each face that it could continuously identify it with the same serial number, even hours later. The display demonstrated that capability by distinguishing between the number of total faces it had seen, and the number of unique serial numbers. Photo: Alex Emmons
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  • Instead, “Realtime Crowd Insights” is an example of facial characterization technology — where computers analyze faces without necessarily identifying them. Facial characterization has many positive applications — it has been tested in the classroom, as a tool for spotting struggling students, and Microsoft has boasted that the tool will even help blind people read the faces around them. But facial characterization can also be used to assemble and store large profiles of information on individuals, even anonymously.
  • Alvaro Bedoya, a professor at Georgetown Law School and expert on privacy and facial recognition, has hailed that code of conduct as evidence that Microsoft is trying to do the right thing. But he pointed out that it leaves a number of questions unanswered — as illustrated in Cleveland and Philadelphia. “It’s interesting that the app being shown at the convention ‘remembered’ the faces of the people who walked by. That would seem to suggest that their faces were being stored and processed without the consent that Microsoft’s policy requires,” Bedoya said. “You have to wonder: What happened to the face templates of the people who walked by that booth? Were they deleted? Or are they still in the system?” Microsoft officials declined to comment on exactly what information is collected on each face and what data is retained or stored, instead referring me to their privacy policy, which does not address the question. Bedoya also pointed out that Microsoft’s marketing did not seem to match the consent policy. “It’s difficult to envision how companies will obtain consent from people in large crowds or rallies.”
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    But nobody is saying that the output of this technology can't be combined with the output of facial recognition technology to let them monitor you individually AND track your emotions. Fortunately, others are fighting back with knowledge and tech to block facial recognition. http://goo.gl/JMQM2W
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