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Manne also reported in the book that women are less likely to be perceived as competent. When they are considered competent, they're often disliked and considered polarizing. She said female candidates are also often judged to be untrustworthy "on no ostensible basis" and women's claims are viewed as less credible than claims by men. Then, when women defend themselves from unfair attacks, they're accused of "playing the victim."

Yet Bernie Sanders -- the frontrunner among declared Democratic candidates -- has also been accused of mistreating his aides, but those allegations don't seem to have gotten the same media attention. One former staffer told the Vermont newspaper Seven Days in 2015 that Sanders was "unbelievably abusive" and claimed "to have endured frequent verbal assaults." The paper reported that others who worked for Sanders also said that "the senator is prone to fits of anger." A spokesperson for Sanders responded to Seven Days and said that Sanders "had very positive relations with people who have worked with him.") And Sanders, in response to the article, told the Des Moines Register, "Yes, I do work hard. Yes, I do demand a lot of the people who work with me. Yes, some people have left who were not happy. But I would say that by and large in my Senate office, in my House office, on my campaigns, the vast majority of people who have worked with me considered that to be a very, very good experience..."

Ultimately, the solution to women not appearing to fit the role of president because a woman has never been president seems obvious: Voters need to elect a woman president. But, in order for that to happen, even those of us who are eager to empower women may need to rethink how we judge female candidates.

If Mr. Trump talks about Muslim parents and their son who was killed in action, that’s what the public remembers. If he goes to Mexico or Louisiana, that’s what they recall reading or hearing about him. If Mr. Trump calls President Obama the founder of the Islamic State, “ISIS” moves to the top of the list of what Americans tell us they are hearing about the Republican candidate.

it may not matter exactly why Americans are so likely to recall reading about Mrs. Clinton’s email situation week after week. Its looming prominence in the public’s mind has become a reality, and it has the effect of superseding public awareness of her policy speeches and statements about issues.

What Americans recall hearing about Mrs. Clinton is significantly less varied. Specifically — and to an extraordinary degree — Americans have consistently told us that they are reading and hearing about her handling of emails

By contrast, it’s clear that Mrs. Clinton and her campaign team have not wanted her handling of emails to dominate what Americans have been taking away from her campaign over the past two months

we can assume he wants his statements and actions to be seen and heard, to attract attention. The evidence is clear that they are. The public may be getting no more than a superficial understanding of Mr. Trump’s positions on key issues or how he would implement them as laws if he is elected, but the public clearly is repeating back to us what he intends for it to hear.

the public may be learning about the candidates’ temperament, character, personality and health issues, but from what they tell us, Americans aren’t getting much in the way of real substance.

The moderators of the coming series of debates will most likely focus directly on the candidates’ positions on issues. This may shift what Americans tell us they are learning about the candidates, and if so, it could signal a significant upgrade in the way the process is working. But that also means that a lot still depends on the candidates themselves and how they end up shaping the contours of the debates

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

during a presidential campaign, it can be deeply dysfunctional, ensuring that many major issues are barely discussed. Problems that cannot be candidly described and vigorously debated are unlikely to be addressed seriously. In a country where citizens think of themselves as practical problem-solvers and realists, this aversion to bad news is a surprising feature of the democratic process.

“It has a pernicious effect on our politics and on governing, because to govern, you need a mandate. And you don’t get a mandate if you don’t say what you’re going to do.”

the self-censorship it produces in politicians is bipartisan, even if it is more pronounced on the left for some issues and the right for others.

Mr. Carter, they will say, disastrously spoke of a national “crisis of confidence” and failed to project the optimism that Americans demand of their presidents. He lost his re-election bid to sunny Ronald Reagan, who promised “morning in America” and left an indelible lesson for candidates of both parties: that voters can be vindictive toward anyone who dares criticize the country and, implicitly, the people.

This is a peculiarly American brand of nationalism. “European politicians exercise much greater freedom to address bluntly the uglier social problems,”

Absent any other champion, they supported the jerk they thought was more on their side — that is, on the issues that most concerned them.

We can look for the worst in our opponents, but that doesn’t always explain how they got the best of us.

This method puts the spotlight on the reactions of voting blocs and takes the spotlight off the individual qualities of candidates. It puts the spotlight on messaging and takes the spotlight off product: actual policies.

It puts the spotlight on slight differences across the socio-economic spectrum and takes the spotlight off the power of events to reframe the whole mood and landscape.

Another victim was President Obama. His 2012 campaign was legendary from an analytic point of view, and, of course, it was victorious. But it lacked a policy agenda and produced no mandate. Without a compelling agenda, the administration has projected an image of reactive drift and lost public confidence.

Some politicians, like F.D.R. or Ronald Reagan, can reframe debates and envision coalitions that don’t exist. Their visions emerge out of unique life experiences, which are unusual but have broad appeal. They build trust not through a few targeted messages but by fully embodying a moment and a people. They often don’t pander to existing identities but arouse different identities.

Obviously demography matters a lot. But, at heart, politics is a personal enterprise. Voters are looking for quality of leadership, character, vision and solidarity that defies quantification. Candidates like Daniel Patrick Moynihan or Jerry Brown can arouse great loyalty in ways that are impossible to predict.

In the midst of this scuffling economy, voters are thinking as Americans and not as members of a niche. They’re asking: What can be done to kick-start America? They’re not asking: How can I guarantee affordable contraception?

The more you look at political history, the more you see that political imagination is the rarest and most valuable of qualities. Voters don’t always know what they want, but they look to leaders to jump ahead of the current moment and provide visions they haven’t thought of.

other victims include the Democratic senators in red states. Winning in a state that the other party dominates is a personal enterprise. It requires an ineffable individual connection with voters. It requires an idiosyncratic approach to issues. By eclipsing individual quirks with generic messages, the data-driven style deprives outnumbered candidates of precisely what they need to survive.

Data-driven candidates sacrifice their own souls. Instead of being inner-directed leaders driven by their own beliefs, they become outer-directed pleasers driven by incomplete numbers.

And there is no reason to think that he will be the last viral candidate.

“It depends on what you mean by artificial intelligence.”

Computers are flexible enough to model the strange evolved convolutions of our thought, and yet responsive only to precise instructions. So if the endeavor succeeds, it will be a double victory: we will finally come to know the exact mechanics of our selves—and we’ll have made intelligent machines.

Ever since he was about 14, when he found out that his youngest sister, Molly, couldn’t understand language, because she “had something deeply wrong with her brain” (her neurological condition probably dated from birth, and was never diagnosed), he had been quietly obsessed by the relation of mind to matter.

How could consciousness be physical? How could a few pounds of gray gelatin give rise to our very thoughts and selves?

Consciousness, Hofstadter wanted to say, emerged via just the same kind of “level-crossing feedback loop.”

In 1931, the Austrian-born logician Kurt Gödel had famously shown how a mathematical system could make statements not just about numbers but about the system itself.

But then AI changed, and Hofstadter didn’t change with it, and for that he all but disappeared.

By the early 1980s, the pressure was great enough that AI, which had begun as an endeavor to answer yes to Alan Turing’s famous question, “Can machines think?,” started to mature—or mutate, depending on your point of view—into a subfield of software engineering, driven by applications.

Take Deep Blue, the IBM supercomputer that bested the chess grandmaster Garry Kasparov. Deep Blue won by brute force.

Hofstadter wanted to ask: Why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?”

AI started working when it ditched humans as a model, because it ditched them. That’s the thrust of the analogy: Airplanes don’t flap their wings; why should computers think?

It’s a compelling point. But it loses some bite when you consider what we want: a Google that knows, in the way a human would know, what you really mean when you search for something

Cognition is recognition,” he likes to say. He describes “seeing as” as the essential cognitive act: you see some lines a

How do you make a search engine that understands if you don’t know how you understand?

s “an A,” you see a hunk of wood as “a table,” you see a meeting as “an emperor-has-no-clothes situation” and a friend’s pouting as “sour grapes”

That’s what it means to understand. But how does understanding work?

analogy is “the fuel and fire of thinking,” the bread and butter of our daily mental lives.

there’s an analogy, a mental leap so stunningly complex that it’s a computational miracle: somehow your brain is able to strip any remark of the irrelevant surface details and extract its gist, its “skeletal essence,” and retrieve, from your own repertoire of ideas and experiences, the story or remark that best relates.

in Hofstadter’s telling, the story goes like this: when everybody else in AI started building products, he and his team, as his friend, the philosopher Daniel Dennett, wrote, “patiently, systematically, brilliantly,” way out of the light of day, chipped away at the real problem. “Very few people are interested in how human intelligence works,”

For more than 30 years, Hofstadter has worked as a professor at Indiana University at Bloomington

The quick unconscious chaos of a mind can be slowed down on the computer, or rewound, paused, even edited

project out of IBM called Candide. The idea behind Candide, a machine-translation system, was to start by admitting that the rules-based approach requires too deep an understanding of how language is produced; how semantics, syntax, and morphology work; and how words commingle in sentences and combine into paragraphs—to say nothing of understanding the ideas for which those words are merely conduits.

, Hofstadter directs the Fluid Analogies Research Group, affectionately known as FARG.

Parts of a program can be selectively isolated to see how it functions without them; parameters can be changed to see how performance improves or degrades. When the computer surprises you—whether by being especially creative or especially dim-witted—you can see exactly why.

When you read Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, which describes in detail this architecture and the logic and mechanics of the programs that use it, you wonder whether maybe Hofstadter got famous for the wrong book.

ut very few people, even admirers of GEB, know about the book or the programs it describes. And maybe that’s because FARG’s programs are almost ostentatiously impractical. Because they operate in tiny, seemingly childish “microdomains.” Because there is no task they perform better than a human.

“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”

“Nobody is a very reliable guide concerning activities in their mind that are, by definition, subconscious,” he once wrote. “This is what makes vast collections of errors so important. In an isolated error, the mechanisms involved yield only slight traces of themselves; however, in a large collection, vast numbers of such slight traces exist, collectively adding up to strong evidence for (and against) particular mechanisms.

So IBM threw that approach out the window. What the developers did instead was brilliant, but so straightforward,

The technique is called “machine learning.” The goal is to make a device that takes an English sentence as input and spits out a French sentence

What you do is feed the machine English sentences whose French translations you already know. (Candide, for example, used 2.2 million pairs of sentences, mostly from the bilingual proceedings of Canadian parliamentary debates.)

By repeating this process with millions of pairs of sentences, you will gradually calibrate your machine, to the point where you’ll be able to enter a sentence whose translation you don’t know and get a reasonable resul

Google Translate team can be made up of people who don’t speak most of the languages their application translates. “It’s a bang-for-your-buck argument,” Estelle says. “You probably want to hire more engineers instead” of native speakers.

But the need to serve 1 billion customers has a way of forcing the company to trade understanding for expediency. You don’t have to push Google Translate very far to see the compromises its developers have made for coverage, and speed, and ease of engineering. Although Google Translate captures, in its way, the products of human intelligence, it isn’t intelligent itself.

“Did we sit down when we built Watson and try to model human cognition?” Dave Ferrucci, who led the Watson team at IBM, pauses for emphasis. “Absolutely not. We just tried to create a machine that could win at Jeopardy.”

For Ferrucci, the definition of intelligence is simple: it’s what a program can do. Deep Blue was intelligent because it could beat Garry Kasparov at chess. Watson was intelligent because it could beat Ken Jennings at Jeopardy.

“There’s a limited number of things you can do as an individual, and I think when you dedicate your life to something, you’ve got to ask yourself the question: To what end? And I think at some point I asked myself that question, and what it came out to was, I’m fascinated by how the human mind works, it would be fantastic to understand cognition, I love to read books on it, I love to get a grip on it”—he called Hofstadter’s work inspiring—“but where am I going to go with it? Really what I want to do is build computer systems that do something.

Peter Norvig, one of Google’s directors of research, echoes Ferrucci almost exactly. “I thought he was tackling a really hard problem,” he told me about Hofstadter’s work. “And I guess I wanted to do an easier problem.”

Of course, the folly of being above the fray is that you’re also not a part of it

As our machines get faster and ingest more data, we allow ourselves to be dumber. Instead of wrestling with our hardest problems in earnest, we can just plug in billions of examples of them.

Hofstadter hasn’t been to an artificial-intelligence conference in 30 years. “There’s no communication between me and these people,” he says of his AI peers. “None. Zero. I don’t want to talk to colleagues that I find very, very intransigent and hard to convince of anything

Everything from plate tectonics to evolution—all those ideas, someone had to fight for them, because people didn’t agree with those ideas.

Academia is not an environment where you just sit in your bath and have ideas and expect everyone to run around getting excited. It’s possible that in 50 years’ time we’ll say, ‘We really should have listened more to Doug Hofstadter.’ But it’s incumbent on every scientist to at least think about what is needed to get people to understand the ideas.”

This idea seems laughable if you assume that most swing voters are fiscal conservatives and social moderates, allergic to culture-war appeals and pining for a dream ticket of Michael Bloomberg and Olympia Snowe. But as New York magazine’s Jonathan Chait has explained, there’s more than one kind of “moderate” in American politics:

There are, very roughly speaking, two kinds of swing voters. One kind is economically conservative, socially liberal swing voters. This is the kind of voter you usually read about, because it’s the kind most familiar to political reporters – affluent and college educated. But there’s a second kind of voter at least as numerous – economically populist and socially conservative. Think of disaffected blue-collar workers, downscale white men who love guns, hate welfare, oppose free trade, and want higher taxes on the rich and corporations. Romney appeals to the former, but Santorum more to the latter

That’s because the former senator has the instincts of an activist, rather than of a president or statesman.

a Rust Belt background would be a potential advantage for a Republican presidential candidate. But a Rust Belt background that includes an 17-point repudiation from the Pennsylvania electorate that knew Santorum best looks more like a liability instead.

both Catholicism and social conservatism are potential assets in a campaign against a president who has spoken condescendingly about Middle Americans who “get bitter” and “cling to guns or religion.” But a Catholic conservatism that manifests itself in campaign-trail critiques of contraception promises to alienate many more voters (female voters, especially) than it attracts.

All things being equal, a populist style that’s at odds with the Acela corridor’s attitudes and values can often play well in the heartland. But no presidential candidate can succeed without a modicum of favorable media coverage, and so a successful populist needs to be able to disarm elite journalists (as Huckabee so expertly did, schmoozing on The Daily Show and elsewhere) as often as he alienates them. And nobody has ever used the word “disarming” to describe Rick Santorum’s approach to politics.

his political persona is worlds away from the Washington-New York definitions of “middle-of-the-road.” But a mix of social conservatism and economic populism has a great deal of general-election potential – especially in a contest against a president whose style of liberalism can seem professorial, condescending and aloof.

Whether the topic is social issues or foreign policy, his zeal exceeds his prudence, and as a result his career is littered with debating society provocations (referencing “man-on-dog” sex in an argument about gay marriage, using his doomed 2006 Senate bid to educate Pennsylvanians on the evils of Hugo Chavez, etc.) that have won him far more enemies than friends. His passion for ideas and argument often does him credit, but in a national campaign it would probably do him in.

But Obama’s early opposition to the Iraq war gave him a political firewall that protected him throughout that primary campaign, while Romney has no such policy safe harbor to safeguard him from an intramural backlash.

Romney and Obama share a lack of natural affinity for this key group of swing voters, but it is Romney who needs to figure out some way of addressing this shortcoming if he wants to make it to the White House. It’s Romney’s misfortune that the voters’ prioritization of economic issues, his own privileged upbringing and his lack of connection with his party’s base on other core issues put him in a much more precarious position than candidate Obama ever reached.

By the time the 2008 general election rolled around, Obama had bolstered his outreach to these voters by recruiting the blue-collar avatar Joe Biden as his running mate. Should Romney win the Republican nomination this year, his advisers will almost certainly be tempted by the working-class credentials that a proletarian like New Jersey Governor Chris Christie or Florida Senator Marco Rubio would bring to the ticket.

Of more immediate concern to Team Romney should be how their candidate can overcome his habit of economic tone-deafness before Rick Santorum steals away enough working-class and culturally conservative voters to throw the Republican primary into complete and utter turmoil.

The curious thing about Romney’s verbal missteps is how limited they are to this very specific area of public policy. He is usually quite articulate when talking about foreign affairs and national security. Despite his complicated history on social and cultural matters like health care and abortion, his explanations are usually both coherent and comprehensible, even to those who oppose his positions. It’s only when he begins talking about economic issues – his biographical strength – that he seems to get clumsy.

The second possibility would be for him to outline a series of proposals specifically targeted at the needs of working-class and poor Americans, not only to control the damage from his gaffes but also to underscore the conservative premise that a right-leaning agenda will create opportunities for those on the lower rungs of the economic ladder. But while that approach might help Romney in a broader philosophical conversation, it’s unlikely to offer him much protection from the attacks and ridicule that his unforced errors will continue to bring him.

The question is why Romney hasn’t embraced a third alternative – admitting the obvious and then explaining why he gets so tongue-tied when the conversation turns to money. Romney’s upbringing and religious faith suggest a sense of obligation to the less fortunate and an unspoken understanding that it isn’t appropriate to call attention to one’s financial success.It wouldn’t be that hard for him to say something like:I was taught not to brag and boast and think I’m better than other people because of the successes I’ve had, so occasionally I’m going to say things that sound awkward. It’s because I’d rather talk about what it takes to get America back to work.

just as we saw in the summer with immigration writ large, the progression doesn't end with Trump. We've had three presidential elections since the 9/11 terror attacks and no presidential candidate has ever proposed shutting down mosques in the United States or creating a special registry and identification cards for Muslims living in the United States.

So yesterday Megyn Kelly asked Marco Rubio whether he'd shut down radical mosques like Trump. He tried to deflect the question by saying that it wasn't about mosques but closing down any facility that was promoting radicalism. In other words, Rubio, while clearly not eager to answer the question, pointedly refused to rule out following Trump's lead.

It is a very good example of how Trump is not only shaping the debate on the right but rapidly mainstreaming ideas that were as recently as a week ago considered entirely outside the realm of mainstream political discourse.

It's particularly effective with the less sophisticated and principled candidates like Rubio. Jeb Bush said flatly this morning that Trump's database proposal is "just wrong." But Ben Carson quickly took Trump's lead comparing Syrian refugees to "mad dogs." The difference is that Marco Rubio could very well be president in 18 months. Jeb Bush won't be.

this is no longer a matter of Trump yakking on about building a gilded 100-foot wall along the southern border and having Mexico agree to pay for it. Trump is now proposing things that sound like they put millions of American citizens and resident aliens on a road to something like the internment of Japanese Americans during World War II.

“There are a few reasons that explain conservatives who were Never-Trumpers during the election, and who remain anti-Trump today. [...] They do not believe that America is engaged in a civil war, with the survival of America as we know it at stake.

“That means praise him when he’s right, and find the most plausible possible defense when he’s wrong.”

“The conservative reaction to Trump’s Paris decision really drove home how this is all — and I do mean all — about waging culture war against the left.”

Rather than seeing the science in “pragmatic terms,” the president and the G.O.P. have made the issue into a “tribal struggle.” The cost of the right’s “desire to piss off lefty tree-huggers,” however, is an uncertain future for our grandchildren.

“A man who wished to become the most powerful man in the world [...] was granted his wish. Surely he must have imagined that more power meant more flattery, a grander image, a greater hall of mirrors reflecting back his magnificence. But he misunderstood power and prominence.”

“When it comes to decisions about strangers, the easiest, most accessible shortcut is our first impression. Unknowledgeable voters go for this shortcut.”

he writes about his work on first impressions and their effect on political outcomes. When we don’t have a lot of information, he explains, our brains rely on “shortcuts”; low-information voters tend to rely on appearance to guide their decisions.

the psychological tendency to favor information that confirms our beliefs

If this explanation is right, then there is a relatively straightforward solution to political polarization: We need to consciously expose ourselves to evidence that challenges our beliefs to compensate for our inclination to discount it.

But what if confirmation bias isn’t the only culprit? It recently struck us that confirmation bias is often conflated with “telling people what they want to hear,” which is actually a distinct phenomenon known as desirability bias, or the tendency to credit information you want to believe.

we decided to conduct an experiment that would isolate these biases

The results, which we report in a forthcoming paper in the Journal of Experimental Psychology: General, were clear and robust. Those people who received desirable evidence — polls suggesting that their preferred candidate was going to win — took note and incorporated the information into their subsequent belief

. In contrast, those people who received undesirable evidence barely changed their belief about which candidate was most likely to win.

we observed a general bias toward the desirable evidence.

What about confirmation bias? To our surprise, those people who received confirming evidence — polls supporting their prior belief about which candidate was most likely to win — showed no bias in favor of this information.

They tended to incorporate this evidence into their subsequent belief to the same extent as those people who had their prior belief disconfirmed. In other words, we observed little to no bias toward the confirming evidence.

Our study suggests that political belief polarization may emerge because of peoples’ conflicting desires, not their conflicting beliefs per se

This is rather troubling, as it implies that even if we were to escape from our political echo chambers, it wouldn’t help much. Short of changing what people want to believe, we must find other ways to unify our perceptions of reality.

with most polls indicating that it’s anyone’s guess who will carry Florida

held rallies in nearby Tampa on Thursday that underlined the polar-opposite realities each candidate now inhabits:

a conservative who said he had blocked Fox News on social media for being “too mainstream,”

“felt that it would be a good learning experience, so she could learn about some history and politics firsthand.”

Does he have a locker-room mentality?

“Yeah, sure, but I’m not hiring him to be nice.”

once overshadowed by Clearwater’s beach-retirement hub — has been transformed by recent years that have brought a mix of gentrification and progressive politics.

get-out-the-vote rally in honor of Representative John Lewis, the Georgia Democrat and voting rights champion who died over the summer

One in five Black Floridians is thought to have a felony conviction and could be unable to vote because of it.

“I’m not encouraging anyone to break the law, but today is a day for some ‘good trouble,’” she added, using one of Mr. Lewis’s signature phrases from his civil rights struggles

Mr. Overcast sighed. He said that living in the same county as the Church of Scientology’s headquarters had taught him how to tolerate almost any point of view, even that of the neighbor, who he suspected had stolen the Trump sign from his lawn.Mr. Overcast said his home security system had filmed the culprit making off with it. But rather than confront the neighbor, Mr. Overcast just bought another sign.“But with this one I tied 90 pounds of fishing line on it and tied it to a tree in my yard,” he said.

Billy Overcast — “spelled like the weather,” said Mr. Overcast, pointing up, however, to a typically clear day with one hand, a “Trump 2020” flag in the other.

Clearwater

The Confederate flag also bothered him, he told his fellow Jeep enthusiasts, as did the systemic racism

decorated with red, white and blue balloons; an American flag; a Black Lives Matter flag; another flag with an L.G.B.T. rainbow he found on Amazon; and lots of Biden-Harris campaign paraphernalia.

St. Petersburg and conservative Clearwater, the vacation hub and base of the Church of Scientology.

in the swing state that crowned past presidents by small margins, much in this country is riding o

n the all-important question of who flies what flag on which Jeep.

Pinellas County, about three-quarters of which is white, includes a good number of older retirees and suburban women

favor Joseph R. Biden Jr., the Democratic presidential nominee.

256,000 are registered as Democrats

252,000 as Republicans.

a swing county in a swing state

Donald J. Trump narrowly triumphed here by roughly 6,000 votes, out of about half a million cast

But postelection observers said her appeals to middle-class voters in Pinellas County didn’t resonate as well as Mr. Obama’s had.

Of the 32 teams in the NFL, just four have a nonwhite head coach. Of the five head coaching vacancies in the offseason, just one was filled by a nonwhite person when Ron Rivera, who is Hispanic, was hired by Washington

The NFL has been criticized for its response to former 49ers quarterback Colin Kaepernick kneeling during the national anthem over the 2016 season to protest police brutality and racial injustice. When Kaepernick became a free agent in 2017, no team offered him a contract and he accused team owners of colluding to keep him from being signed.

Now, clubs will be required to interview at least two minority candidates for head coach vacancies; at least one minority candidate for any of the three coordinator vacancies; and at least one external minority candidate for the senior football operations or general manager position.

Minority and female applicants must also be included in the interview processes for senior level front office positions such as club president and senior executives in communications, finance, human resources, legal, football operations, sales, marketing, sponsorship, information technology, and security positions.