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“Educational investment will not be attractive to those without unique skills,” she said. Graduates, she noted, need to earn a return on their investment in training: “But instead they will lose jobs, replaced by information simulation. They will stay uneducated.” In such a scenario, high-salary jobs would remain for those equipped with problem-solving skills, she predicted. But many common tasks now done by college graduates might vanish.

Over the next 10 to 20 years, “10 percent to 20 percent pushed out of work by A.I. will be a catastrophe,” she says. “I can’t begin to think what 50 percent would mean — way beyond a catastrophe and such numbers can’t be ruled out if A.I. performs well in the future.”

A recent study published by the Program on the Impacts of Future Technology, at Oxford University’s Oxford Martin School, predicted that nearly half of all jobs in the United States could be replaced by computers over the next two decades.

Smart machines will give companies “the opportunity to automate many tasks, redesign jobs, and do things never before possible even with the best human work forces,” according to a report this year by the business consulting firm McKinsey.

Advances in speech recognition, translation and pattern recognition threaten employment in the service sectors — call centers, marketing and sales — precisely the sectors that provide most jobs in developed economies.

Gartner’s 2013 chief executive survey, published in April, found that 60 percent of executives surveyed dismissed as “‘futurist fantasy” the possibility that smart machines could displace many white-collar employees within 15 years.

Kenneth Brant, research director at Gartner, told a conference in October: “Job destruction will happen at a faster pace, with machine-driven job elimination overwhelming the market’s ability to create valuable new ones.”

Optimists say this could lead to the ultimate elimination of work — an “Athens without the slaves” — and a possible boom for less vocational-style education. Mr. Brant’s hope is that such disruption might lead to a system where individuals are paid a citizen stipend and be free for education and self-realization. “This optimistic scenario I call Homo Ludens, or ‘Man, the Player,’ because maybe we will not be the smartest thing on the planet after all,” he said. “Maybe our destiny is to create the smartest thing on the planet and use it to follow a course of self-actualization.”

Nate Silver, who runs the political Web site FiveThirtyEight, now part of the New York Times, uses many of Tufte’s maxims in the site’s design. Silver told me that he tries to keep the “data-ink” ratio of his current site very high, meaning most of the pixels on the screen show actual numbers or data points; he also thinks of the site’s design in terms of “small multiples,” another Tufte neologism that refers to a series of related numbers that reveal subtle differences over time. “Tufte treats data like good writing,” he said. “You have a certain thought—how clearly and beautifully are you conveying it?”

Good design, then, is not about making dull numbers somehow become magically exhilarating, it is about picking the right numbers in the first place. “It’s about data that matters to you,”

“A lot of experts have long been saying that one of the results of an Israeli attack on Iran could be a dramatic acceleration of the Iranian nuclear program,” Mr. Diskin said at a community forum in Kfar Saba, a central Israeli city of 80,000. “What the Iranians prefer to do today slowly and quietly, they would have the legitimacy to do quickly and in a much shorter time.”

Shin Bet does not deal with foreign affairs, and Mr. Diskin was careful to say that he was not saying that attacking Iran “is not a legitimate decision,” but instead he was questioning the leaders’ motives and abilities. “I am just very afraid that they are not the people who I truly would want to be holding the wheel when we set out on an endeavor of that sort,

Mr. Diskin’s comments were significant because he left the government in good stead with Mr. Netanyahu — unlike Mr. Dagan, who was forced out — and because he was widely respected “for being professional and honest and completely disconnected from politics.”

Mr. Diskin did not limit his critique to Iran. He said Israel had in recent years become “more and more racist,” and, invoking the 1995 assassination of Yitzhak Rabin, said there are many extremist Jews today who “would be willing to take up arms against their Jewish brothers.”

Self-destructive behaviors — dropping out of school, joining a gang, taking drugs, bearing children when one isn’t ready — compound poverty.

Yet scholars are also learning to understand the roots of these behaviors, and they’re far more complicated than the conservative narrative of human weakness.

For starters, there is growing evidence that poverty and mental health problems are linked in complex, reinforcing ways

If you’re battling mental health problems, or grow up with traumas like domestic violence (or seeing your brother shot dead), you’re more likely to have trouble in school, to self-medicate with drugs or alcohol, to have trouble in relationships.

A second line of research has shown that economic stress robs us of cognitive bandwidth.

Worrying about bills, food or other problems, leaves less capacity to think ahead or to exert self-discipline. So, poverty imposes a mental tax.

It turns out that when people have elevated levels of cortisol, a stress hormone, they are less willing to delay gratification.

it’s circumstances that can land you in a situation where it’s really hard to make a good decision because you’re so stressed out. And the ones you get wrong matter much more, because there’s less slack to play with.”

That emphasis on personal responsibility is part of the 12-step program to confront alcoholism or drug addiction, and it may be useful for people like Jackson. But for society to place the blame entirely on the individual seems to me a cop-out.

Let’s also remember, though, that today we have randomized trials — the gold standard of evidence — showing that certain social programs make self-destructive behaviors less common.

as long as we’re talking about personal irresponsibility, let’s also examine our own. Don’t we have a collective responsibility to provide more of a fair start in life to all, so that children aren’t propelled toward bad choices?

a growing number of scientists are using autonomous robots to interrogate animal behavior and cognition. Researchers have designed robots to behave like ants, cockroaches, rodents, chickens, and more, then deployed their bots in the lab or in the environment to see how similarly they behave to their flesh-and-blood counterparts.

robots give behavioral biologists the freedom to explore the mind of an animal in ways that would not be possible with living subjects, says University of Sheffield researcher James Marshall, who in March helped launch a 3-year collaborative project to build a flying robot controlled by a computer-run simulation of the entire honeybee brain.

“I really think there is a lot to be discovered by doing the engineering side along with the science.”

Not only did the bots move around the space like the rat pups did, they aggregated in remarkably similar ways to the real animals.3 Then Schank realized that there was a bug in his program. The robots weren’t following his predetermined rules; they were moving randomly.

Animal experiments are still needed to advance neuroscience.” But, he adds, robots may prove to be an indispensable new ethological tool for focusing the scope of research. “If you can have good physical models,” Prescott says, “then you can reduce the number of experiments and only do the ones that answer really important questions.”

animal-mimicking robots is not easy, however, particularly when knowledge of the system’s biology is lacking.

However, when the researchers also gave the robots a sense of flow, and programmed them to assume that odors come from upstream, the bots much more closely mimicked real lobster behavior. “That was a demonstration that the animals’ brains were multimodal—that they were using chemical information and flow information,” says Grasso, who has since worked on robotic models of octopus arms and crayfish.

some sense, the use of robotics in animal-behavior research is not that new. Since the inception of the field of ethology, researchers have been using simple physical models of animals—“dummies”—to examine the social behavior of real animals, and biologists began animating their dummies as soon as technology would allow. “The fundamental problem when you’re studying an interaction between two individuals is that it’s a two-way interaction—you’ve got two players whose behaviors are both variable,”

building a robot that animals will accept as one of their own is complicated, to say the least.

handful of other researchers have also successfully integrated robots with live animals—including fish, ducks, and chickens. There are several notable benefits to intermixing robots and animals; first and foremost, control. “One of the problems when studying behavior is that, of course, it’s very difficult to have control of animals, and so it’s hard for us to interpret fully how they interact with each other

The real payoff in subscribing to the idea of a brain as a computer would come from using that idea to profitably guide research. In an article last fall in the journal Science, two of my colleagues (Adam Marblestone of M.I.T. and Thomas Dean of Google) and I endeavored to do just that, suggesting that a particular kind of computer, known as the field programmable gate array, might offer a preliminary starting point for thinking about how the brain works.

FIELD programmable gate arrays consist of a large number of “logic block” programs that can be configured, and reconfigured, individually, to do a wide range of tasks. One logic block might do arithmetic, another signal processing, and yet another look things up in a table. The computation of the whole is a function of how the individual parts are configured. Much of the logic can be executed in parallel, much like what happens in a brain.

our suggestion is that the brain might similarly consist of highly orchestrated sets of fundamental building blocks, such as “computational primitives” for constructing sequences, retrieving information from memory, and routing information between different locations in the brain. Identifying those building blocks, we believe, could be the Rosetta stone that unlocks the brain.

it is unlikely that we will ever be able to directly connect the language of neurons and synapses to the diversity of human behavior, as many neuroscientists seem to hope. The chasm between brains and behavior is just too vast.

Our best shot may come instead from dividing and conquering. Fundamentally, that may involve two steps: finding some way to connect the scientific language of neurons and the scientific language of computational primitives (which would be comparable in computer science to connecting the physics of electrons and the workings of microprocessors); and finding some way to connect the scientific language of computational primitives and that of human behavior (which would be comparable to understanding how computer programs are built out of more basic microprocessor instructions).

If neurons are akin to computer hardware, and behaviors are akin to the actions that a computer performs, computation is likely to be the glue that binds the two.

In the nations that lead the international rankings — Singapore, Japan, South Korea, Finland, Canada — teachers are drawn from the top third of college graduates, rather than the bottom 60 percent as is the case in the United States. Training in these countries is more rigorous, more tied to classroom practice and more often financed by the government than in America. There are also many fewer teacher-training institutions, with much higher standards.

By these criteria, American education is a failed profession. There is no widely agreed-upon knowledge base, training is brief or nonexistent, the criteria for passing licensing exams are much lower than in other fields, and there is little continuous professional guidance. It is not surprising, then, that researchers find wide variation in teaching skills across classrooms; in the absence of a system devoted to developing consistent expertise, we have teachers essentially winging it as they go along, with predictably uneven results.

Teaching requires a professional model, like we have in medicine, law, engineering, accounting, architecture and many other fields. In these professions, consistency of quality is created less by holding individual practitioners accountable and more by building a body of knowledge, carefully training people in that knowledge, requiring them to show expertise before they become licensed, and then using their professions’ standards to guide their work.

hese elements create a virtuous cycle: strong academic performance leads to schools with greater autonomy and more public financing, which in turn makes education an attractive profession for talented people.

These countries also have much stronger welfare states; by providing more support for students’ social, psychological and physical needs, they make it easier for teachers to focus on their academic needs.

Teachers in leading nations’ schools also teach much less than ours do. High school teachers provide 1,080 hours per year of instruction in America, compared with fewer than 600 in South Korea and Japan, where the balance of teachers’ time is spent collaboratively on developing and refining lesson plans

In America, both major teachers’ unions and the organization representing state education officials have, in the past year, called for raising the bar for entering teachers; one of the unions, the American Federation of Teachers, advocates a “bar exam.”

Ideally the exam should not be a one-time paper-and-pencil test, like legal bar exams, but a phased set of milestones to be attained over the first few years of teaching. Akin to medical boards, they would require prospective teachers to demonstrate subject and pedagogical knowledge — as well as actual teaching skill.

We let doctors operate, pilots fly, and engineers build because their fields have developed effective ways of certifying that they can do these things. Teaching, on the whole, lacks this specialized knowledge base; teachers teach based mostly on what they have picked up from experience and from their colleagues.

other fields spend 5 percent to 15 percent of their budgets on research and development, while in education, it is around 0.25 percent

Education-school researchers publish for fellow academics; teachers develop practical knowledge but do not evaluate or share it; commercial curriculum designers make what districts and states will buy, with little regard for quality.

Early- to mid-career teachers need time to collaborate and explore new directions — having mastered the basics, this is the stage when they can refine their skills. The system should reward master teachers with salaries commensurate with leading professionals in other fields.

research suggests that the labels don’t matter — there are good and bad programs of all types, including university-based ones. The best programs draw people who majored as undergraduates in the subjects they wanted to teach; focus on extensive clinical practice rather than on classroom theory; are selective in choosing their applicants rather than treating students as a revenue stream; and use data about how their students fare as teachers to assess and revise their practice.

It is nerdy-sounding, but I believe critical to this discussion, to distinguish between measurement and knowledge. I made a very strong claim about measurement, and a very specific claim about knowledge. I claim that we cannot usefully measure the effect of the stimulus program launched in 2009 at all.

All potentially useful predictions made about the output impact of the stimulus program are non-falsifiable. Failure of predictions can be simply justified by this sort of ad hoc explanation after the fact.

This argument will always degenerate back into endlessly dueling regressions, because there is no ability to adjudicate among them via experiment.

It simply means that we have no scientific knowledge about this topic. Macroeconomic assertions about the effect of a proposed stimulus policy are not valueless, but despite their complex mathematical justifications, do not have standing as knowledge that can trump common sense, historical reasoning, and so on in the same way that a predictive rule that has been verified through experimental testing can.

When using stimulus to ameliorate the economic crisis, we are like primitive tribesmen using herbs to treat an infection, and we should not allow ourselves to imagine that we are using antibiotics that have been proven through clinical trials. This should not imply merely a different feeling about the same actions, but should rationally lead us to greater circumspection.

For constrained domains the GOFAI approach is a winning strategy.

there is nothing intelligent or even interesting about the brute force approach.

the dominant paradigm in AI research has largely “moved on from GOFAI to embodied, distributed intelligence.” And Faustus from Cincinnati insists that as a result “machines with bodies that experience the world and act on it” will be “able to achieve intelligence.”

The new, embodied paradigm in AI, deriving primarily from the work of roboticist Rodney Brooks, insists that the body is required for intelligence. Indeed, Brooks’s classic 1990 paper, “Elephants Don’t Play Chess,” rejected the very symbolic computation paradigm against which Dreyfus had railed, favoring instead a range of biologically inspired robots that could solve apparently simple, but actually quite complicated, problems like locomotion, grasping, navigation through physical environments and so on. To solve these problems, Brooks discovered that it was actually a disadvantage for the system to represent the status of the environment and respond to it on the basis of pre-programmed rules about what to do, as the traditional GOFAI systems had. Instead, Brooks insisted, “It is better to use the world as its own model.”

although they respond to the physical world rather well, they tend to be oblivious to the global, social moods in which we find ourselves embedded essentially from birth, and in virtue of which things matter to us in the first place.

the embodied AI paradigm is irrelevant to Watson. After all, Watson has no useful bodily interaction with the world at all.

The statistical machine learning strategies that it uses are indeed a big advance over traditional GOFAI techniques. But they still fall far short of what human beings do.

“The illusion is that this computer is doing the same thing that a very good ‘Jeopardy!’ player would do. It’s not. It’s doing something sort of different that looks the same on the surface. And every so often you see the cracks.”

Watson doesn’t understand relevance at all. It only measures statistical frequencies. Because it is relatively common to find mismatches of this sort, Watson learns to weigh them as only mild evidence against the answer. But the human just doesn’t do it that way. The human being sees immediately that the mismatch is irrelevant for the Erie Canal but essential for Toronto. Past frequency is simply no guide to relevance.

The fact is, things are relevant for human beings because at root we are beings for whom things matter. Relevance and mattering are two sides of the same coin. As Haugeland said, “The problem with computers is that they just don’t give a damn.” It is easy to pretend that computers can care about something if we focus on relatively narrow domains — like trivia games or chess — where by definition winning the game is the only thing that could matter, and the computer is programmed to win. But precisely because the criteria for success are so narrowly defined in these cases, they have nothing to do with what human beings are when they are at their best.

Far from being the paradigm of intelligence, therefore, mere matching with no sense of mattering or relevance is barely any kind of intelligence at all. As beings for whom the world already matters, our central human ability is to be able to see what matters when.

But, as we show in our recent book, this is an existential achievement orders of magnitude more amazing and wonderful than any statistical treatment of bare facts could ever be. The greatest danger of Watson’s victory is not that it proves machines could be better versions of us, but that it tempts us to misunderstand ourselves as poorer versions of them.

“We’re at the beginning of a 10-year period where we’re going to transition from computers that can’t understand language to a point where computers can understand quite a bit about language.”

E-discovery technologies generally fall into two broad categories that can be described as “linguistic” and “sociological.”

The most basic linguistic approach uses specific search words to find and sort relevant documents. More advanced programs filter documents through a large web of word and phrase definitions.

The sociological approach adds an inferential layer of analysis, mimicking the deductive powers of a human Sherlock Holmes

But it is an explanation

it is naïve to expect that we ever really can overcome the most basic instinct of all, the instinct to keep ourselves alive. Rational decision making may seem intelligent. Moral decision making may seem honorable. The survival imperative trumps them both.

“Over the past 20 years the average increase in spending on some items has exceeded the growth of incomes. The gap is especially poignant for those under 25 years old.”

As of 2013, tuition at a private university was projected to cost nearly $130,000 on average over four years, and that’s not counting food, lodging, books, or other expenses.

Sure, there are still some scholarships and grants available. But even as college costs rise, the availability of those programs is falling, leaving middle-class and lower-income students further in debt as out-of-pocket costs rise.

Even as overall wealth in this country has shifted upward, away from middle-class families, the cost of medical care is increasingly being borne by the families themselves. As the Milliman study shows, the employer-funded portion of healthcare costs has risen 52 percent since 2007, the first year of the recession. But household costs have risen by a staggering 73 percent, or 8 percent per year, and now average $9,144.

he financial crisis of 2008, driven by the greed of Wall Street one percenters, robbed most American household of their primary assets. And right-wing “centrists” of both parties, not satisfied with the rising retirement age which has already cut the program’s benefits, continue to press for even deeper cuts to the program.

Vacations; an education; staying home to raise your kids; a life without crushing debt; seeing the doctor when you don’t feel well; a chance to retire: one by one, these mainstays of middle-class life are disappearing for most Americans. Until we demand political leadership that will do something about it, they’re not coming back.

Can the American dream be restored? Yes, but it will take concerted effort to address two underlying problems. First, we must end the domination of our electoral process by wealthy and powerful elites. At the same time, we must begin to address the problem of growing economic inequality. Without a national movement to call for change, change simply isn’t going to happen.

However, as Kevin Slavin, a research affiliate at M.I.T.’s Media Lab said, “We are now writing algorithms we cannot read. That makes this a unique moment in history, in that we are subject to ideas and actions and efforts by a set of physics that have human origins without human comprehension.

As Slavin has often noted, “It’s a bright future, if you’re an algorithm.”

“Today, programmers use stuff that Knuth, and others, have done as components of their algorithms, and then they combine that together with all the other stuff they need,”

“With A.I., we have the same thing. It’s just that the combining-together part will be done automatically, based on the data, rather than based on a programmer’s work. You want A.I. to be able to combine components to get a good answer based on the data

But you have to decide what those components are. It could happen that each component is a page or chapter out of Knuth, because that’s the best possible way to do some task.”

“I am worried that algorithms are getting too prominent in the world,” he added. “It started out that computer scientists were worried nobody was listening to us. Now I’m worried that too many people are listening.”

In January, Facebook threw a wrench into that media economy by changing its branded content policy to prohibit creators from accepting money for such link-sharing deals, and re-engineering its News Feed algorithms. Traffic to many viral publishers plummeted overnight. LittleThings, a female-focused digital publisher that had amassed more than 12 million Facebook followers, announced that it was shutting down and blamed Facebook’s News Feed changes for cratering its organic traffic.

No matter how much data you have and how many patterns you discern, your data will never match the creativity of human beings or the fluidity of the real world. The universe of possible sentences is too complex. There is no end to the variety of life — or to the ways in which we can talk about that variety.

Once upon a time, before the fashionable rise of machine learning and “big data,” A.I. researchers tried to understand how complex knowledge could be encoded and processed in computers. This project, known as knowledge engineering, aimed not to create programs that would detect statistical patterns in huge data sets but to formalize, in a system of rules, the fundamental elements of human understanding, so that those rules could be applied in computer programs.

That job proved difficult and was never finished. But “difficult and unfinished” doesn’t mean misguided. A.I. researchers need to return to that project sooner rather than later, ideally enlisting the help of cognitive psychologists who study the question of how human cognition manages to be endlessly flexible.

It just learns everything from us and as our echo, picks up the prejudices we’ve become deaf to.

AI will have to be programmed to embrace equality.

These confessions can be explained by the field of behavioral economics, which holds that human decision-making departs frequently, significantly and predictably from what would be expected if we acted in purely “rational” ways.

Rather, our behavior is powerfully influenced by our emotions, identity and environment, as well as by how options are presented to us.

(organ donation rates are over 90 percent in countries where citizens need to override a default and opt out of donation compared with 4 to 27 percent where they much choose to opt in)

Employees were randomly assigned to one of three groups. The first was “usual care,” in which they received educational materials and free smoking cessation aids. The second was a reward program: Employees could receive up to $800 over six months if they quit. The third was a deposit program, in which smokers initially forked over $150 of their money, but if they quit, they got their deposit back along with a $650 bonus.

Those in the lottery group were eligible for a daily lottery prize with frequent small payouts and occasional large rewards — but only if they clocked in at or below their weight loss goal.