How Drinking Coffee Makes Us Younger and More Open-Minded

(p. C2) . . . , if a baby monkey heard a new sound pattern many times, her neurons (brain cells) would adjust to respond more to that sound pattern. Older monkeys’ neurons didn’t change in the same way.

At least part of the reason for this lies in neurotransmitters, chemicals that help to connect one neuron to another. Young animals have high levels of “cholinergic” neurotransmitters that make the brain more plastic, easier to change. Older animals start to produce inhibitory chemicals that counteract the effect of the cholinergic ones. They actually actively keep the brain from changing.

. . .

In the new research, Jay Blundon and colleagues at St. Jude Children’s Research Hospital in Memphis, Tenn., tried to restore early-learning abilities to adult mice. As in the earlier experiments, they exposed the mice to a new sound and recorded whether their neurons changed in response. But this time the researchers tried making the adult mice more flexible by keeping the inhibitory brain chemicals from influencing the neurons.

In some studies, they actually changed the mouse genes so that the animals no longer produced the inhibitors in the same way. In others, they injected other chemicals that counteracted the inhibitors. (Caffeine seems to work in this way, by counteracting inhibitory neurotransmitters. That’s why coffee makes us more alert and helps us to learn.)

In all of these cases in the St. Jude study, the adult brains started to look like the baby brains.

For the full commentary, see:

Alison Gopnik. “MIND & MATTER; How to Get Old Brains to Think Like Young Ones.” The New York Times (Saturday, July 8, 2017): C2.

(Note: ellipses added.)

(Note: the online version of the commentary has the date July 7, 2017, and has the same title as the print version.)

The article co-authored by Jay Blundon and mentioned above,is:

Blundon, Jay A., Noah C. Roy, Brett J. W. Teubner, Jing Yu, Tae-Yeon Eom, K. Jake Sample, Amar Pani, Richard J. Smeyne, Seung Baek Han, Ryan A. Kerekes, Derek C. Rose, Troy A. Hackett, Pradeep K. Vuppala, Burgess B. Freeman, and Stanislav S. Zakharenko. “Restoring Auditory Cortex Plasticity in Adult Mice by Restricting Thalamic Adenosine Signaling.” Science 356, no. 6345 (June 30, 2017): 1352-56.

Stalin’s “Despotism in Mass Bloodshed”

(p. A13) In the aftermath of Lenin’s death in January 1924, Joseph Stalin—already secretary-general of the Communist Party—emerged as the outright leader of the Soviet Union. “Right through 1927,” Stephen Kotkin notes, Stalin “had not appeared to be a sociopath in the eyes of those who worked most closely with him.” But by 1929-30, he “was exhibiting an intense dark side.” Mr. Kotkin’s “Stalin: Waiting for Hitler, 1929-1941,” the second volume of a planned three-volume biography, tracks the Soviet leader’s transformation during these crucial years. “Impatient with dictatorship,” Mr. Kotkin says, Stalin set out to forge “a despotism in mass bloodshed.”

The three central episodes of Mr. Kotkin’s narrative, all from the 1930s, are indeed violent and catastrophic, if in different ways: the forced collectivization of Soviet agriculture; the atrocities of the Great Terror, when Stalin “arrested and murdered immense numbers of loyal people”; and the rise of Adolf Hitler, the man who would become Stalin’s ally and then, as Mr. Kotkin puts it, his “principal nemesis.” In each case, as Mr. Kotkin shows, Stalin’s personal character—a combination of ruthlessness and paranoia—played a key role in the unfolding of events.

For the full review, see:

Joshua Rubenstein. “BOOKSHELF; The Turn to Tyranny; We may never know what degree of personal obsession, political calculation and ideological zeal drove Stalin to kill and persecute so many.” The Wall Street Journal (Wednesday, Nov. 1, 2017): A13.

(Note: the online version of the review has the date Oct. 31, 2017, and has the same title “BOOKSHELF; Review: The Turn to Tyranny; We may never know what degree of personal obsession, political calculation and ideological zeal drove Stalin to kill and persecute so many.”)

The book under review is:

Kotkin, Stephen. Stalin: Volume 2: Waiting for Hitler, 1929-1941. New York: Penguin Press, 2017.

Manic Energy from Bipolar Disorder May Enable “Heights of Success”

(p. A17) Dr. Ronald R. Fieve, who was a pioneer in the prescription of lithium to treat mania and other mood disorders — while avowing that some gifted individuals, like Abraham Lincoln, Theodore Roosevelt and Winston Churchill, might have benefited from being bipolar — died on Jan. 2 [2018] at his home in Palm Beach, Fla.

. . .

He cited estimates that as many as one in 15 people experienced a manic episode during their lifetimes, and that bipolar disorder — characterized by swings from elation, hyperactivity and a decreased need for sleep to incapacitating depression — was often misclassified as schizophrenia or other illnesses, or undiagnosed altogether.

He cautioned, however, that some highly creative, exuberant and energetic people have derived benefits from the condition because they have what he called “a hypomanic edge.”

“I have found that some of the most gifted individuals in our society suffer from this condition — including many outstanding writers, politicians, business executives and scientists — where tremendous amounts of manic energy have enabled them to achieve their heights of success,” Dr. Fieve told a symposium in 1973.

But without proper treatment, he said, those individuals afflicted with manic depression “more often than not either go too ‘high’ or suddenly crash into a devastating depression that we only hear about after a successful suicide.”

In contrast to antidepressant drugs or electroshock treatments, he said, regular doses of lithium carbonate appeared to stabilize mood swings without cramping creativity, memory or personality.

. . .

Before it was approved to treat depression, lithium was found in the late 1940s to be potentially unsafe as a salt substitute. But Dr. Fieve pointed out that lithium had been found in natural mineral waters prescribed by Greek and Roman physicians 1,500 years earlier to treat what were then called manic insanity and melancholia.

For the full obituary, see:

Sam Roberts. “Dr. Ronald Fieve, Pioneer In Lithium, Is Dead at 87.” The New York Times (Wednesday, Jan. 17, 2018): A17.

(Note: ellipses, and bracketed year, added.)

(Note: the online version of the obituary has the date Jan. 12, 2018, and has the title “Dr. Ronald Fieve, 87, Dies; Pioneered Lithium to Treat Mood Swings.”)

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.

Those Who Are Overconfident Convince Others They Are More Competent

(p. B6) What is it about an elite upbringing that seems to make people feel qualified for tasks where they have little experience? This is one of the questions that inspired a study published Monday in The Journal of Personality and Social Psychology.

The researchers suggest that part of the answer involves what they call “overconfidence.” In several experiments, they found that people who came from a higher social class were more likely to have an inflated sense of their skills — even when tests proved that they were average. This unmerited overconfidence, they found, was interpreted by strangers as competence.

. . .

In an attempt to understand the implications of overconfidence, the researchers constructed a mock job interview. The students were asked the same question and videotaped. A group of strangers then watched the videos and rated the candidates. The selection committee generally opted for the same people who’d overestimated their trivia abilities. Overconfidence was misinterpreted as competence.

. . .

So how do managers, employers, voters and customers avoid overvaluing social class and being duped by incompetent wealthy people? Dr. Kennedy said she had been encouraged to find that if you show people actual facts about a person, the elevated status that comes with overconfidence often fades away.

“We may also need to punish overconfident behavior more than we do,” she said.

For the full story, see:

Heather Murphy. “Why High-Class People Think They Know More, and Why You Believe Them.” The New York Times (Tuesday, May 21, 2019): B6.

(Note: ellipses added.)

(Note: the online version of the story has the date May 20, 2019, and has the title “Why High-Class People Get Away With Incompetence.”)

The study mentioned above from the Journal of Personality and Social Psychology, is:

Belmi, Peter, Margaret A. Neale, David Reiff, and Rosemary Ulfe. “The Social Advantage of Miscalibrated Individuals: The Relationship between Social Class and Overconfidence and Its Implications for Class-Based Inequality.” Journal of Personality and Social Psychology (May 20, 2019), published online in advance of print publication.

SpotMini Robot Looks Like a Dog, but “Is Like a Hollow Doll”

(p. B3) Last time in this esteemed newsletter, my colleague Steve Lohr warned that automation would change the economy. But as he also explained, jobs are “more likely to be transformed by digital technology than destroyed by it.” This becomes clear as you look a little closer at the progress of robotics, including everything from the robotic arms that help build stuff in factories to the jaw-droppingly agile machines under development at a company called Boston Dynamics.

This past week, I wrote about Boston Dynamics, which runs a semi-secretive lab in Waltham, Mass., about 10 miles outside Boston. Built to move like animals and even humans, its machines are truly amazing (as YouTube watchers will attest).

At times, you can’t help but think of these mechanical creations as living things. The company will start selling one of them, a doglike robot called SpotMini, in the coming year. But even Boston Dynamics is not quite sure what these robots are actually good for.

Robots play tricks on the mind. We tend to think they are more advanced than they really are, perhaps because of science fiction movies or because our brains are wired to believe in bots. This is particularly true when it comes to the biomimetic machines inside a lab like Boston Dynamics.

“When we see a biped that looks like a person or a quadruped that looks like a dog, we project our previous experiences with people and dogs onto these machines. But, in fact, there is nothing inside,” said Gill Pratt, who worked with Boston Dynamics as an official at Darpa, a research arm of the Defense Department, and is now exploring new forms of robotics as the chief executive of the Toyota Research Institute. “It is like a hollow doll.”

For the full commentary, see:

Cade Metz. “The Week in Tech; Robots Are Improving Quickly, But They Can Still Be Dumb.” The New York Times (Monday, Oct. 1, 2018): B3.

(Note: the online version of the commentary has the date Sept. 28, 2018, and has the title “The Week in Tech; The Robots Aren’t as Human as They Seem.”)