Category: Labor Economics

Amazon Will Fund Employees to Quit and Found Delivery Startups

(p. B6) First, Amazon made two-day shipping the norm. Now, as it aims to cut that to a single day, the company is encouraging its employees to quit and start their own delivery businesses.

Under a new incentive program, announced on Monday, Amazon said that it would fund up to $10,000 in start-up costs and provide three months of pay to any employee who decides to make the jump.

The new incentives build on a program the company started last June to encourage anyone, employee or not, to get into the competitive business of last-mile package delivery.

“We’ve heard from associates that they want to participate in the program but struggled with the transition,” Dave Clark, senior vice president for worldwide operations, said in a statement. “Now we have a path.”

For the full story, see:

Niraj Chokshi. “Amazon Has A Novel Idea For Delivery.” The New York Times (Tuesday, MAY 14, 2019): B6.

(Note: the online version of the story has the date MAY 13, 2019, and has the title “Amazon Will Pay Workers to Quit and Start Their Own Delivery Businesses.”)

Facebook Hires More Humans to Do What Its AI Cannot Do

(p. B5) If telling us what to look at next is Facebook’s raison d’être, then the AI that enables that endless spoon-feeding of content is the company’s most important, and sometimes most controversial, intellectual property.

. . .

At the same time, the company’s announcement that it is hiring more humans to screen ads and filter content shows there is so much essential to Facebook’s functionality that AI alone can’t accomplish.

AI algorithms are inherently black boxes whose workings can be next to impossible to understand—even by many Facebook engineers.

For the full commentary, see:

Christopher Mims. “KEYWORDS; The Algorithm Driving Facebook.” The Wall Street Journal (Monday, October 23, 2017): B1 & B5.

(Note: ellipses added.)

(Note: the online version of the commentary has the date Oct. 22, 2017, and the title “KEYWORDS; How Facebook’s Master Algorithm Powers the Social Network.”)

Much of the “Intelligence” in Artificial Intelligence Is Human, Not Artificial

(p. B5) Everything we’re injecting artificial intelligence into—self-driving vehicles, robot doctors, the social-credit scores of more than a billion Chinese citizens and more—hinges on a debate about how to make AI do things it can’t, at present.

. . .

On one side of this debate are the proponents of “deep learning”—an approach that, since a landmark paper in 2012 by a trio of researchers at the University of Toronto, has exploded in popularity.

. . .

On the other side of this debate are researchers such as Gary Marcus, former head of Uber Technologies Inc.’s AI division and currently a New York University professor, who argues that deep learning is woefully insufficient for accomplishing the sorts of things we’ve been promised. It could never, for instance, be able to usurp all white collar jobs and lead us to a glorious future of fully automated luxury communism.

Dr. Marcus says that to get to “general intelligence”—which requires the ability to reason, learn on one’s own and build mental models of the world—will take more than what today’s AI can achieve.

“That they get a lot of mileage out of [deep learning] doesn’t mean that it’s the right tool for theory of mind or abstract reasoning,” says Dr. Marcus.

To go further with AI, “we need to take inspiration from nature,” say Dr. Marcus. That means coming up with other kinds of artificial neural networks, and in some cases giving them innate, pre-programmed knowledge—like the instincts that all living things are born with.

. . .

Until we figure out how to make our AIs more intelligent and robust, we’re going to have to hand-code into them a great deal of existing human knowledge, says Dr. Marcus. That is, a lot of the “intelligence” in artificial intelligence systems like self-driving software isn’t artificial at all. As much as companies need to train their vehicles on as many miles of real roads as possible, for now, making these systems truly capable will still require inputting a great deal of logic that reflects the decisions made by the engineers who build and test them.

For the full commentary, see:

Christopher Mims. “KEYWORDS; Should Artificial Intelligence Copy the Brain?” The Wall Street Journal (Saturday, October 26, 2017): B5.

(Note: ellipses added.)

(Note: the online version of the commentary has the same date as the print version, and has the title “KEYWORDS; Should Artificial Intelligence Copy the Human Brain?”)

Big, Frequent Meetings Are Unproductive and Crowd Out Deep Thought

(p. 7) To figure out why the workers in Microsoft’s device unit were so dissatisfied with their work-life balance, the organizational analytics team examined the metadata from their emails and calendar appointments. The team divided the business unit into smaller groups and looked for differences in the patterns between those where people were satisfied and those where they were unhappy.

It seemed as if the problem would involve something about after-hours work. But no matter how Ms. Klinghoffer and Mr. Fuller crunched the data, there weren’t any meaningful correlations to be found between groups that had a lot of tasks to do at odd times and those that were unhappy. Gut instincts about overwork just weren’t supported by the numbers.

The two kept iterating until something emerged in the data. People in Mr. Ostrum’s division were spending an awful lot of time in meetings: an average of 27 hours a week. That wasn’t so much more than the typical team at Microsoft. But what really distinguished those teams with low satisfaction scores from the rest was that their meetings tended to include a lot of people — 10 or 20 bodies arrayed around a conference table coordinating plans, as opposed to two or three people brainstorming ideas.

The issue wasn’t that people had to fly to China or make late-night calls. People who had taken jobs requiring that sort of commitment seemed to accept these things as part of the deal. The issue was that their managers were clogging their schedules with overcrowded meetings, reducing available hours for tasks that rewarded more focused concentration — thinking deeply about trying to solve a problem.

Data alone isn’t insight. But once the Microsoft executives had shaped the data into a form they could understand, they could better question employees about the source of their frustrations. Staffers’ complaints about spending evenings and weekends catching up with more solitary forms of work started to make more sense. Now it was clearer why the first cuts of the data didn’t reveal the problem. An engineer sitting down to do individual work for several hours on a Saturday afternoon probably wouldn’t bother putting it on her calendar, or create digital exhaust in the form of trading emails with colleagues during that time.

Anyone familiar with the office-drone lifestyle might scoff at what it took Microsoft to get here. Does it really take that much analytical firepower, and the acquisition of an entire start-up, to figure out that big meetings make people sad?

For the full story, see:

Neil Irwin. “How to Win at Winner-Take-All.” The New York Times, SundayBusiness Section (Sunday, June 15, 2019): 1 & 6-7.

(Note: the online version of the story has the date June 15, 2019, and has the title “The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy.”)

The article quoted above, is adapted from:

Irwin, Neil. How to Win in a Winner-Take-All World: The Definitive Guide to Adapting and Succeeding in High-Performance Careers. New York: St. Martin’s Press, 2019.

Robots Relieve Restaurant Workers of Small, Mundane, Tedious Tasks

(p. A1) John Miller, chief executive and founder of CaliBurger LLC, finds it harder to find employees these days. His solution is Flippy, a robot that turns the burgers and cleans the hot, greasy grill.

The chain plans to install Flippy in up to ten of its 50 restaurants by year end. CaliBurger doesn’t intend to kick humans to the curb as a result. Flippy will handle the gruntwork, freeing employees to tidy the dining rooms and refill drinks, less arduous work that might make it easier to recruit and retain workers.

“We’re a long way from teaching a robot to walk the restaurant and do those things,” Mr. Miller said.

Experts have warned for years that robots will replace humans in restaurants. Instead, a twist on that prediction is unfolding. Amid the lowest unemployment in years, fast-food restaurants are turning to machines—not to get rid of workers, but because they can’t find enough.

. . .

(p. A10) Dunkin’ conducted focus groups with former employees to pinpoint the mundane tasks that made them want to leave and geared automation around that.

Workers used to create thousands of hand-written labels daily for everything from coffee to cheese expirations. Last year, Dunkin’ installed small terminals that print out expiration times.

Brewing a single pot involved grinding and weighing coffee and comparing its fineness and coarseness to a perfect sample. Now, some Dunkin’ shops use digital refractometers to determine if coffee meets specifications.

. . .

Alexandra Guajardo, the morning shift leader at a Dunkin’ Donuts shop in Corona, Calif. said she’s likely to stick with the job longer now than she otherwise would have.

“I don’t have to constantly be worried about other smaller tasks that were tedious,” she said. “I can focus on other things that need my attention in the restaurant.”

Mr. Murphy said he can’t see a time when a Dunkin’ Donuts shop is fully automated. The company experimented with a robot barista nearly two years ago at an innovation lab in Massachusetts. The robot did fine at making simple drinks, but couldn’t grasp custom orders, such as “light sugar.”

The machine also required a lot of cleaning and maintenance, and at up to $100,000 per robot, Mr. Murphy said he couldn’t see a return on the investment.

For the full story, see:

Julie Jargon and Eric Morath. “Short of Workers, Robots Man the Grill.” The Wall Street Journal (Monday, June 25, 2018): A1 & A10.

(Note: ellipses added.)

(Note: the online version of the story has the date June 24, 2018, and the title “Short of Workers, Fast-Food Restaurants Turn to Robots.”)

More Workers Satisfied with Jobs Than in Recent Years

(p. B6) Just more than half of U.S. workers—51%—said they were satisfied with their jobs in 2017, the highest level since 2005, according to a new report from The Conference Board, a business-research group.

Over the past seven years, Americans report feeling better about their pay along with a greater sense of job security, both features of an economy with a low unemployment rate and a long decline in layoffs. In July [2018], jobless claims continued an extended post-recession slide and hit their lowest level in nearly 50 years.

For the full story, see:

Weber, Lauren. “Fewer Workers Move for New Jobs.” The Wall Street Journal (Thursday, August 30, 2018): B6.

(Note: bracketed year added.)

(Note: the online version of the story has the date Aug. 29, 2018, and the title “Fewer Americans Uproot Themselves for a New Job.”)

Job-Related Relocations Declining

(p. A1) Fewer U.S. workers are moving around the country to seek new job opportunities, as changing family ties and more openings near home make people less willing to uproot their lives for work.

About 3.5 million people relocated for a new job last year, according to U.S. census data, a 10% drop from 3.8 million in 2015. The numbers have fluctuated between 2.8 million and 4.5 million since the government started tracking annual job-related relocations in 1999—but have been trending lower overall, even as the U.S. population grew by nearly 20% over that stretch.

Experts cite a number of factors that in some periods have kept people in one place, including a depressed value for their home or limited job openings. In the current strong economy, real-estate values have rebounded, but that has made housing costs prohibitively high in some regions where jobs are abundant, such as major East and West Coast cities.

For the full story, see:

Rachel Feintzeig and Lauren Weber. “Fewer Workers Move for New Jobs.” The Wall Street Journal (Monday, August 20, 2018): A1-A2.

(Note: the online version of the story did not give a date, and has the title “Fewer Americans Uproot Themselves for a New Job.”)

If You Have Lost Your Spouse, Chatbot Asks: “What’s Your Tracking Number?”

(p. B4) When LivePerson Inc. started piloting chatbots in early 2018, one of them made an embarrassing faux pas, assuming a client’s customer was talking about a lost package after mentioning losing a spouse.

“And the bot goes, ‘All right, great, I can help you with that. What’s your tracking number?’” said Malik Jenkins, an employee at the artificial-intelligence software company who was involved in the pilots. He said the issue was immediately flagged by someone at the client company and his team tweaked the bot to avoid such responses in the future.

For the full story, see:

Jared Council. “A Human Touch Is Given to Chatbots.” The Wall Street Journal (Thursday, June 13, 2019): B4.

(Note: the online version of the story has the date June 12, 2019, and the title “When Chatbots Falter, Humans Steer Them the Right Way.”)

IBM’s Watson AI Platform Is Not Curing Cancer

(p. B1) Can Watson cure cancer?

That’s what International Business Machines Corp. IBM 0.03% asked soon after its artificial-intelligence system beat humans at the quiz show “Jeopardy!” in 2011. Watson could read documents quickly and find patterns in data. Could it match patient information with the latest in medical studies to deliver personalized treatment recommendations?

“Watson represents a technology breakthrough that can help physicians improve patient outcomes,” said Herbert Chase, a professor of biomedical informatics at Columbia University, in a 2012 IBM press release.

Six years and billions of dollars later, the diagnosis for Watson is gloomy. Continue reading “IBM’s Watson AI Platform Is Not Curing Cancer”

“Only 5% to 10% of Jobs Can Have the Human Element Removed Entirely”

(p. A15) Careful studies using a task-based view of this sort find that, although substantial parts of many jobs can be automated—that is, technology can help still-needed workers become more productive—only 5% to 10% of jobs can have the human element removed entirely. The rate of productivity growth implied by the coming wave of automation would thus look similar to historical rates.

. . .

. . . the best insights into the future of work may be found in the trenches of everyday management. Take “Human + Machine,” by Accenture leaders Paul Daugherty and Jim Wilson, which opens in a BMW assembly plant where “a worker and robot are collaborating.” In their view, “machines are not taking over the world, nor are they obviating the need for humans in the workplace.”

The authors explain, for instance, why making robots operate more safely alongside humans has been critical to factory deployment—the very breakthrough emphasized by Dynamic’s CEO, but ignored by Mr. West. They describe AI’s role alongside existing workers in decidedly unsexy fields like equipment maintenance, bank-fraud detection and customer complaint management. And they illuminate the promise and pitfalls of implementing new processes that allocate some tasks to machines, requiring new forms of oversight and coordination.

Even in their overuse of acronyms and the word “reimagine,” the authors bring to life the realities of modern management. Readers gain a tactile sense of how technology changes business over time and why “the robots are coming” is no scarier an observation than ever before.

For the full review, see:

Oren Cass. “BOOKSHELF; Reckoning With the Robots; Automation rarely outright destroys jobs. It instead augments—taking over routine tasks while humans handle more complex ones.” The Wall Street Journal (Monday, June 25, 2018): A15.

(Note: ellipses added.)

(Note: the online version of the review has the date June 24, 2018, and has the title “BOOKSHELF; ‘The Future of Work’ and ‘Human + Machine’ Review: Reckoning With the Robots; Automation rarely outright destroys jobs. It instead augments—taking over routine tasks while humans handle more complex ones.”)

The book under review, in the passages above, is:

Daugherty, Paul R., and H. James Wilson. Human + Machine: Reimagining Work in the Age of AI. Boston, MA: Harvard Business Review Press, 2018.

Astros Got Scouting and Analytics to Work Together

(p. A15) Mr. Reiter . . . has written a full account of the remarkable story of how one of the greatest turnarounds in modern baseball history was engineered. As he tells us in “Astroball: The New Way to Win It All,” Houston had looked at the processes that Oakland A’s general manager Billy Beane had used early in the 21st century. That team’s methods—sophisticated statistical analyses and attention to “undervalued” measuring sticks (like on-base percentage)—were detailed in Michael Lewis’s “Moneyball” (2003), and they changed the way baseball front offices operated. But Mr. Lewis’s book also portrayed a somewhat fraught internal organization, with old-fashioned scouts in one corner and the analytic nerds in the other, often disagreeing about players and prospects and resenting one another as well.

Astros general manager Jeff Luhnow wanted to figure out how to get scouting and analytics to work together and eventually produce an internal metric that would render a decision on a player as simple as the one in blackjack: hit or stay, keep or trade, play or bench.

. . .

Under Mr. Luhnow, scouts not only made subjective judgments about a prospect’s talent but also collected unique data that they fed to the folks in the Nerd Cave. And the nerds began listening to the scouts. All of this was easier said than done, but it was done, and the team made a series of sound, even brilliant, choices as it drafted, traded and signed players.

For the full review, see:

Paul Dickson. “BOOKSHELF; Lone Star Turnaround; How the Houston Astros used a combination of data-driven analytics and team-building to go from last place to World Series champions.” The Wall Street Journal (Tuesday, July 17, 2018): A15.

(Note: ellipses added.)

(Note: the online version of the review has the date July 16, 2018, and has the title “BOOKSHELF; ‘Astroball’ Review: Lone Star Turnaround
How the Houston Astros used a combination of data-driven analytics and team-building to go from last place to World Series champions.”)

The book under review is:

Reiter, Ben. Astroball: The New Way to Win It All. New York: Crown Archetype, 2018.