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Accelerationists favour automation. They favour the further merging of the digital and the human. They often favour the deregulation of business, and drastically scaled-back government. They believe that people should stop deluding themselves that economic and technological progress can be controlled.

Accelerationism, therefore, goes against conservatism, traditional socialism, social democracy, environmentalism, protectionism, populism, nationalism, localism and all the other ideologies that have sought to moderate or reverse the already hugely disruptive, seemingly runaway pace of change in the modern world

Robin Mackay and Armen Avanessian in their introduction to #Accelerate: The Accelerationist Reader, a sometimes baffling, sometimes exhilarating book, published in 2014, which remains the only proper guide to the movement in existence.

“We all live in an operating system set up by the accelerating triad of war, capitalism and emergent AI,” says Steve Goodman, a British accelerationist

A century ago, the writers and artists of the Italian futurist movement fell in love with the machines of the industrial era and their apparent ability to invigorate society. Many futurists followed this fascination into war-mongering and fascism.

One of the central figures of accelerationism is the British philosopher Nick Land, who taught at Warwick University in the 1990s

Land has published prolifically on the internet, not always under his own name, about the supposed obsolescence of western democracy; he has also written approvingly about “human biodiversity” and “capitalistic human sorting” – the pseudoscientific idea, currently popular on the far right, that different races “naturally” fare differently in the modern world; and about the supposedly inevitable “disintegration of the human species” when artificial intelligence improves sufficiently.

In our politically febrile times, the impatient, intemperate, possibly revolutionary ideas of accelerationism feel relevant, or at least intriguing, as never before. Noys says: “Accelerationists always seem to have an answer. If capitalism is going fast, they say it needs to go faster. If capitalism hits a bump in the road, and slows down” – as it has since the 2008 financial crisis – “they say it needs to be kickstarted.”

On alt-right blogs, Land in particular has become a name to conjure with. Commenters have excitedly noted the connections between some of his ideas and the thinking of both the libertarian Silicon Valley billionaire Peter Thiel and Trump’s iconoclastic strategist Steve Bannon.

“In Silicon Valley,” says Fred Turner, a leading historian of America’s digital industries, “accelerationism is part of a whole movement which is saying, we don’t need [conventional] politics any more, we can get rid of ‘left’ and ‘right’, if we just get technology right. Accelerationism also fits with how electronic devices are marketed – the promise that, finally, they will help us leave the material world, all the mess of the physical, far behind.”

In 1972, the philosopher Gilles Deleuze and the psychoanalyst Félix Guattari published Anti-Oedipus. It was a restless, sprawling, appealingly ambiguous book, which suggested that, rather than simply oppose capitalism, the left should acknowledge its ability to liberate as well as oppress people, and should seek to strengthen these anarchic tendencies, “to go still further … in the movement of the market … to ‘accelerate the process’”.

By the early 90s Land had distilled his reading, which included Deleuze and Guattari and Lyotard, into a set of ideas and a writing style that, to his students at least, were visionary and thrillingly dangerous. Land wrote in 1992 that capitalism had never been properly unleashed, but instead had always been held back by politics, “the last great sentimental indulgence of mankind”. He dismissed Europe as a sclerotic, increasingly marginal place, “the racial trash-can of Asia”. And he saw civilisation everywhere accelerating towards an apocalypse: “Disorder must increase... Any [human] organisation is ... a mere ... detour in the inexorable death-flow.”

With the internet becoming part of everyday life for the first time, and capitalism seemingly triumphant after the collapse of communism in 1989, a belief that the future would be almost entirely shaped by computers and globalisation – the accelerated “movement of the market” that Deleuze and Guattari had called for two decades earlier – spread across British and American academia and politics during the 90s. The Warwick accelerationists were in the vanguard.

In the US, confident, rainbow-coloured magazines such as Wired promoted what became known as “the Californian ideology”: the optimistic claim that human potential would be unlocked everywhere by digital technology. In Britain, this optimism influenced New Labour

The Warwick accelerationists saw themselves as participants, not traditional academic observers

The CCRU gang formed reading groups and set up conferences and journals. They squeezed into the narrow CCRU room in the philosophy department and gave each other impromptu seminars.

The main result of the CCRU’s frantic, promiscuous research was a conveyor belt of cryptic articles, crammed with invented terms, sometimes speculative to the point of being fiction.

At Warwick, however, the prophecies were darker. “One of our motives,” says Plant, “was precisely to undermine the cheery utopianism of the 90s, much of which seemed very conservative” – an old-fashioned male desire for salvation through gadgets, in her view.

K-punk was written by Mark Fisher, formerly of the CCRU. The blog retained some Warwick traits, such as quoting reverently from Deleuze and Guattari, but it gradually shed the CCRU’s aggressive rhetoric and pro-capitalist politics for a more forgiving, more left-leaning take on modernity. Fisher increasingly felt that capitalism was a disappointment to accelerationists, with its cautious, entrenched corporations and endless cycles of essentially the same products. But he was also impatient with the left, which he thought was ignoring new technology

lex Williams, co-wrote a Manifesto for an Accelerationist Politics. “Capitalism has begun to constrain the productive forces of technology,” they wrote. “[Our version of] accelerationism is the basic belief that these capacities can and should be let loose … repurposed towards common ends … towards an alternative modernity.”

What that “alternative modernity” might be was barely, but seductively, sketched out, with fleeting references to reduced working hours, to technology being used to reduce social conflict rather than exacerbate it, and to humanity moving “beyond the limitations of the earth and our own immediate bodily forms”. On politics and philosophy blogs from Britain to the US and Italy, the notion spread that Srnicek and Williams had founded a new political philosophy: “left accelerationism”.

Two years later, in 2015, they expanded the manifesto into a slightly more concrete book, Inventing the Future. It argued for an economy based as far as possible on automation, with the jobs, working hours and wages lost replaced by a universal basic income. The book attracted more attention than a speculative leftwing work had for years, with interest and praise from intellectually curious leftists

Even the thinking of the arch-accelerationist Nick Land, who is 55 now, may be slowing down. Since 2013, he has become a guru for the US-based far-right movement neoreaction, or NRx as it often calls itself. Neoreactionaries believe in the replacement of modern nation-states, democracy and government bureaucracies by authoritarian city states, which on neoreaction blogs sound as much like idealised medieval kingdoms as they do modern enclaves such as Singapore.

Land argues now that neoreaction, like Trump and Brexit, is something that accelerationists should support, in order to hasten the end of the status quo.

In 1970, the American writer Alvin Toffler, an exponent of accelerationism’s more playful intellectual cousin, futurology, published Future Shock, a book about the possibilities and dangers of new technology. Toffler predicted the imminent arrival of artificial intelligence, cryonics, cloning and robots working behind airline check-in desks

Land left Britain. He moved to Taiwan “early in the new millennium”, he told me, then to Shanghai “a couple of years later”. He still lives there now.

In a 2004 article for the Shanghai Star, an English-language paper, he described the modern Chinese fusion of Marxism and capitalism as “the greatest political engine of social and economic development the world has ever known”

Once he lived there, Land told me, he realised that “to a massive degree” China was already an accelerationist society: fixated by the future and changing at speed. Presented with the sweeping projects of the Chinese state, his previous, libertarian contempt for the capabilities of governments fell away

Without a dynamic capitalism to feed off, as Deleuze and Guattari had in the early 70s, and the Warwick philosophers had in the 90s, it may be that accelerationism just races up blind alleys. In his 2014 book about the movement, Malign Velocities, Benjamin Noys accuses it of offering “false” solutions to current technological and economic dilemmas. With accelerationism, he writes, a breakthrough to a better future is “always promised and always just out of reach”.

“The pace of change accelerates,” concluded a documentary version of the book, with a slightly hammy voiceover by Orson Welles. “We are living through one of the greatest revolutions in history – the birth of a new civilisation.”

Shortly afterwards, the 1973 oil crisis struck. World capitalism did not accelerate again for almost a decade. For much of the “new civilisation” Toffler promised, we are still waiting

Code is too hard to think about. Before trying to understand the attempts themselves, then, it’s worth understanding why this might be: what it is about code that makes it so foreign to the mind, and so unlike anything that came before it.

Technological progress used to change the way the world looked—you could watch the roads getting paved; you could see the skylines rise. Today you can hardly tell when something is remade, because so often it is remade by code.

Software has enabled us to make the most intricate machines that have ever existed. And yet we have hardly noticed, because all of that complexity is packed into tiny silicon chips as millions and millions of lines of cod

The programmer, the renowned Dutch computer scientist Edsger Dijkstra wrote in 1988, “has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before.” Dijkstra meant this as a warning.

As programmers eagerly poured software into critical systems, they became, more and more, the linchpins of the built world—and Dijkstra thought they had perhaps overestimated themselves.

What made programming so difficult was that it required you to think like a computer.

The introduction of programming languages like Fortran and C, which resemble English, and tools, known as “integrated development environments,” or IDEs, that help correct simple mistakes (like Microsoft Word’s grammar checker but for code), obscured, though did little to actually change, this basic alienation—the fact that the programmer didn’t work on a problem directly, but rather spent their days writing out instructions for a machine.

Though he runs a lab that studies the future of computing, he seems less interested in technology per se than in the minds of the people who use it. Like any good toolmaker, he has a way of looking at the world that is equal parts technical and humane. He graduated top of his class at the California Institute of Technology for electrical engineering,

“The serious problems that have happened with software have to do with requirements, not coding errors.” When you’re writing code that controls a car’s throttle, for instance, what’s important is the rules about when and how and by how much to open it. But these systems have become so complicated that hardly anyone can keep them straight in their head. “There’s 100 million lines of code in cars now,” Leveson says. “You just cannot anticipate all these things.”

a nearly decade-long investigation into claims of so-called unintended acceleration in Toyota cars. Toyota blamed the incidents on poorly designed floor mats, “sticky” pedals, and driver error, but outsiders suspected that faulty software might be responsible

software experts spend 18 months with the Toyota code, picking up where NASA left off. Barr described what they found as “spaghetti code,” programmer lingo for software that has become a tangled mess. Code turns to spaghetti when it accretes over many years, with feature after feature piling on top of, and being woven around

Using the same model as the Camry involved in the accident, Barr’s team demonstrated that there were actually more than 10 million ways for the onboard computer to cause unintended acceleration. They showed that as little as a single bit flip—a one in the computer’s memory becoming a zero or vice versa—could make a car run out of control. The fail-safe code that Toyota had put in place wasn’t enough to stop it

. In all, Toyota recalled more than 9 million cars, and paid nearly $3 billion in settlements and fines related to unintended acceleration.

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little.

This is the trouble with making things out of code, as opposed to something physical. “The complexity,” as Leveson puts it, “is invisible to the eye.”

The fact that the two of them were thinking about the same problem in the same terms, at the same time, was not a coincidence. They had both just seen the same remarkable talk, given to a group of software-engineering students in a Montreal hotel by a computer researcher named Bret Victor. The talk, which went viral when it was posted online in February 2012, seemed to be making two bold claims. The first was that the way we make software is fundamentally broken. The second was that Victor knew how to fix it.

“Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.

in early 2012, Victor had finally landed upon the principle that seemed to thread through all of his work. (He actually called the talk “Inventing on Principle.”) The principle was this: “Creators need an immediate connection to what they’re creating.” The problem with programming was that it violated the principle. That’s why software systems were so hard to think about, and so rife with bugs: The programmer, staring at a page of text, was abstracted from whatever it was they were actually making.

“Our current conception of what a computer program is,” he said, is “derived straight from Fortran and ALGOL in the late ’50s. Those languages were designed for punch cards.”

WYSIWYG (pronounced “wizzywig”) came along. It stood for “What You See Is What You Get.”

Victor’s point was that programming itself should be like that. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.

With the right interface, it was almost as if you weren’t working with code at all; you were manipulating the game’s behavior directly.

When the audience first saw this in action, they literally gasped. They knew they weren’t looking at a kid’s game, but rather the future of their industry. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands. One programmer who saw the talk wrote later: “Suddenly all of my tools feel obsolete.”

hen John Resig saw the “Inventing on Principle” talk, he scrapped his plans for the Khan Academy programming curriculum. He wanted the site’s programming exercises to work just like Victor’s demos. On the left-hand side you’d have the code, and on the right, the running program: a picture or game or simulation. If you changed the code, it’d instantly change the picture. “In an environment that is truly responsive,” Resig wrote about the approach, “you can completely change the model of how a student learns ... [They] can now immediately see the result and intuit how underlying systems inherently work without ever following an explicit explanation.” Khan Academy has become perhaps the largest computer-programming class in the world, with a million students, on average, actively using the program each month.

“Everyone thought I was interested in programming environments,” he said. Really he was interested in how people see and understand systems—as he puts it, in the “visual representation of dynamic behavior.” Although code had increasingly become the tool of choice for creating dynamic behavior, it remained one of the worst tools for understanding it. The point of “Inventing on Principle” was to show that you could mitigate that problem by making the connection between a system’s behavior and its code immediate.

“Typically the main problem with software coding—and I’m a coder myself,” Bantegnie says, “is not the skills of the coders. The people know how to code. The problem is what to code. Because most of the requirements are kind of natural language, ambiguous, and a requirement is never extremely precise, it’s often understood differently by the guy who’s supposed to code.”

In a pair of later talks, “Stop Drawing Dead Fish” and “Drawing Dynamic Visualizations,” Victor went one further. He demoed two programs he’d built—the first for animators, the second for scientists trying to visualize their data—each of which took a process that used to involve writing lots of custom code and reduced it to playing around in a WYSIWYG interface.

Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. “I’m not sure that programming has to exist at all,” he told me. “Or at least software developers.” In his mind, a software developer’s proper role was to create tools that removed the need for software developers. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.

Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. “The inconveniences of daily life are not the significant problems,” he wrote. Instead, they should focus on scientists and engineers—as he put it to me, “these people that are doing work that actually matters, and critically matters, and using really, really bad tools.”

Bantegnie’s company is one of the pioneers in the industrial use of model-based design, in which you no longer write code directly. Instead, you create a kind of flowchart that describes the rules your program should follow (the “model”), and the computer generates code for you based on those rules

In a model-based design tool, you’d represent this rule with a small diagram, as though drawing the logic out on a whiteboard, made of boxes that represent different states—like “door open,” “moving,” and “door closed”—and lines that define how you can get from one state to the other. The diagrams make the system’s rules obvious: Just by looking, you can see that the only way to get the elevator moving is to close the door, or that the only way to get the door open is to stop.

. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules. So that’s what you spend your time thinking about. It’s a way of focusing less on the machine and more on the problem you’re trying to get it to solve.

The ideas spread. The notion of liveness, of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table.

On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself.

for this approach to succeed, much of the work has to be done well before the project even begins. Someone first has to build a tool for developing models that are natural for people—that feel just like the notes and drawings they’d make on their own—while still being unambiguous enough for a computer to understand. They have to make a program that turns these models into real code. And finally they have to prove that the generated code will always do what it’s supposed to.

tice brings order and accountability to large codebases. But, Shivappa says, “it’s a very labor-intensive process.” He estimates that before they used model-based design, on a two-year-long project only two to three months was spent writing code—the rest was spent working on the documentation.

uch of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. You’re free to tweak your blueprint without fear of introducing new bugs. Your code is, in FAA parlance, “correct by construction.”

“people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.”

The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.”

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper. “That human fallibility means that some of the more subtle, dangerous bugs turn out to be errors in design; the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.”

Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect. A single subtle bug could be catastrophic. But he knew how hard bugs were to find, especially as an algorithm grew more complex. You could do all the testing you wanted and you’d never find them all.

An algorithm written in TLA+ could in principle be proven correct. In practice, it allowed you to create a realistic model of your problem and test it not just thoroughly, but exhaustively. This was exactly what he’d been looking for: a language for writing perfect algorithms.

TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy

Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,”

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.

But TLA+ occupies just a small, far corner of the mainstream, if it can be said to take up any space there at all. Even to a seasoned engineer like Newcombe, the language read at first as bizarre and esoteric—a zoo of symbols.

this is a failure of education. Though programming was born in mathematics, it has since largely been divorced from it. Most programmers aren’t very fluent in the kind of math—logic and set theory, mostly—that you need to work with TLA+. “Very few programmers—and including very few teachers of programming—understand the very basic concepts and how they’re applied in practice. And they seem to think that all they need is code,” Lamport says. “The idea that there’s some higher level than the code in which you need to be able to think precisely, and that mathematics actually allows you to think precisely about it, is just completely foreign. Because they never learned it.”

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus. And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.”

Programmers, as a species, are relentlessly pragmatic. Tools like TLA+ reek of the ivory tower. When programmers encounter “formal methods” (so called because they involve mathematical, “formally” precise descriptions of programs), their deep-seated instinct is to recoil.

Formal methods had an image problem. And the way to fix it wasn’t to implore programmers to change—it was to change yourself. Newcombe realized that to bring tools like TLA+ to the programming mainstream, you had to start speaking their language.

he presented TLA+ as a new kind of “pseudocode,” a stepping-stone to real code that allowed you to exhaustively test your algorithms—and that got you thinking precisely early on in the design process. “Engineers think in terms of debugging rather than ‘verification,’” he wrote, so he titled his internal talk on the subject to fellow Amazon engineers “Debugging Designs.” Rather than bemoan the fact that programmers see the world in code, Newcombe embraced it. He knew he’d lose them otherwise. “I’ve had a bunch of people say, ‘Now I get it,’” Newcombe says.

In the world of the self-driving car, software can’t be an afterthought. It can’t be built like today’s airline-reservation systems or 911 systems or stock-trading systems. Code will be put in charge of hundreds of millions of lives on the road and it has to work. That is no small task.

The law, for instance, establishes a new $20 billion Directorate for Technology, which will specialize in pushing new technologies from the prototype stage into the mass market. It is meant to prevent what happened with the solar industry—where America invented a new technology, only to lose out on commercializing it—from happening again

Within a few years, when the funding has fully ramped up, the government will spend roughly $80 billion a year on accelerating the development and deployment of zero-carbon energy and preparing for the impacts of climate change. That exceeds the GDP of about 120 of the 192 countries that have signed the Paris Agreement on Climate Change

By the end of the decade, the federal government will have spent more than $521 billion

the bill’s programs focus on the bleeding edge of the decarbonization problem, investing money in technology that should lower emissions in the 2030s and beyond.

The International Energy Association has estimated that almost half of global emissions reductions by 2050 will come from technologies that exist only as prototypes or demonstration projects today.

To get those technologies ready in time, we need to deploy those new ideas as fast as we can, then rapidly get them to commercial scale, Carey said. “What used to take two decades now needs to take six to 10 years.” That’s what the CHIPS Act is supposed to do

When viewed with the Inflation Reduction Act, which the House is poised to pass later this week, and last year’s bipartisan infrastructure law, a major shift in congressional climate spending comes into focus. According to the RMI analysis, these three laws are set to more than triple the federal government’s average annual spending on climate and clean energy this decade, compared with the 2010s.

Congress has explicitly tasked the new office with studying “natural and anthropogenic disaster prevention or mitigation” as well as “advanced energy and industrial efficiency technologies,” including next-generation nuclear reactors.

The bill also directs about $12 billion in new research, development, and demonstration funding to the Department of Energy, according to RMI’s estimate. That includes doubling the budget for ARPA-E, the department’s advanced-energy-projects skunk works.

it allocates billions to upgrade facilities at the government’s in-house defense and energy research institutes, including the National Renewable Energy Laboratory, the Princeton Plasma Physics Laboratory, and Berkeley Lab, which conducts environmental-science research.

RMI’s estimate of the climate spending in the CHIPS bill should be understood as just that: an estimate. The bill text rarely specifies how much of its new funding should go to climate issues.

When you add CHIPS, the IRA, and the infrastructure law together, Washington appears to be unifying behind a new industrial policy, focused not only on semiconductors and defense technology but clean energy

The three bills combine to form a “a coordinated, strategic policy for accelerating the transition to the technologies that are going to define the 21st century,”

scholars and experts have speculated about whether industrial policy—the intentional use of law to nurture and grow certain industries—might make a comeback to help fight climate change. Industrial policy was central to some of the Green New Deal’s original pitch, and it has helped China develop a commanding lead in the global solar industry.

“Industrial policy,” he said, “is back.”

There's very little scientific evidence to suggest our perception of time changes as we age. And yet, we consistently report that the past felt longer — that time is flying by faster as we age. What's going on?

Scientists can look at time estimation, or our ability to estimate how long a minute passes, compared with a clock. (This is what Sothern is doing.) They can also look at time awareness, or the broad feeling that time is moving quickly or slowly. Finally there's time perspective, the sense of a past, present, and future as constructed by our memories.

What researchers have found out is that while time estimation and time awareness don't change much as we age, time perspective does. In other words: Our memories create the illusion time is accelerating.

There weren't many differences between the old and the young. "[C]hronological age showed no systematic influence on the perception of these brief intervals of time up," the authors wrote. (That said, the researchers did find that males overestimate time while females underestimate it, perhaps due to having slightly different circadian clocks and therefore slightly different metabolic rates

Here, too, age seemed not to matter. Older people didn't seem to be aware of time passing any faster than younger people. The only question that yielded a statistically significant difference was, "How fast did the last decade pass?" Even there, the reported differences were tiny, and the effect appeared to plateau around age 50.

psychologists William Friedman and Steve Janssen found scant evidence that the subjective experience of time speeds up with age. They write in their 2009 paper, "We can concluded that when adults report on their general impressions of the speed of time, age differences are very small."

One possibility is that participants were simply biased by the (incorrect) conventional wisdom — they reported their later years as flying by more quickly because that's what everyday lore says should happen.

When people reflect back on their own life, they feel like their early years went by very slowly and their later years go by more quickly. This could be the source of the belief that time goes more quickly as they age.

 "Most people feel that time is currently passing faster for them than it did in the past," Janssen writes me in an email. "They have forgotten how they experienced the passage of time when they were younger."

We use significant events as signposts to gauge the passage of time. The fewer events, the faster time seems to go by.

Childhood is full of big, memorable moments like learning to ride a bike or making first friends. By contrast, adult life becomes ordinary and mechanized, and ambles along by.

Each passing year converts some of this experience into automatic routine which we hardly notice at all, the days and weeks smooth themselves out in recollection, and the years grow hollow and collapse.

Each new minute represents a smaller fraction of our lives. One day as a 10 year old represents about .027 percent of the kid's life. A day for a 60 year old? .0045 percent. The kid's life is just... bigger.

Also, our ability to recall events declines with age. If we can't remember a time, it didn't happen.

"[F]inding that there is insufficient time to get things done may be reinterpreted as the feeling that time is passing quickly," they write. Deadlines always come sooner than we'd like.

Psychologists have long understood the phenomenon called "forward telescoping" — i.e., our tendency to underestimate how long ago very memorable events occurred. "Because we know that memories fade over time, we use the clarity of a memory as a guide to its recency," science writer Claudia Hammond writes in her book Time Warped. "So if a memory seems unclear we assumed it happened longer ago." But very clear memories are assumed to be more recent.

If our memories can trick us into thinking time is moving quickly, then maybe there are ways to trick our brains into thinking that time is slowing down — such as committing to breaking routines and learning new things. You're more likely to remember learning how to skydive than watching another hour of mindless television.

ct, their lifetime earnings are reduced by one year.

The benefits of being younger are even greater for those who skip a grade, an option available to many high-achieving children. Compared with nonskippers of similar talent and motivation, these youngsters pursue advanced degrees and enter professional school more often. Acceleration is a powerful intervention, with effects on achievement that are twice as large as programs for the gifted.

Parents who want to give their young children an academic advantage have a powerful tool: school itself. In a large-scale study at 26 Canadian elementary schools, first graders who were young for their year made considerably more progress in reading and math than kindergartners who were old for their year

The question we should ask instead is: What approach gives children the greatest opportunity to learn?

school makes children smarter.

These differences may come from the increased challenges of a demanding environment. Learning is maximized not by getting all the answers right, but by making errors and correcting them quickly.

Some children, especially boys, are slow to mature emotionally, a process that may be aided by the presence of older children.

The benefits of interacting with older children may extend to empathetic abilities. Empathy requires the ability to reason about the beliefs of others. This capacity relies on brain maturation, but it is also influenced by interactions with other children. Having an older (but not younger) sibling speeds the onset of this capacity in 3- to 5-year-olds. The acceleration is large: up to half a year per sibling.

children are not on a fixed trajectory but learn actively from teachers — and classmates. It matters very much who a child’s peers are. Redshirted children begin school with others who are a little further behind them. Because learning is social, the real winners in that situation are their classmates.

“We may be sitting on a precipice of a major extinction event,”

There are clear signs already that humans are harming the oceans to a remarkable degree, the scientists found. Some ocean species are certainly overharvested, but even greater damage results from large-scale habitat loss, which is likely to accelerate as technology advances the human footprint, the scientists reported.

Coral reefs, for example, have declined by 40 percent worldwide,

Fragile ecosystems like mangroves are being replaced by fish farms, which are projected to provide most of the fish we consume within 20 years.

Mining operations, too, are poised to transform the ocean. Contracts for seabed mining now cover 460,000 square miles underwater, the researchers found, up from zero in 2000.

ecosystems may seem impervious to change.

The fossil record indicates that a number of large animal species became extinct as humans arrived on continents and islands.

But it was only after 1800, with the Industrial Revolution, that extinctions on land really accelerated.

Humans began to alter the habitat that wildlife depended on, wiping out forests for timber, plowing under prairie for farmland, and laying down roads and railroads across continents.

Over the past five centuries, researchers have recorded 514 animal extinctions on land

There’s little predictability in how things will play out after this anthropogenic jolt, especially in the living world. [More on the “great acceleration” behind the jolt is here.]

Once you begin to get the many feedbacks bouncing off each other and bouncing off the Earth system, it’s going to be very hard to follow what’s going to happen, particularly biologically…. One could not imagine, at the very end of the Cretaceous, the beginning of the Tertiary, that the mammals — these itty-bitty little squeaky furry things, would take over – effectively taking the position that the dinosaurs held for so long. All we can say is that, for sure, it will be different. We’re going down a different trouser leg of history.

on whether it’s possible to have a “good Anthropocene.” Kolbert’s Twitter post last spring nicely captured their view:

Taking full ownership of the Anthropocene won’t be easy. The necessary feeling is a queasy mix of excitement and unease. I’ve compared it to waking up in the first car on the first run of a new roller coaster that hasn’t been examined fully by engineers.

That’s a very different sensation than, say, mourning the end of nature. It’s more a celebration, in a way — a deeper acceptance of our place on the planet, with all of our synthetic trappings, and our faults, as fundamentally natural.

in the broadest sense we have to embrace the characteristics, good and bad, that make humans such a rare thing — a species that has become a planet-scale force.

“The way I would like to see it is in, say, 100 years in the future the London Geological Society will look back and consider this period…a transition from the lesser Anthropocene to the greater Anthropocene.”

Fully integrating this awareness into our personal choices and societal norms and policies will take time. It is “the great work,” as Thomas Berry put it.

Technology alone will not do the trick. Another keystone to better meshing humanity’s infinite aspirations with life on a finite planet will be slowly shifting value systems from the foundation up

Edward O. Wilson’s “Biophilia” was a powerful look outward at the characteristics of the natural world that we inherently cherish.

Now we need a dose of what I’ve taken to calling anthropophilia, as well.

We have to accept ourselves, flaws and all, in order to move beyond what has been something of an unconscious, species-scale pubescent growth spurt, enabled by fossil fuels in place of testosterone.

We’re stuck with “The World With Us.” It’s time to grasp that uncomfortable, but ultimately hopeful, idea.

Raising marine fish is a different proposition. The harsh ocean environment makes production risky, and the biology of these species makes many of them difficult and costly to breed and grow.

Improvements in technology have reduced, though not eliminated, the amount of fish used in feeds, especially for farmed salmon. It now takes half as much fresh fish to raise salmon as it did 20 years ago.

Marine fish farming is currently done in sheltered bays and sea lochs. But there is growing interest in a new high-tech method that raises fish in huge submersible cages anchored far from land in the open ocean. It’s risky business, with high operating costs. Expensive infrastructure is vulnerable to intense storms.

The quality of mathematics research in the United States today is the envy of the scientific world. This is a direct result of the openness and inclusivity of the profession.

Can Americans maintain this unmatched excellence in the future? There are worrisome signs that suggest not.

The Organization for Economic Cooperation and Development compares mathematical proficiency among 15-year-olds by country worldwide. According to its 2018 report, America ranked 37th while China, America’s main competitor for world leadership, came in first.

This is despite the fact that the United States is the fifth-highest spender per pupil among the 37 developed OECD nations

This massive failure of our K-12 education system trickles through the STEM pipeline.

At the undergraduate level, too few American students are prepared for higher-level mathematics courses. These students are then unprepared for rigorous graduate-level work

According to our own experiences at the universities where we teach, an overwhelming majority of American students with strong math backgrounds are either foreign-born or first-generation students who have additional support from their education-conscious families. At all levels, STEM disciplines are more and more dependent on a constant flow of foreign talent.

There are many reasons for this failure, but the way that we educate and prepare teachers is particularly influential. The vast majority of K-12 math teachers are graduates of teacher-preparation programs that teach very little substantive mathematics

This has led to a constant stream of ill-advised and dumbed-down reforms. One of the latest fads is anti-racist mathematics. Promoted in several states, the bizarre doctrine threatens to further degrade the teaching of mathematics.

Another major concern is the twisted interpretation of diversity, equity, and inclusion (DEI).

Under the banner of DEI, universities are abandoning the use of standardized tests like the SAT and GRE in admissions, and cities are considering scrapping academic tracking and various gifted programs in schools, which they deem “inequitable.”

such programs are particularly effective, when properly implemented, at discovering and encouraging talented children from disadvantaged backgrounds.

The new 2021 Mathematics Framework, currently under consideration by California’s Department of Education, does away “with all tracking, acceleration, gifted programs, or any instruction that involves clustering by individual differences, without expressing any awareness of the impact these drastic alterations would have in preparing STEM-ready candidates.”

These measures will not only hinder the progress of the generations of our future STEM workforce but also contribute to structural inequalities, as they are uniquely detrimental to students whose parents cannot send them to private schools or effective enrichment programs.

These are just a few examples of an unprecedented fervor for revolutionary change in the name of Critical Race Theory (CRT), a doctrine that views the world as a fierce battleground for the narratives of various identity groups.

This will only lead to a further widening of racial disparities in educational outcomes while lowering American children’s rankings in education internationally.

Ill-conceived DEI policies, often informed by CRT, and the declining standards of K-12 math education feed each other in a vicious circle

Regarding minorities, in particular, public K-12 education all too often produces students unprepared to compete, thus leading to large disparities in admissions at universities, graduate programs, and faculty positions. This disparity is then condemned as a manifestation of structural racism, resulting in administrative measures to lower the evaluation criteria. Lowering standards at all levels leads eventually to even worse outcomes and larger disparities, and so on in a downward spiral.

A case in point is the recent report by the American Mathematical Society that accuses the entire mathematics community, with the thinnest of specific evidence, of systemic racial discrimination. A major justification put forward for such a grave accusation is the lack of sufficient representation of Black mathematicians in the professio

the report, while raising awareness of several ugly facts from the long-ago past, makes little effort to address the real reasons for this, mainly the catastrophic failure of the K-12 mathematical educational system.

The National Science Foundation, a federal institution meant to support fundamental research, is now diverting some of its limited funding to various DEI initiatives of questionable benefit.

Meanwhile, other countries, especially China, are doing precisely the opposite, following the model of our past dedication to objective measures of excellence. How long before we will see a reverse exodus, away from the United States?

The present crisis can still be reversed by focusing on a few concrete actions:

Improve schools in urban areas and inner-city neighborhoods by following the most promising education programs. These programs demonstrate that inner-city children benefit if they are challenged by high standards and a nurturing environment.

Follow the lead of other highly successful rigorous programs such as BASIS schools and Math for America, which focus on rigorous STEM curricula, combined with 21st-century teaching methods, and recruit talented teachers to help them build on their STEM knowledge and teaching methods.

Increase, rather than eliminate, tailored instruction, both for accelerated and remedial math courses.

Reject the soft bigotry of low expectations, that Black children cannot do well in competitive mathematics programs and need dumbed-down ethnocentric versions of mathematics.

Uphold the objective selection process based on merit at all levels of education and research.

In this frivolous new world, everything must be pleasing and inoffensive. Everything and everybody gets marketed like an exciting new product—even old, creepy politicians, or ancient film actors, or 80-year-old rock stars.

The public accepts this as a matter of course. They hardly expect anything to be real nowadays.

Let’s stay with 1996 for a moment—because it was a turning point. Before that time, Susan Sontag had been very receptive to popular culture—movies, commercial music, and campy pop art. But each of these was becoming unrecognizable in their turn-of-the-century guises.

Here’s a thumbnail sketch of the top grossing films of that year.

These films were all different—but they had one thing in common: overwhelming special effects. In the final years of the twentieth century, computer technology had reached a level where massive levels of destruction could be shown on screen with an immediacy never before possible. Technology was setting the agenda for creativity. Everything else—script, directing, acting, was subservient to the computer-generated imagery. But 1996 was just the beginning of cinema as a digital spectacle. With each passing year, the artificial digital component has increased, and the real human element decreased. The advent of AI will now accelerate this even further. We may soon reach the point where nothing on the screen is real.

Carlyle rightly mocks the citizens of France who, in those days, put so much trust in paper—whether the deceptive newspapers or the collapsing paper currency. Humans had once created a Stone Age and an Iron Age—but now settled for the Age of Paper.

By implication, we live today in a digital age—or the Age of Less-Than-Paper. It doesn’t help that that cutting edge technologies are focused so much on deception—fake images, fake video, fake audio, fake books by fake authors, fake songs by fake musicians, fake news, fake everything.

Fake is our leading candidate for word of the century. It captures almost everything relevant now in a single syllable.

So are you surprised that everything in culture has a feeling of unreality right now? It’s like cotton candy that shrinks to nothing as soon as you put it in your mouth, just leaving a brief sickly sweet taste.

Never before in history has authenticity been in such short supply. That’s so much the case, that the very word authenticity is mocked. (I will write about that more in the future.)

This is the flip side of our culture of artificiality. Anything that threatens the dominant fakeness with reality stirs up an intense backlash. The dreamer does not want to awaken from the dream.

Here’s the scariest part of the story: Most of this is by design. Our culture is now obsessed with deception and misdirection—and it’s not just on the movie screen anymore. You see it everywhere, from cosplay conventions to bands wearing masks to the misguided virtual reality mania.

A psychoanalyst would say that the hostility here is displaced—like people who kick the dog because they hate their boss.

In a cotton candy society, everything feels insubstantial:People go to war online—in toxic Twitter posts—but it’s a fake war with the angriest combatants typically hiding behind avatars.People seek love online, but even here the fakeness is toxic—hence many are catfished (a term that didn’t exist a few years ago) by scammers pretending to be a romantic interest. People not only work and play online, but even construct their selves online—which is where their identity increasingly resides.

These are all signs that we are living in a society running low on seriousness.

Ah, the word action. That’s fallen out of favor, too—another symptom worth noting. Here’s the Google analysis of its usage since the year 1900.

The decline in the word action accelerated during the same period that saw the rise (shown above) in the word fake. They are mirror images of the same cultural shift.

Participants at Normandy and Selma were taking action. But soup hurlers operate at a symbolic level, or (let’s be honest) a less-than-symbolic level—because these paintings have no connection in any way with the issues at stake.

The targeted paintings aren’t appropriate symbols of the evil they are supposed to represent. In fact, they embody the exact opposite.

It’s not mere coincidence that anger and violence are targeted at objects that are inescapablyrealtangibleuniqueauthentic (that word again!)produced by human hand

Can you build a culture on cotton candy? We will soon find out.

In an age of fakery, people who operate without seriousness will inevitably focus their hostility on precisely these cherished objects.

he cluelessness in Cupertino is understandable. The dominant companies in Silicon Valley are threatened by reality and seriousness—which are like Kryptonite to the digital agenda. So these mishaps are inevitable. Fakery is now a business model. Reality is its hated competitor.

here’s the most salient fact of all: People who have their act together are now taking things very seriously in there own lives. They aren't waiting for guidance from an app from the Apple Store or a post from an influencer.

let’s call them leaders—because that’s what they will be. And that’s such a better word than influencer.

Five years from now, the cultural landscape will look much different. I expect a lot will change in just the next 12 months.

COMING SOON: I will write about “How to Become a Serious Person.”

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.

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.

"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?

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.

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

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.

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.

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.

“And one of the things we want is to spend more time with people who think like us and less with people who are different,” Haidt added. “The Facebook effect isn’t trivial. But it’s catalyzing or amplifying a tendency that was already there.”

prevalent for many users are the posts we see from friends and from other people and groups we follow on the network, and this information is utterly contingent on choices we ourselves make

The Internet isn’t rigged to give us right or left, conservative or liberal — at least not until we rig it that way. It’s designed to give us more of the same, whatever that same is

So it goes with the fiction we read, the movies we watch, the music we listen to and, scarily, the ideas we subscribe to. They’re not challenged. They’re validated and reinforced.

this colors our days, or rather bleeds them of color, reducing them to a single hue.

Facebook, along with other social media, definitely conspires in this. Haidt noted that it often discourages dissent within a cluster of friends by accelerating shaming. He pointed to the enforced political correctness among students at many colleges.

Carnival barkers, conspiracy theories, willful bias and nasty partisanship aren’t anything new, and they haven’t reached unprecedented heights today. But what’s remarkable and sort of heartbreaking is the way they’re fed by what should be strides in our ability to educate ourselves.

The proliferation of cable television networks and growth of the Internet promised to expand our worlds, not shrink them. Instead they’ve enhanced the speed and thoroughness with which we retreat into enclaves of the like-minded.

there’s no argument that in an era that teems with choice, brims with niche marketing and exalts individualism to the extent that ours does, we’re sorting ourselves with a chillingly ruthless efficiency. We’ve surrendered universal points of reference. We’ve lost common ground.

Marc Dunkelman, adding that it also makes it easier for us to avoid “face-to-face interactions with diverse ideas.” He touched on this in an incisive 2014 book, “The Vanishing Neighbor,” which belongs with Haidt’s work and with “Bowling Alone,” “Coming Apart” and “The Fractured Republic” in the literature of modern American fragmentation, a booming genre all its own.

We’re less committed to, and trustful of, large institutions than we were at times in the past. We question their wisdom and substitute it with the groupthink of micro-communities, many of which we’ve formed online, and their sensibilities can be more peculiar and unforgiving.

We construct precisely contoured echo chambers of affirmation that turn conviction into zeal, passion into fury, disagreements with the other side into the demonization of it

It’s not about some sorcerer’s algorithm. It’s about a tribalism that has existed for as long as humankind has and is now rooted in the fertile soil of the Internet, which is coaxing it toward a full and insidious flower

It's quite possible that our love of music was simply an accident. We originally evolved emotions to help us navigate dangerous worlds (fear) and social situations (joy). And somehow, the tones and beats of musical composition activate similar brain areas.

Nature Neuroscience, led by Zatorre, researchers found that dopamine release is strongest when a piece of music reaches an emotional peak and the listener feels "chills"— the spine-tingling sensation of excitement and awe.

"Music engages the same [reward] system, even though it is not biologically necessary for survival," says Zatorre.

Presumably, we evolved to recognize patterns because it's an essential skill for survival. Does a rustling in the trees mean a dangerous animal is about to attack? Does the smell of smoke mean I should run, because a fire may be coming my way?

Music is a pattern. As we listen, we're constantly anticipating what melodies, harmonies, and rhythms may come next.

That's why we typically don't like styles of music we're not familiar with. When we're unfamiliar with a style of music, we don't have a basis to predict its patterns

We learn through our cultures what sounds constitute music. The rest is random noise.

When we hear a piece of music, its rhythm latches onto us in a process called entrainment. If the music is fast-paced, our heartbeats and breathing patterns will accelerate to match the beat.

Another hypothesis is that music latches onto the regions of the brain attuned to speech — which convey all of our emotions.

"It makes sense that our brains are really good at picking up emotions in speech," the French Institute of Science's Aucouturier says. It's essential to understand if those around us are happy, sad, angry, or scared. Much of that information is contained in the tone of a person's speech. Higher-pitched voices sound happier. More warbled voices are scared.

Music may then be an exaggerated version of speech.

And because we tend to mirror the emotions we hear in others, if the music is mimicking happy speech, then the listener will become happy too.

If he does go down that path, he will risk inviting trade retaliation by our trade partners, which could lead the global economy down the road to the beggar-thy-neighbor policies of the 1930s.

“In America, you almost need an excuse for knocking on a neighbor’s door,” Dr. Tang told me. “We want to break down those barriers.”

“You don’t need a playmate every day,” Dr. Tang said. “But knowing you’re valued and a contributing member of society is incredibly reaffirming.” Advertisement Continue reading the main story

A great paradox of our hyper-connected digital age is that we seem to be drifting apart. Increasingly, however, research confirms our deepest intuition: Human connection lies at the heart of human well-being. It’s up to all of us — doctors, patients, neighborhoods and communities — to maintain bonds where they’re fading, and create ones where they haven’t existed.

Young people, who have no place in this dead-eyed, sanitized landscape, scarcely venture from their bedrooms. Political freedom now means choosing between alternative versions of market fundamentalism.

Even the freedoms we do possess we tend not to exercise. We spend hours every day watching other people doing what we might otherwise be doing: dancing, singing, playing sports, even cooking. We venture outdoors to seek marginally different varieties of stuff we already possess

We entertain the illusion that we have chosen our lives. Why, if this is the case, do our apparent choices differ so little from those of other people? Why do we live and work and travel and eat and dress and entertain ourselves in almost identical fashion? It’s no wonder, when we possess and use it so little, that we make a fetish out of freedom.Continue reading the main story

our survival in the modern economy requires the use of few of the mental and physical capacities we possess. Sometimes it feels like a small and shuffling life. Our humdrum, humiliating lives leave us, I believe, ecologically bored.

Across many rich nations, especially the United States, global competition is causing the abandonment of farming on less fertile land. Rather than trying to tame and hold back the encroaching wilds, I believe we should help to accelerate the process of reclamation, removing redundant roads and fences, helping to re-establish missing species, such as wolves and cougars and bears, building bridges between recovering habitats to create continental-scale wildlife corridors, such as those promoted by the Rewilding Institute.

This rewilding of the land permits, if we choose, a partial rewilding of our own lives. It allows us to step into a world that is not controlled and regulated, to imagine ourselves back into the rawer life from which we came

Indeed, there is no evidence beyond revelation, authority, and scripture to support the religious claims above, and most of the world’s believers would reject at least one of them

faith involves pretending to know things you don’t

faith doesn’t mean “belief without good evidence,” but “confidence derived from scientific tests and repeated, documented experience.”

You have faith (i.e., confidence) that the sun will rise tomorrow because it always has, and there’s no evidence that the Earth has stopped rotating or the sun has burnt out.

We know no more now about the divine than we did 1,000 years ago.

The conflation of faith as “unevidenced belief” with faith as “justified confidence” is simply a word trick used to buttress religion.

The constant scrutiny of our peers ensures that science is largely self-correcting, so that we really can approach the truth about our universe

There is strong evidence for the Higgs boson, whose existence was confirmed last year by two independent teams using a giant accelerator and rigorous statistical analysis. But there isn’t, and never will be, any evidence for that sea of milk.

Two objects of scientific faith are said to be physical laws and reason. Doing science, it is said, requires unevidenced faith in the “orderliness of nature” and an “unexplained set of physical laws,” as well as in the value of reason in determining truth. Both claims are wrong.

The orderliness of nature—the set of so-called natural laws—is not an assumption but an observation

We take nature as we find it, and sometimes it behaves predictably.

Reason—the habit of being critical, logical, and of learning from experience—is not an a priori assumption but a tool that’s been shown to work

Finally, isn’t science at least based on the faith that it’s good to know the truth? Hardly.

So the next time you hear someone described as a “person of faith,” remember that although it’s meant as praise, it’s really an insult.

Facebook just opened up its Instant Articles feature to all publishers. The feature allows external outlets to publish their content directly onto Facebook’s platform, eliminating that pesky journey to their actual website. They can either place their own ads on the content or join a revenue-sharing program with Facebook. Facebook has touted this plan as one which provides a better user experience and has noted the ability for publishers to create ads on the platform as well.

The benefit to Facebook is obvious: It gets to keep people inside its house. They don’t have to leave for even a second. The publisher essentially has to accept this reality, sigh about the gradual death of websites and hope that everything works out on the financial side.

It’s all part of a much bigger story: that of how the internet, that supposed smasher of gates and leveler of playing fields, has coalesced around a mere handful of mega-giants in the space of just a couple of decades. The gates didn’t really come down. The identities of the gatekeepers just changed. Google, Facebook, Apple, Amazon

iterating arguments explored in more depth in books like Al Gore’s “The Assault on Reason,” Susan Jacoby’s “The Age of American Unreason,” Robert Hughes’s “Culture of Complaint” and, of course, Richard Hofstadter’s 1963 classic, “Anti-Intellectualism in American Life.” Nichols’s source notes are one of the highlights of the volume, pointing the reader to more illuminating books and articles.

“resistance to intellectual authority” naturally took root in a country, dedicated to the principles of liberty and egalitarianism, and how American culture tends to fuel “romantic notions about the wisdom of the common person or the gumption of the self-educated genius.”

today’s populism has magnified disdain for elites and experts of all sorts, be they in foreign policy, economics, even science.

the “protective swaddling environment of the modern university infantilizes students,”

Trump won the 2016 election, Nichols writes, because “he connected with a particular kind of voter who believes that knowing about things like America’s nuclear deterrent is just so much pointy-headed claptrap.” Worse, he goes on, some of these voters “not only didn’t care that Trump is ignorant or wrong, they likely were unable to recognize his ignorance or errors,” thanks to their own lack of knowledge.

While the internet has allowed more people more access to more information than ever before, it has also given them the illusion of knowledge when in fact they are drowning in data and cherry-picking what they choose to read

it becomes easy for one to succumb to “confirmation bias” — the tendency, as Nichols puts it, “to look for information that only confirms what we believe, to accept facts that only strengthen our preferred explanations, and to dismiss data that challenge what we accept as truth.”

When confronted with hard evidence that they are wrong, many will simply double down on their original assertions. “This is the ‘backfire effect,’” Nichols writes, “in which people redouble their efforts to keep their own internal narrative consistent, no matter how clear the indications that they’re wrong.” As a result, extreme views are amplified online, just as fake news and propaganda easily go viral.

Today, all these factors have combined to create a maelstrom of unreason that’s not just killing respect for expertise, but also undermining institutions, thwarting rational debate and spreading an epidemic of misinformation. These developments, in turn, threaten to weaken the very foundations of our democracy.

“Laypeople complain about the rule of experts and they demand greater involvement in complicated national questions, but many of them only express their anger and make these demands after abdicating their own important role in the process: namely, to stay informed and politically literate enough to choose representatives who can act on their behalf.”