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“Major economies need to ramp up their ambition – starting with the U.S., where expectations are high for an emissions and finance pledge to make up for lost time. Others like Japan, Canada, Korea, New Zealand and China, have committed to net zero goals by mid-century, but we are still missing their promised new near-term plans to get there,” she said in a statement released ahead of the report.

“This study is an important step in trying to understand the roots of common-sense understanding of other people’s actions. It shows quite strikingly that in some sense, the basic math that is at the heart of how economists think about rational choice is very intuitive to babies who don’t know math, don’t speak, and can barely understand a few words

“Abstract, interrelated concepts like cost and value — concepts at the center both of our intuitive psychology and of utility theory in philosophy and economics — may originate in an early-emerging system by which infants understand other people's actions,” she says. 

In other words, they apply the well-known logic that all of us rely on when we try to assess someone’s preferences: The harder she tries to achieve something, the more valuable is the expected reward to her when she succeeds.”

“We have to recognize that we’re very far from building AI systems that have anything like the common sense even of a 10-month-old,” Tenenbaum says. “But if we can understand in engineering terms the intuitive theories that even these young infants seem to have, that hopefully would be the basis for building machines that have more human-like intelligence.

Games are more consistent at rewarding us for the choices we make, and they also provide a diversity of choice that the real world doesn’t provide.

Many modern – even violent – games might be better teaching tools than we realize.

“I think games can provide a framework for understanding contemporary issues such as governmental budgets and spending,”

Building blocks of a better world

Aside from the physical benefits of gaming, video games excel at setting clear goals and showing a player’s progression towards those goals.

The playful nature of video games lowers the barrier of entry for people to get behind new social causes.

When used correctly, video games hold the potential to show us the world through a different set of lenses

those who play games feel a need to break free from the mundane slavery of their reality.

how they encourage or discourage violence, inspire creativity, or nurture laziness.

the human ability to play is so powerful.

ones more fantastical than our own

enjoy retreats to other realities

Immersyve’s complex needs-satisfaction metrics narrow down to three basic categories.

desire to seek out control or to feel mastery over a situation.

need for competence

People like to feel successful, and we like to feel like we’re growing and progressing in our knowledge and accomplishments.

video games make us feel more accomplished.

need is autonomy

the desire to feel independent or have a certain amount of control over our actions.

need is relatedness.

gamers can fulfill this need for relatedness by playing games with friends online,

make us feel more competent, more autonomous, and more related because these experiences make us feel good and keep us mentally healthy.

In 2014, the Pew Research Center asked Americans to rank the “greatest dangers in the world.” A plurality put inequality first, ahead of “religious and ethnic hatred,” nuclear weapons, and environmental degradation. And yet people don’t agree about what, exactly, “equality” means.

One side argues that the city should guarantee procedural equality: it should insure that all students and families are equally informed about and encouraged to study for the entrance exam. The other side argues for a more direct, representation-based form of equality: it would jettison the exam, adopting a new admissions system designed to produce student bodies reflective of the city’s demography

In the past year, for example, New York City residents have found themselves in a debate over the city’s élite public high schools

The complexities of egalitarianism are especially frustrating because inequalities are so easy to grasp. C.E.O.s, on average, make almost three hundred times what their employees make; billionaire donors shape our politics; automation favors owners over workers; urban economies grow while rural areas stagnate; the best health care goes to the richest.

It’s not just about money. Tocqueville, writing in 1835, noted that our “ordinary practices of life” were egalitarian, too: we behaved as if there weren’t many differences among us. Today, there are “premiere” lines for popcorn at the movies and five tiers of Uber;

Inequality is everywhere, and unignorable. We’ve diagnosed the disease. Why can’t we agree on a cure?

In a book based on those lectures, “One Another’s Equals: The Basis of Human Equality,” Waldron points out that people are also marked by differences of skill, experience, creativity, and virtue. Given such consequential differences, he asks, in what sense are people “equal”?

According to the Declaration of Independence, it is “self-evident” that all men are created equal. But, from a certain perspective, it’s our inequality that’s self-evident.

More than twenty per cent of Americans, according to a 2015 poll, agree: they believe that the statement “All men are created equal” is false.

In Waldron’s view, though, it’s not a binary choice; it’s possible to see people as equal and unequal simultaneously. A society can sort its members into various categories—lawful and criminal, brilliant and not—while also allowing some principle of basic equality to circumscribe its judgments and, in some contexts, override them

Egalitarians like Dworkin and Waldron call this principle “deep equality.” It’s because of deep equality that even those people who acquire additional, justified worth through their actions—heroes, senators, pop stars—can still be considered fundamentally no better than anyone else.

In the course of his search, he explores centuries of intellectual history. Many thinkers, from Cicero to Locke, have argued that our ability to reason is what makes us equals.

Other thinkers, including Immanuel Kant, have cited our moral sense.

Some philosophers, such as Jeremy Bentham, have suggested that it’s our capacity to suffer that equalizes us

Waldron finds none of these arguments totally persuasive.

In various religious traditions, he observes, equality flows not just from broad assurances that we are all made in God’s image but from some sense that everyone is the protagonist in a saga of error, realization, and redemption: we’re equal because God cares about how things turn out for each of us.

Waldron himself is taken by Hannah Arendt’s related concept of “natality,” the notion that what each of us share is having been born as a “newcomer,” entering into history with “the capacity of beginning something anew, that is, of acting.”

equality may be not a self-evident fact about human beings but a human-made social construction that we must choose to put into practice.

In the end, Waldron concludes that there is no “small polished unitary soul-like substance” that makes us equal; there’s only a patchwork of arguments for our deep equality, collectively compelling but individually limited.

Equality is a composite idea—a nexus of complementary and competing intuitions.

The blurry nature of equality makes it hard to solve egalitarian dilemmas from first principles. In each situation, we must feel our way forward, reconciling our conflicting intuitions about what “equal” means.

The communities that have the easiest time doing that tend to have some clearly defined, shared purpose. Sprinters competing in a hundred-metre dash have varied endowments and train in different conditions; from a certain perspective, those differences make every race unfair.

By embracing an agreed-upon theory of equality before the race, the sprinters can find collective meaning in the ranked inequalities that emerge when it ends

Perhaps because necessity is so demanding, our egalitarian commitments tend to rest on a different principle: luck.

“Some people are blessed with good luck, some are cursed with bad luck, and it is the responsibility of society—all of us regarded collectively—to alter the distribution of goods and evils that arises from the jumble of lotteries that constitutes human life as we know it.” Anderson, in an influential coinage, calls this outlook “luck egalitarianism.”

This sort of artisanal egalitarianism is comparatively easy to arrange. Mass-producing it is what’s hard. A whole society can’t get together in a room to hash things out. Instead, consensus must coalesce slowly around broad egalitarian principles.

No principle is perfect; each contains hidden dangers that emerge with time. Many people, in contemplating the division of goods, invoke the principle of necessity: the idea that our first priority should be the equal fulfillment of fundamental needs. The hidden danger here becomes apparent once we go past a certain point of subsistence.

a core problem that bedevils egalitarianism—what philosophers call “the problem of expensive tastes.”

The problem—what feels like a necessity to one person seems like a luxury to another—is familiar to anyone who’s argued with a foodie spouse or roommate about the grocery bil

The problem is so insistent that a whole body of political philosophy—“prioritarianism”—is devoted to the challenge of sorting people with needs from people with wants

the line shifts as the years pass. Medical procedures that seem optional today become necessities tomorrow; educational attainments that were once unusual, such as college degrees, become increasingly indispensable with time

Some thinkers try to tame the problem of expensive tastes by asking what a “normal” or “typical” person might find necessary. But it’s easy to define “typical” too narrowly, letting unfair assumptions influence our judgment

an odd feature of our social contract: if you’re fired from your job, unemployment benefits help keep you afloat, while if you stop working to have a child you must deal with the loss of income yourself. This contradiction, she writes, reveals an assumption that “the desire to procreate is just another expensive taste”; it reflects, she argues, the sexist presumption that “atomistic egoism and self-sufficiency” are the human norm. The word “necessity” suggests the idea of a bare minimum. In fact, it sets a high bar. Clearing it may require rethinking how society functions.

he forces his employees to bridge the enormous gap between technological reality and his dreams. This disconnect fosters a negligent and sometimes cruel workplace, to disastrous effect.

That fully self-driving announcement that so delighted his fans came as a far more jarring revelation to the project’s engineers, who found out about their staggering new mission when Mr. Musk tweeted about it.

This is the fundamental weakness of every organization run as a cult of personality: The dear leader can’t be everywhere or make every decision but often fails to provide the clear code of values that allows managers to independently shape their decisions around common goals.

Lawsuits by workers and California’s Department of Fair Employment and Housing allege that Black workers were tasked with menial physical labor in parts of the factory nicknamed “the plantation,” where they were subjected to racist slurs and graffiti.

He ultimately gave up and cobbled together a manual-labor-intensive production line in an open-air tent.

Female workers have sued, alleging a pervasive culture of sexual harassment and groping by supervisors. Mr. Musk was indifferent, emailing workers who experienced abuse that “it is important to be thick-skinned.”

lantatio

Mr. Musk’s reliance on hype is especially jarring.

By moving to buy Twitter, Mr. Musk has not only added another distraction to his long list but has also already shown the same drive to announce sweeping decisions in public.

Ultimately Mr. Musk’s goals for Twitter, as they are for Tesla, are not about making the right decisions for his companies or the people who make them possible.

They are about playing to the crowd and burnishing the legend that keeps fresh bodies and minds moving through the businesses that chew them up and spit them out.

In the summer of 2020, “public-health experts” decided that racism is a public-health crisis comparable to the coronavirus pandemic. It was therefore, they claimed, within their purview to express public support for the Black Lives Matter protests following the murder of George Floyd and to argue that the benefits of such protests outweighed the increased risk of spreading the disease. Those supposed experts actually knew nothing about the likely effects of the protests. They made no concrete predictions about whether they would in any way ameliorate racism in America, just as Nature can make no concrete predictions about whether its political endorsements will actually help a preferred candidate without jeopardizing its other important goals. The political action was expressive, not evidence-based...

as is often the case, a debate which appears to be about the neutrality of institutions is not really about neutrality at all... Rather, it is about whether there is any room left for soberly weighing our goals and values and thinking in a measured way about the consequences of our actions rather than simply reacting to situations in an impulsive and expressive manner, broadcasting our views to the world so that people know where we stand.

Our goals and values might not be “neutral” at all, but they might still be best served by procedures, institutions, and even individuals that follow neutral principles.

“Historically,” says Greg Blatt, the CEO of Match.com’s parent company, “relationships have been billed as ‘hard’ because, historically, commitment has been the goal. You could say online dating is simply changing people’s ideas about whether commitment itself is a life value.” Mate scarcity also plays an important role in people’s relationship decisions. “Look, if I lived in Iowa, I’d be married with four children by now,” says Blatt, a 40‑something bachelor in Manhattan. “That’s just how it is.”

“I think divorce rates will increase as life in general becomes more real-time,” says Niccolò Formai, the head of social-media marketing at Badoo, a meeting-and-dating app with about 25 million active users worldwide. “Think about the evolution of other kinds of content on the Web—stock quotes, news. The goal has always been to make it faster. The same thing will happen with meeting. It’s exhilarating to connect with new people, not to mention beneficial for reasons having nothing to do with romance. You network for a job. You find a flatmate. Over time you’ll expect that constant flow. People always said that the need for stability would keep commitment alive. But that thinking was based on a world in which you didn’t meet that many people.”

“You could say online dating allows people to get into relationships, learn things, and ultimately make a better selection,” says Gonzaga. “But you could also easily see a world in which online dating leads to people leaving relationships the moment they’re not working—an overall weakening of commitment.”

Explaining the mentality of a typical dating-site executive, Justin Parfitt, a dating entrepreneur based in San Francisco, puts the matter bluntly: “They’re thinking, Let’s keep this fucker coming back to the site as often as we can.” For instance, long after their accounts become inactive on Match.com and some other sites, lapsed users receive notifications informing them that wonderful people are browsing their profiles and are eager to chat. “Most of our users are return customers,” says Match.com’s Blatt.

The market is hugely more efficient … People expect to—and this will be increasingly the case over time—access people anywhere, anytime, based on complex search requests … Such a feeling of access affects our pursuit of love … the whole world (versus, say, the city we live in) will, increasingly, feel like the market for our partner(s). Our pickiness will probably increase.” “Above all, Internet dating has helped people of all ages realize that there’s no need to settle for a mediocre relationship.”

in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.

it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.

"Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?

AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.

The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.

I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.

if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.

if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.

take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no

if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.

that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.

it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.

people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.

with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject

You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.

What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh

at we're looking for anymore than Galileo did, and there's a lot to learn from that.

ARABIC IS ONE of the languages the U.S. Department of State dubs “extremely hard.” Chinese, Japanese, and Korean are the others. These languages’ structures are vastly different from that of English, and they are memorization-driven.

To help meet its language-learning goals, in 2003 the Department of Defense established the University of Maryland Center for Advanced Study of Language.

MICHAEL GEISLER, a vice president at Middlebury College, which runs the foremost language-immersion school in the country, was blunt: “The drill-and-kill approach we used 20 years ago doesn’t work.” He added, “The typical approach that most programs take these days—Rosetta Stone is one example—is scripted dialogue and picture association. You have a picture of the Eiffel Tower, and you have a sentence to go with it. But that’s not going to teach you the language.”

According to Geisler, you need four things to learn a language. First, you have to use it. Second, you have to use it for a purpose. Research shows that doing something while learning a language—preparing a cooking demonstration, creating an art project, putting on a play—stimulates an exchange of meaning that goes beyond using the language for the sake of learning it.Third, you have to use the language in context. This is where Geisler says all programs have fallen short.

Fourth, you have to use language in interaction with others. In a 2009 study led by Andrew Meltzoff at the University of Washington, researchers found that young children easily learned a second language from live human interaction while playing and reading books. But audio and DVD approaches with the same material, without the live interaction, fostered no learning progress at all. Two people in conversation constantly give each other feedback that can be used to make changes in how they respond.

our research shows that the ideal model is a blended one,” one that blends technology and a teacher. “Our latest research shows that with the proper use of technology and cognitive neuroscience, we can make language learning more efficient.” 

The school released its first two online programs, for French and Spanish, last year. The new courses use computer avatars for virtual collaboration; rich video of authentic, unscripted conversations with native speakers; and 3-D role-playing games in which students explore life in a city square, acting as servers and taking orders from customers in a café setting. The goal at the end of the day, as Geisler put it, is for you to “actually be able to interact with a native speaker in his own language and have him understand you, understand him, and, critically, negotiate when you don’t understand what he is saying.” 

The program includes the usual vocabulary lists and lessons in how to conjugate verbs, but students are also consistently immersed in images, audio, and video of people from different countries speaking with different accents. Access to actual teachers is another critical component.

The straight-A student said she hoped to study neuroscience at Harvard one day.

She hopes they see from her experiences that education is the best way to avoid repeating her struggles.

“This experience showed me that I’m raising well-rounded young ladies who can adapt to any situation and make the best of it,” Ms. Hopewell said. “Their capabilities are endless.”

ThinkUp is something like Elf on the Shelf for digitally addled adults — a constant reminder that someone is watching you, and that you’re being judged.

“The goal is to make you act like less of a jerk online,” Ms. Trapani said. “The big goal is to create mindfulness and awareness, and also behavioral change.”

One of the biggest dangers is saying something off the cuff that might make sense in a particular context, but that sounds completely off the rails to the wider public. The problem, in other words, is acting without thinking — being caught up in the moment, without pausing to reflect on the long-term consequences. You’re never more than a few taps away from an embarrassment that might ruin your career, or at least your reputation, for years to come.

Because social networks often suggest a false sense of intimacy, they tend to lower people’s self-control.

Like a drug or perhaps a parasite, they worm into your devices, your daily habits and your every free moment, and they change how you think.Continue reading the main story Continue reading the main story

For those of us most deeply afflicted, myself included, every mundane observation becomes grist for a 140-character quip, and every interaction a potential springboard into an all-consuming, emotionally wrenching flame battle.

people often tweet and update without any perspective about themselves. That’s because Facebook and Twitter, as others have observed, have a way of infecting our brains.

getting a daily reminder from ThinkUp that there are good ways and bad ways to behave online — has a tendency to focus the mind.

More basically, though, it’s helped me pull back from social networks. Each week, ThinkUp tells me how often I’ve tweeted. Sometimes that number is terribly high — a few weeks ago it was more than 800 times — and I realize I’m probably overtaxing my followers

ThinkUp charges $5 a month for each social network you connect to it. Is it worth it? After all, there’s a better, more surefire way of avoiding any such long-term catastrophe caused by social media: Just stop using social networks.

The main issue constraining growth, the founders say, is that it has been difficult to explain to people why they might need ThinkUp.

your online profile plays an important role in how you’re perceived by potential employers. In a recent survey commissioned by the job-hunting site CareerBuilder, almost half of companies said they perused job-seekers’ social networking profiles to look for red flags and to see what sort of image prospective employees portrayed online.

even though “never tweet” became a popular, ironic thing to tweet this year, actually never tweeting, and never being on Facebook, is becoming nearly impossible for many people.

That may change as more people falter on social networks, either by posting unthinking comments that end up damaging their careers, or simply by annoying people to the point that their online presence becomes a hindrance to their real-life prospects.

invoked an old metaphor to explain why he isn’t convinced by the analysis: He compared it to establishing a theory of how weather works by pointing a video camera out the window for 7 hours.

Indeed, among neuroscientists, the new “comprehensive atlas” of the cerebral cortex is almost as controversial as a historical atlas of the Middle East. That’s because every word has a constellation of meanings and associations — and it’s hard for scientists to agree about how best to study them in the lab.

For this study, neuroscientist Jack Gallant and his team at the University of California, Berkeley played more than two hours’ worth of stories from the Moth Radio Hour for seven grad students and postdocs while measuring their cerebral blood flow using functional magnetic resonance imaging. Then, they linked the activity in some 50,000 pea-sized regions of the cortex to the “meaning” of the words being heard at that moment.

How, you might ask, did they establish the meaning of words? The neuroscientists pulled all the nouns and verbs from the podcasts. With a computer program, they then looked across millions of pages of text to see how often the words from the podcasts are used near 985 common words taken from Wikipedia’s List of 1,000 Basic Words. “Wolf,” for instance, would presumably be used more often in proximity to “dog” than to, say, “eggplant.” Using that data, the program assigned numbers that approximated the meaning of each individual word from the podcasts — and, with some fancy number crunching, they figured out what areas of the brain were activated when their research subjects heard words with certain meanings.

Everyone agrees that the research is innovative in its method. After all, linking up the meanings of thousands of words to the second-by-second brain activity in thousands of tiny brain regions is no mean feat. “That’s way more data than any human being can possibly think about,” said Gallant.

What they can’t agree on is what it means. “In this study, our goal was not to ask a specific question. Our goal was to map everything so that we can ask questions after that,” said Gallant. “One of the most frequent questions we get is, ‘What does it mean?’ If I gave you a globe, you wouldn’t ask what it means, you’d start using it for stuff. You can look for the smallest ocean or how long it will take to get to San Francisco.”

This “data-driven approach” still involves assumptions about how to break up language into different categories of meaning

“Of course it’s a very simplified version of how meaning is captured in our minds, but it seems to be a pretty good proxy,” she said.

hordes of unanswered questions: “We can map where your brain represents the meaning of a narrative text that is associated with family, but we don’t know why the brain is responding to family at that location. Is it the word ‘father’ itself? Is it your memories of your own father? Is it your own thinking about being a parent yourself?” He hopes that it’s just those types of questions that researchers will ask, using his brain map as a guide.

You’re in a collective extended “survival vehicle” relationship.

In a kind of no-brainer biochemical “social contract,” bacterial colonies, like human communities, have to handle the “common good” (suppressing cheating, free-riding, the “tragedy of the commons,” etc).

We dominate because we’re the best cooperators (Yuval Harari).

Evolution is itself a free-floating logic pattern (for discovering other, ever more effective logic patterns, and enacting “competence without comprehension").

Evolution’s logic is like geometry’s: in both relevant patterns and results arise from the intrinsic logic of the elements involved. In geometry, it’s lines, planes, etc. In evolution it’s kinetic functions like survival, varying replication, and adaptation.

Technology has rewarded sprinters (people who can recognize and alertly post a message on Twitter about some interesting immediate event) and marathoners (people who can write large conceptual stories), but it has hurt middle-distance runners (people who write 800-word summaries of yesterday’s news conference).

Technology has rewarded graphic artists who can visualize data, but it has punished those who can’t turn written reporting into video presentations.

More generally, the age of brilliant machines seems to reward a few traits.

First, it rewards enthusiasm. The amount of information in front of us is practically infinite; so is that amount of data that can be collected with new tools. The people who seem to do best possess a voracious explanatory drive, an almost obsessive need to follow their curiosity.

Second, the era seems to reward people with extended time horizons and strategic discipline.

a human can provide an overall sense of direction and a conceptual frame. In a world of online distractions, the person who can maintain a long obedience toward a single goal, and who can filter out what is irrelevant to that goal, will obviously have enormous worth.

Third, the age seems to reward procedural architects. The giant Internet celebrities didn’t so much come up with ideas, they came up with systems in which other people could express ideas: Facebook, Twitter, Wikipedia, etc.

One of the oddities of collaboration is that tightly knit teams are not the most creative. Loosely bonded teams are, teams without a few domineering presences, teams that allow people to think alone before they share results with the group. So a manager who can organize a decentralized network around a clear question, without letting it dissipate or clump, will have enormous value.

Fifth, essentialists will probably be rewarded.

creativity can be described as the ability to grasp the essence of one thing, and then the essence of some very different thing, and smash them together to create some entirely new thing.

In the 1950s, the bureaucracy was the computer. People were organized into technocratic systems in order to perform routinized information processing.

now the computer is the computer. The role of the human is not to be dispassionate, depersonalized or neutral. It is precisely the emotive traits that are rewarded: the voracious lust for understanding, the enthusiasm for work, the ability to grasp the gist, the empathetic sensitivity to what will attract attention and linger in the mind.

Unable to compete when it comes to calculation, the best workers will come with heart in hand.

it’s impossible to know for sure, but, according to one publisher’s estimate, book sales in the U.S. now make up no more than seven per cent of the company’s roughly seventy-five billion dollars in annual revenue.

A monopoly is dangerous because it concentrates so much economic power, but in the book business the prospect of a single owner of both the means of production and the modes of distribution is especially worrisome: it would give Amazon more control over the exchange of ideas than any company in U.S. history.

“The key to understanding Amazon is the hiring process,” one former employee said. “You’re not hired to do a particular job—you’re hired to be an Amazonian. Lots of managers had to take the Myers-Briggs personality tests. Eighty per cent of them came in two or three similar categories, and Bezos is the same: introverted, detail-oriented, engineer-type personality. Not musicians, designers, salesmen. The vast majority fall within the same personality type—people who graduate at the top of their class at M.I.T. and have no idea what to say to a woman in a bar.”

According to Marcus, Amazon executives considered publishing people “antediluvian losers with rotary phones and inventory systems designed in 1968 and warehouses full of crap.” Publishers kept no data on customers, making their bets on books a matter of instinct rather than metrics. They were full of inefficiences, starting with overpriced Manhattan offices.

For a smaller house, Amazon’s total discount can go as high as sixty per cent, which cuts deeply into already slim profit margins. Because Amazon manages its inventory so well, it often buys books from small publishers with the understanding that it can’t return them, for an even deeper discount

According to one insider, around 2008—when the company was selling far more than books, and was making twenty billion dollars a year in revenue, more than the combined sales of all other American bookstores—Amazon began thinking of content as central to its business. Authors started to be considered among the company’s most important customers. By then, Amazon had lost much of the market in selling music and videos to Apple and Netflix, and its relations with publishers were deteriorating

In its drive for profitability, Amazon did not raise retail prices; it simply squeezed its suppliers harder, much as Walmart had done with manufacturers. Amazon demanded ever-larger co-op fees and better shipping terms; publishers knew that they would stop being favored by the site’s recommendation algorithms if they didn’t comply. Eventually, they all did.

Brad Stone describes one campaign to pressure the most vulnerable publishers for better terms: internally, it was known as the Gazelle Project, after Bezos suggested “that Amazon should approach these small publishers the way a cheetah would pursue a sickly gazelle.”

ithout dropping co-op fees entirely, Amazon simplified its system: publishers were asked to hand over a percentage of their previous year’s sales on the site, as “marketing development funds.”

The figure keeps rising, though less for the giant pachyderms than for the sickly gazelles. According to the marketing executive, the larger houses, which used to pay two or three per cent of their net sales through Amazon, now relinquish five to seven per cent of gross sales, pushing Amazon’s percentage discount on books into the mid-fifties. Random House currently gives Amazon an effective discount of around fifty-three per cent.

In December, 1999, at the height of the dot-com mania, Time named Bezos its Person of the Year. “Amazon isn’t about technology or even commerce,” the breathless cover article announced. “Amazon is, like every other site on the Web, a content play.” Yet this was the moment, Marcus said, when “content” people were “on the way out.”

By 2010, Amazon controlled ninety per cent of the market in digital books—a dominance that almost no company, in any industry, could claim. Its prohibitively low prices warded off competition

In 2004, he set up a lab in Silicon Valley that would build Amazon’s first piece of consumer hardware: a device for reading digital books. According to Stone’s book, Bezos told the executive running the project, “Proceed as if your goal is to put everyone selling physical books out of a job.”

Lately, digital titles have levelled off at about thirty per cent of book sales.

The literary agent Andrew Wylie (whose firm represents me) says, “What Bezos wants is to drag the retail price down as low as he can get it—a dollar-ninety-nine, even ninety-nine cents. That’s the Apple play—‘What we want is traffic through our device, and we’ll do anything to get there.’ ” If customers grew used to paying just a few dollars for an e-book, how long before publishers would have to slash the cover price of all their titles?

As Apple and the publishers see it, the ruling ignored the context of the case: when the key events occurred, Amazon effectively had a monopoly in digital books and was selling them so cheaply that it resembled predatory pricing—a barrier to entry for potential competitors. Since then, Amazon’s share of the e-book market has dropped, levelling off at about sixty-five per cent, with the rest going largely to Apple and to Barnes & Noble, which sells the Nook e-reader. In other words, before the feds stepped in, the agency model introduced competition to the market

But the court’s decision reflected a trend in legal thinking among liberals and conservatives alike, going back to the seventies, that looks at antitrust cases from the perspective of consumers, not producers: what matters is lowering prices, even if that goal comes at the expense of competition. Barry Lynn, a market-policy expert at the New America Foundation, said, “It’s one of the main factors that’s led to massive consolidation.”

Publishers sometimes pass on this cost to authors, by redefining royalties as a percentage of the publisher’s receipts, not of the book’s list price. Recently, publishers say, Amazon began demanding an additional payment, amounting to approximately one per cent of net sales

brick-and-mortar retailers employ forty-seven people for every ten million dollars in revenue earned; Amazon employs fourteen.

Since the arrival of the Kindle, the tension between Amazon and the publishers has become an open battle. The conflict reflects not only business antagonism amid technological change but a division between the two coasts, with different cultural styles and a philosophical disagreement about what techies call “disruption.”

Bezos told Charlie Rose, “Amazon is not happening to bookselling. The future is happening to bookselling.”

n Grandinetti’s view, the Kindle “has helped the book business make a more orderly transition to a mixed print and digital world than perhaps any other medium.” Compared with people who work in music, movies, and newspapers, he said, authors are well positioned to thrive. The old print world of scarcity—with a limited number of publishers and editors selecting which manuscripts to publish, and a limited number of bookstores selecting which titles to carry—is yielding to a world of digital abundance. Grandinetti told me that, in these new circumstances, a publisher’s job “is to build a megaphone.”

it offers an extremely popular self-publishing platform. Authors become Amazon partners, earning up to seventy per cent in royalties, as opposed to the fifteen per cent that authors typically make on hardcovers. Bezos touts the biggest successes, such as Theresa Ragan, whose self-published thrillers and romances have been downloaded hundreds of thousands of times. But one survey found that half of all self-published authors make less than five hundred dollars a year.

The business term for all this clear-cutting is “disintermediation”: the elimination of the “gatekeepers,” as Bezos calls the professionals who get in the customer’s way. There’s a populist inflection to Amazon’s propaganda, an argument against élitist institutions and for “the democratization of the means of production”—a common line of thought in the West Coast tech world

“Book publishing is a very human business, and Amazon is driven by algorithms and scale,” Sargent told me. When a house gets behind a new book, “well over two hundred people are pushing your book all over the place, handing it to people, talking about it. A mass of humans, all in one place, generating tremendous energy—that’s the magic potion of publishing. . . . That’s pretty hard to replicate in Amazon’s publishing world, where they have hundreds of thousands of titles.”

By producing its own original work, Amazon can sell more devices and sign up more Prime members—a major source of revenue. While the company was building the

Like the publishing venture, Amazon Studios set out to make the old “gatekeepers”—in this case, Hollywood agents and executives—obsolete. “We let the data drive what to put in front of customers,” Carr told the Wall Street Journal. “We don’t have tastemakers deciding what our customers should read, listen to, and watch.”

book publishers have been consolidating for several decades, under the ownership of media conglomerates like News Corporation, which squeeze them for profits, or holding companies such as Rivergroup, which strip them to service debt. The effect of all this corporatization, as with the replacement of independent booksellers by superstores, has been to privilege the blockbuster.

The combination of ceaseless innovation and low-wage drudgery makes Amazon the epitome of a successful New Economy company. It’s hiring as fast as it can—nearly thirty thousand employees last year.

the long-term outlook is discouraging. This is partly because Americans don’t read as many books as they used to—they are too busy doing other things with their devices—but also because of the relentless downward pressure on prices that Amazon enforces.

he digital market is awash with millions of barely edited titles, most of it dreck, while r

Amazon believes that its approach encourages ever more people to tell their stories to ever more people, and turns writers into entrepreneurs; the price per unit might be cheap, but the higher number of units sold, and the accompanying royalties, will make authors wealthier

In Friedman’s view, selling digital books at low prices will democratize reading: “What do you want as an author—to sell books to as few people as possible for as much as possible, or for as little as possible to as many readers as possible?”

The real talent, the people who are writers because they happen to be really good at writing—they aren’t going to be able to afford to do it.”

Seven-figure bidding wars still break out over potential blockbusters, even though these battles often turn out to be follies. The quest for publishing profits in an economy of scarcity drives the money toward a few big books. So does the gradual disappearance of book reviewers and knowledgeable booksellers, whose enthusiasm might have rescued a book from drowning in obscurity. When consumers are overwhelmed with choices, some experts argue, they all tend to buy the same well-known thing.

These trends point toward what the literary agent called “the rich getting richer, the poor getting poorer.” A few brand names at the top, a mass of unwashed titles down below, the middle hollowed out: the book business in the age of Amazon mirrors the widening inequality of the broader economy.

“If they did, in my opinion they would save the industry. They’d lose thirty per cent of their sales, but they would have an additional thirty per cent for every copy they sold, because they’d be selling directly to consumers. The industry thinks of itself as Procter & Gamble*. What gave publishers the idea that this was some big goddam business? It’s not—it’s a tiny little business, selling to a bunch of odd people who read.”

Bezos is right: gatekeepers are inherently élitist, and some of them have been weakened, in no small part, because of their complacency and short-term thinking. But gatekeepers are also barriers against the complete commercialization of ideas, allowing new talent the time to develop and learn to tell difficult truths. When the last gatekeeper but one is gone, will Amazon care whether a book is any good? ♦

DARPA hopes to do that with an implanted device that will take over some functions of the brain's hippocampus, an area that's important to memory. The agency has already used a device that does this in rodents, Ling said, and the goal is to move on to people quickly.

We believe that the tools and technologies that will come from this initiative will actually enable all brain scientists to do their work better, faster and with more impact,"

For several years now, people with Parkinson's have been able to reduce their tremors with a treatment known as deep brain stimulation.

But Dr. Tom Insel, director of the National Institute of Mental Health, said it's important to remember that the immediate goal of the BRAIN Initiative isn't developing treatments, but understanding the inner workings of the most complex system in the universe.

"They're interested in the brain as a way to understand who we are, what makes us different and what is special about the human brain."

Medieval science was guided by "logical consistency."

The logical empiricist's conception of scientific progress was thus a continuous one; more comprehensive theory replaced compatible, older theory

Hempel also believed that science evolved in a continuous manner. New theory did not contradict past theory: "theory does not simply refute the earlier empirical generalizations in its field; rather, it shows that within a certain limited range defined by qualifying conditions, the generalizations hold true in fairly close approximation." [21]

movement combined induction, based on empiricism, and deduction in the form of logic

e notion of scientific realism held by Newton led to the evolutionary view of the progress of science

he entities and processes of theory were believed to exist in nature, and science should discover those entities and processes

Particularly disturbing discoveries were made in the area of atomic physics. For instance, Heisenberg's indeterminacy (25)principle, according to historian of science Cecil Schneer, yielded the conclusion that "the world of nature is indeterminate.

"even the fundamental principle of causality fail[ed] ."

was not until the second half of the twentieth century that the preservers of the evolutionary idea of scientific progress, the logical empiricists, were seriously challenged

revolutionary model of scientific change and examined the role of the scientific community in preventing and then accepting change. Kuhn's conception of scientific change occurring through revolutions undermined the traditional scientific goal, finding "truth" in nature

Textbooks inform scientists-to-be about this common body of knowledge and understanding.

for the world is too huge and complex to be explored randomly.

a scientist knows what facts are relevant and can build on past research

Normal science, as defined by Kuhn, is cumulative. New knowledge fills a gap of ignorance

ne standard product of the scientific enterprise is missing. Normal science does not aim at novelties of fact or theory and, when successful, finds none."

ntain a mechanism that uncovers anomaly, inconsistencies within the paradigm.

eventually, details arise that are inconsistent with the current paradigm

hese inconsistencies are eventually resolved or are ignored.

y concern a topic of central importance, a crisis occurs and normal science comes to a hal

that the scientists re-examine the foundations of their science that they had been taking for granted

it resolves the crisis better than the others, it offers promise for future research, and it is more aesthetic than its competitors. The reasons for converting to a new paradigm are never completely rational.

Unlike evolutionary science, in which new knowledge fills a gap of ignorance, in Kuhn's model new knowledge replaces incompatible knowledge.

In a sense, we have circled back to the ancient and medieval practice of separating scientific theory from physical reality; both medieval scientists and Kuhn would agree that no theory corresponds to reality and therefore any number of theories might equally well explain a natural phenomenon. [36] Neither twentieth-century atomic theorists nor medieval astronomers are able to claim that their theories accurately describe physical phenomena. The inability to return to scientific realism suggests a tripartite division of the history of science, with a period of scientific realism fitting between two periods in which there is no insistence that theory correspond to reality. Although both scientific realism and the evolutionary idea of scientific progress appeal to common sense, both existed for only a few hundred years.

let him develop the habit of smiling any time he starts feeling rotten. Believe it or not, it works.

This tendency to see meaning in life events seems to reflect a more general aspect of human nature: our powerful drive to reason in psychological terms, to make sense of events and situations by appealing to goals, desires and intentions

This drive serves us well when we think about the actions of other people, who actually possess these psychological states, because it helps us figure out why people behave as they do and to respond appropriately.

But it can lead us into error when we overextend it, causing us to infer psychological states even when none exist. This fosters the illusion that the world itself is full of purpose and design.

we found that highly paranoid people (who tend to obsess over other people’s hidden motives and intentions) and highly empathetic people (who think deeply about other people’s goals and emotions) are particularly likely to believe in fate and to believe that there are hidden messages and signs embedded in their own life events. In other words, the more likely people are to think about other people’s purposes and intentions, the more likely they are to also infer purpose and intention in human life itself.

the belief also has some ugly consequences. It tilts us toward the view that the world is a fundamentally fair place, where goodness is rewarded and badness punished. It can lead us to blame those who suffer from disease and who are victims of crimes, and it can motivate a reflexive bias in favor of the status quo — seeing poverty, inequality and oppression as reflecting the workings of a deep and meaningful plan.

even those who are devout should agree that, at least here on Earth, things just don’t naturally work out so that people get what they deserve. If there is such a thing as divine justice or karmic retribution, the world we live in is not the place to find it. Instead, the events of human life unfold in a fair and just manner only when individuals and society work hard to make this happen.We should resist our natural urge to think otherwise.