Month: August 2015

The Complementarity of Humans and Robots in Education

(p. 6) Computers and robots are already replacing many workers. What can young people learn now that won’t be superseded within their lifetimes by these devices and that will secure them good jobs and solid income over the next 20, 30 or 50 years? In the universities, we are struggling to answer that question.
. . .
Some scholars are trying to discern what kinds of learning have survived technological replacement better than others. Richard J. Murnane and Frank Levy in their book “The New Division of Labor” (Princeton, 2004) studied occupations that expanded during the information revolution of the recent past. They included jobs like service manager at an auto dealership, as opposed to jobs that have declined, like telephone operator.
The successful occupations, by this measure, shared certain characteristics: People who practiced them needed complex communication skills and expert knowledge. Such skills included an ability to convey “not just information but a particular interpretation of information.” They said that expert knowledge was broad, deep and practical, allowing the solution of “uncharted problems.”
. . .
When I arrived at Yale in 1982, there were no undergraduate courses in finance. I started one in the fall of 1985, and it continues today. Increasingly, I’ve tried to connect mathematical theory to actual applications in finance.
Since its beginnings, the course has gradually become more robotic: It resembles a real, dynamic, teaching experience, but in execution, much of it is prerecorded, and exercises and examinations are computerized. Students can take it without need of my physical presence. Yale made my course available to the broader public on free online sites: AllLearn in 2002, Open Yale in 2008 and 2011, and now on Coursera.
The process of tweaking and improving the course to fit better in a digital framework has given me time to reflect about what I am doing for my students. I could just retire now and let them watch my lectures and use the rest of the digitized material. But I find myself thinking that I should be doing something more for them.
So I continue to update the course, thinking about how I can integrate its lessons into an “art of living in the world.” I have tried to enhance my students’ sense that finance should be the art of financing important human activities, of getting people (and robots someday) working together to accomplish things that we really want done.

For the full commentary, see:
ROBERT J. SHILLER. “Economic View; What to Learn in College to Stay One Step Ahead of Computers.” The New York Times, SundayBusiness Section (Sun., MAY 24, 2015): 6.
(Note: ellipses added.)
(Note: the online version of the commentary has the date MAY 22, 2015, and has the title “Economic View; What to Learn in College to Stay One Step Ahead of Computers.”)

The Levy and Murnane book mentioned above, is:
Levy, Frank, and Richard J. Murnane. The New Division of Labor: How Computers Are Creating the Next Job Market. Princeton, NJ: Princeton University Press, 2004.
Some of the core of the Levy and Murnane book can be found in:
Levy, Frank, and Richard Murnane. “Book Excerpt: The New Division of Labor.” Milken Institute Review 6, no. 4 (Dec. 2004): 61-82.

McCulloch Endorses Strunk and White’s “Revise and Rewrite” and “Be Clear”

(p. 10) When you wrote your first book, on the Johnstown flood, did you have a model in mind, a kind of storytelling you admired?
Walter Lord’s “A Night to Remember,” about the sinking of the Titanic, was the best book about a disaster I had ever read. But in an odd way I think I was more influenced at the time by the novels of Conrad Richter, and particularly his Ohio trilogy, “The Trees,” “The Fields” and “The Town,” in the extremely skillful way he evoked a sense of place.
. . .
If you had to name one book that made you who you are today, what would it be?
“The Elements of Style,” by William Strunk Jr. and E. B. White. I read it first nearly 50 years ago and still turn to it as an ever reliable aid-to-navigation, and particularly White’s last chapter, with its reminders to “Revise and Rewrite” and “Be Clear.”

For the full interview, see:
“By the Book: David McCullough.” The New York Times Book Review (Sun., MAY 31, 2015): 10.
(Note: ellipsis added, bold in original. The bold questions are from an anonymous New York Times interviewer.)
(Note: the online version of the interview has the date MAY 28, 2015, and has the title “David McCullough: By the Book.”)

A wonderful book by McCullough, is:
McCullough, David. The Wright Brothers. New York: Simon & Schuster, 2015.

“Buy Local” Inefficiently Wastes Resources

(p. 8) Much is . . . made about the eco-friendliness of handmade.
“Buying handmade (especially really locally) can greatly reduce your carbon footprint on the world,” reads a post on the popular website Handmadeology.
But few economists give much credence to the idea that buying local necessarily saves energy. Most believe that the economies of scale inherent in mass production outweigh the benefits of nearness. These same economies of scale most likely make a toothbrush factory less wasteful, in terms of materials, than 100 individual toothbrush makers each handcrafting 10 toothbrushes a day.

For the full commentary, see:
EMILY MATCHA. “OPINION; It’s Chic. Not Morally Superior. That Handmade Scarf Won’t Save the World.” The New York Times, SundayReview Section (Sun., MAY 3, 2015): 8.
(Note: ellipses added.)
(Note: the online version of the coomentary has the date MAY 2, 2015, and has the title “OPINION; Sorry, Etsy. That Handmade Scarf Won’t Save the World.”)

Average Length of 10-K Reports Rises to 41,911 Words

WordLength10KannualReportGraph2015-07-05.jpgSource of graph: online version of the WSJ article quoted and cited below.

(p. B1) General Electric Co.’s chief financial officer was taken aback by the industrial conglomerate’s 246-page annual report.

The 10-K and supporting documents his finance team and others at the company produced was meant to give investors a comprehensive picture of GE’s businesses and financial performance over the previous 12 months. It did everything but.
Packed with text on the company’s internal controls, auditor statements and regulator-mandated boilerplate on “inflation, recession and currency volatility,” the 2013 annual report was 109,894 words long. “Not a retail investor on planet Earth could get through” it, let alone understand it, said GE finance chief Jeffrey Bornstein.
Companies are spending an increasing amount of time and energy beefing up their regulatory filings to meet disclosure requirements. The average 10K is getting longer–about 42,000 words in 2013, up from roughly 30,000 words in 2000. By comparison, the text of the Sarbanes-Oxley Act of 2002 has 32,000 words.

For the full story, see:
VIPAL MONGA and EMILY CHASAN. “The 109,894-Word Annual Report.” The Wall Street Journal (Tues., June 2, 2015): B1 & B10.

“Big Data” Does Not Tell Us What to Measure, and Ignores What Cannot Be Measured

(p. 6) BIG data will save the world. How often have we heard that over the past couple of years? We’re pretty sure both of us have said something similar dozens of times in the past few months.
If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.
The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”
. . .
So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data.
Facebook has tons of data on how people use its site. It’s easy to see whether a particular news feed story was liked, clicked, commented on or shared. But not one of these is a perfect proxy for more important questions: What was the experience like? Did the story connect you with your friends? Did it inform you about the world? Did it make you laugh?
(p. 7) To get to these measures, Facebook has to take an old-fashioned approach: asking. Every day, hundreds of individuals load their news feed and answer questions about the stories they see there. Big data (likes, clicks, comments) is supplemented by small data (“Do you want to see this post in your News Feed?”) and contextualized (“Why?”).
Big data in the form of behaviors and small data in the form of surveys complement each other and produce insights rather than simple metrics.
. . .
Because of this need for small data, Facebook’s data teams look different than you would guess. Facebook employs social psychologists, anthropologists and sociologists precisely to find what simple measures miss.
And it’s not just Silicon Valley firms that employ the power of small data. Baseball is often used as the quintessential story of data geeks, crunching huge data sets, replacing fallible human experts, like scouts. This story was made famous in both the book and the movie “Moneyball.”
But the true story is not that simple. For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of fivethirtyeight.com estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.
. . .
Human experts can also help data analysts figure out what to look for. For decades, scouts have judged catchers based on their ability to frame pitches — to make the pitch appear more like a strike to a watching umpire. Thanks to improved data on pitch location, analysts have recently checked this hypothesis and confirmed that catchers differ significantly in this skill.

For the full commentary, see:
ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ. “How Not to Drown in Numbers.” The New York Times, SundayReview Section (Sun., MAY 3, 2015): 6-7.
(Note: ellipses added.)
(Note: the online version of the commentary has the date MAY 2, 2015.)

Spread of Robots Creates New and Better Human Jobs

(p. A11) The issues at the heart of “Learning by Doing” come into sharp relief when James Bessen visits a retail distribution center near Boston that was featured on “60 Minutes” two years ago. The TV segment, titled “Are Robots Hurting Job Growth?,” combined gotcha reporting with vintage movie clips–scary-looking Hollywood robots–to tell a chilling tale of human displacement and runaway job loss.
Mr. Bessen isn’t buying it. Although robots at the distribution center have eliminated some jobs, he says, they have created others–for production workers, technicians and managers. The problem at automated workplaces isn’t the robots. It’s the lack of qualified workers. New jobs “require specialized skills,” Mr. Bessen writes, but workers with these skills “are in short supply.”
It is a deeply contrarian view. The conventional wisdom about robots and other new workplace technology is that they do more harm than good, destroying jobs and hollowing out the middle class. MIT economists Erik Brynjolfsson and Andrew McAfee made the case in their best-selling 2014 book, “The Second Machine Age.” They describe a future in which software-driven machines will take over not just routine jobs–replacing clerks, cashiers and warehouse workers–but also tasks done by nurses, doctors, lawyers and stock traders. Mr. Bessen sets out to refute the arguments of such techno-pessimists, relying on economic analysis and on a fresh reading of history.

For the full review, see:
TAMAR JACOBY. “BOOKSHELF; Technology Isn’t a Job Killer; Many predicted ATMs would eliminate bank tellers, but the number of tellers in the U.S. has risen since the machines were introduced.” The Wall Street Journal (Thurs., May 21, 2015): A11.
(Note: the online version of the review has the date May 20, 2015.)

The book under review, is:
Bessen, James. Learning by Doing: The Real Connection between Innovation, Wages, and Wealth. New Haven, CT: Yale University Press, 2015.

Computer Programs “Lack the Flexibility of Human Thinking”

(p. A11) . . . let’s not panic. “Superintelligent” machines won’t be arriving soon. Computers today are good at narrow tasks carefully engineered by programmers, like balancing checkbooks and landing airplanes, but after five decades of research, they are still weak at anything that looks remotely like genuine human intelligence.
. . .
Even the best computer programs out there lack the flexibility of human thinking. A teenager can pick up a new videogame in an hour; your average computer program still can only do just the single task for which it was designed. (Some new technologies do slightly better, but they still struggle with any task that requires long-term planning.)

For the full commentary, see:
GARY MARCUS. “Artificial Intelligence Isn’t a Threat–Yet; Superintelligent machines are still a long way off, but we need to prepare for their future rise.” The Wall Street Journal (Sat., Dec. 13, 2014): A11.
(Note: ellipsis added.)
(Note: the online version of the commentary has the date Dec. 11, 2014.)

Cultural and Institutional Differences Between Europe and U.S. Keep Europe from Having a Silicon Valley

(p. B7) “They all want a Silicon Valley,” Jacob Kirkegaard, a Danish economist and senior fellow at the Peterson Institute for International Economics, told me this week. “But none of them can match the scale and focus on the new and truly innovative technologies you have in the United States. Europe and the rest of the world are playing catch-up, to the great frustration of policy makers there.”
Petra Moser, assistant professor of economics at Stanford and its Europe Center, who was born in Germany, agreed that “Europeans are worried.”
“They’re trying to recreate Silicon Valley in places like Munich, so far with little success,” she said. “The institutional and cultural differences are still too great.”
. . .
There is . . . little or no stigma in Silicon Valley to being fired; Steve Jobs himself was forced out of Apple. “American companies allow their employees to leave and try something else,” Professor Moser said. “Then, if it works, great, the mother company acquires the start-up. If it doesn’t, they hire them back. It’s a great system. It allows people to experiment and try things. In Germany, you can’t do that. People would hold it against you. They’d see it as disloyal. It’s a very different ethic.”
Europeans are also much less receptive to the kind of truly disruptive innovation represented by a Google or a Facebook, Mr. Kirkegaard said.
He cited the example of Uber, the ride-hailing service that despite its German-sounding name is a thoroughly American upstart. Uber has been greeted in Europe like the arrival of a virus, and its reception says a lot about the power of incumbent taxi operators.
“But it goes deeper than that,” Mr. Kirkegaard said. “New Yorkers don’t get all nostalgic about yellow cabs. In London, the black cab is seen as something that makes London what it is. People like it that way. Americans tend to act in a more rational and less emotional way about the goods and services they consume, because it’s not tied up with their national and regional identities.”
. . .
With its emphasis on early testing and sorting, the educational system in Europe tends to be very rigid. “If you don’t do well at age 18, you’re out,” Professor Moser said. “That cuts out a lot of people who could do better but never get the chance. The person who does best at a test of rote memorization at age 17 may not be innovative at 23.” She added that many of Europe’s most enterprising students go to the United States to study and end up staying.
She is currently doing research into creativity. “The American education system is much more forgiving,” Professor Moser said. “Students can catch up and go on to excel.”
Even the vaunted European child-rearing, she believes, is too prescriptive. While she concedes there is as yet no hard scientific evidence to support her thesis, “European children may be better behaved, but American children may end up being more free to explore new things.”

For the full story, see:
JAMES B. STEWART. “Common Sense; A Fearless Culture Fuels Tech.” The New York Times (Fri., JUNE 19, 2015): B1 & B7.
(Note: ellipses added.)
(Note: the online version of the story has the date JUNE 18, 2015, and has the title “Common Sense; A Fearless Culture Fuels U.S. Tech Giants.”)

Babies “Have a Positive Hunger for the Unexpected”

(p. C2) In an amazingly clever new paper in the journal Science, Aimee Stahl and Lisa Feigenson at Johns Hopkins University show systematically that 11-month-old babies, like scientists, pay special attention when their predictions are violated, learn especially well as a result, and even do experiments to figure out just what happened.
They took off from some classic research showing that babies will look at something longer when it is unexpected. The babies in the new study either saw impossible events, like the apparent passage of a ball through a solid brick wall, or straightforward events, like the same ball simply moving through an empty space.
. . .
The babies explored objects more when they behaved unexpectedly. They also explored them differently depending on just how they behaved unexpectedly. If the ball had vanished through the wall, the babies banged the ball against a surface; if it had hovered in thin air, they dropped it. It was as if they were testing to see if the ball really was solid, or really did defy gravity, much like Georgie testing the fake eggs in the Easter basket.
In fact, these experiments suggest that babies may be even better scientists than grown-ups often are. Adults suffer from “confirmation bias”–we pay attention to the events that fit what we already know and ignore things that might shake up our preconceptions. Charles Darwin famously kept a special list of all the facts that were at odds with his theory, because he knew he’d otherwise be tempted to ignore or forget them.
Babies, on the other hand, seem to have a positive hunger for the unexpected. Like the ideal scientists proposed by the philosopher of science Karl Popper, babies are always on the lookout for a fact that falsifies their theories.

For the full commentary, see:
ALISON GOPNIK. “MIND AND MATTER; How 1-Year-Olds Figure Out the World.” The Wall Street Journal (Sat., April 15, 2015): C2.
(Note: ellipsis added.)
(Note: the online version of the commentary has the date April 15, 2015, and has the title “MIND AND MATTER; How 1-Year-Olds Figure Out the World.”)

The scientific article mentioned in the passages quoted, is:
Stahl, Aimee E., and Lisa Feigenson. “Observing the Unexpected Enhances Infants’ Learning and Exploration.” Science 348, no. 6230 (April 3, 2015): 91-94.

“The Great Fact” of “the Ice-Hockey Stick”

(p. 2) Economic history has looked like an ice-hockey stick lying on the ground. It had a long, long horizontal handle at $3 a day extending through the two-hundred-thousand-year history of Homo sapiens to 1800, with little bumps upward on the handle in ancient Rome and the early medieval Arab world and high medieval Europe, with regressions to $3 afterward–then a wholly unexpected blade, leaping up in the last two out of the two thousand centuries, to $30 a day and in many places well beyond.
. . .
(p. 48) The heart of the matter is sixteen. Real income per head nowadays exceeds that around 1700 or 1800 in, say, Britain and in other countries that have experienced modern economic growth by such a large factor as sixteen, at least. You, oh average participant in the British economy, go through at least sixteen times more food and clothing and housing and education in a day than an ancestor of yours did two or three centuries ago. Not sixteen percent more, but sixteen multiplied by the old standard of living. You in the American or the South Korean economy, compared to the wretchedness of former Smiths in 1653 or Kims in 1953, have done even better. And if such novelties as jet travel and vitamin pills and instant messaging are accounted at their proper value, the factor of material improvement climbs even higher than sixteen–to eighteen, or thirty, or far beyond. No previous episode of enrichment for the average person approaches it, not the China of the Song Dynasty or the Egypt of the New Kingdom, not the glory of Greece or the grandeur of Rome.
No competent economist, regardless of her politics, denies the Great Fact.

Source:
McCloskey, Deirdre N. Bourgeois Dignity: Why Economics Can’t Explain the Modern World. Chicago: University of Chicago Press, 2010.
(Note: ellipsis added.)

We Often “See” What We Expect to See

(p. 9) The Justice Department recently analyzed eight years of shootings by Philadelphia police officers. Its report contained two sobering statistics: Fifteen percent of those shot were unarmed; and in half of these cases, an officer reportedly misidentified a “nonthreatening object (e.g., a cellphone) or movement (e.g., tugging at the waistband)” as a weapon.
Many factors presumably contribute to such shootings, ranging from carelessness to unconscious bias to explicit racism, all of which have received considerable attention of late, and deservedly so.
But there is a lesser-known psychological phenomenon that might also explain some of these shootings. It’s called “affective realism”: the tendency of your feelings to influence what you see — not what you think you see, but the actual content of your perceptual experience.
. . .
The brain is a predictive organ. A majority of your brain activity consists of predictions about the world — thousands of them at a time — based on your past experience. These predictions are not deliberate prognostications like “the Red Sox will win the World Series,” but unconscious anticipations of every sight, sound and other sensation you might encounter in every instant. These neural “guesses” largely shape what you see, hear and otherwise perceive.
. . .
. . . , our lab at Northeastern University has conducted experiments to document affective realism. For example, in one study we showed an affectively neutral face to our test subjects, and using special equipment, we secretly accompanied it with a smiling or scowling face that the subjects could not consciously see. (The technique is called “continuous flash suppression.”) We found that the unseen faces influenced the subjects’ bodily activity (e.g., how fast their hearts beat) and their feelings. These in turn influenced their perceptions: In the presence of an unseen scowling face, our subjects felt unpleasant and perceived the neutral face as less likable, less trustworthy, less competent, less attractive and more likely to commit a crime than when we paired it with an unseen smiling face.
These weren’t just impressions; they were actual visual changes. The test subjects saw the neutral faces as having a more furrowed brow, a more surly mouth and so on. (Some of these findings were published in Emotion in 2012.)
. . .
. . . the brain is wired for prediction, and you predict most of the sights, sounds and other sensations in your life. You are, in large measure, the architect of your own experience.

For the full commentary, see:
Feldman Barrett, Lisa, and Jolie Wormwood. “When a Gun Is Not a Gun.” The New York Times, SundayReview Section (Sun., April 19, 2015): 9.
(Note: italics in original; ellipses added.)
(Note: the date of the online version of the commentary is APRIL 17, 2015.)

The academic article mentioned in the passage quoted above, is:
Anderson, Eric, Erika Siegel, Dominique White, and Lisa Feldman Barrett. “Out of Sight but Not out of Mind: Unseen Affective Faces Influence Evaluations and Social Impressions.” Emotion 12, no. 6 (Dec. 2012): 1210-21.