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Sleep deprivation has consequences for cognitive function, namely for attention and memory, two of the most crucial functions for any medical student.

If drinking alcohol before an exam is ludicrous, shouldn't an all-nighter be too?

The Institute of Medicine estimates that 50 to 70 million U.S. adults have a sleep or wakefulness disorder

If we do not find ways to make time for sleep now, we will face greater difficulty in finding balance when the hours become more demanding in our clinical years, in residency, and beyond.

"our ability to address and solve the problems we're facing as individuals and as a society.

"If sleep does not serve an absolutely vital function, then it is the biggest mistake the evolutionary process ever made."

I have never seen this (brain) pathology in someone under the age of 30"

he had more than 10 concussions

He lost consciousness, suffered headaches, neck pain, insomnia and anxiety. He couldn't remember things or concentrate.

ventually saw a neurologist, who prescribed him a muscle relaxant and anti-seizure medication, but they did little and the symptoms continued

he stopped playing football because the symptoms were so strong

he had great opportunities, and he had to turn these opportunities down because of something that was taking over his brain

teach proper technique

Keck believed he suffered from CTE, but it cannot be diagnosed in living people.

Keck died from cardiac arrest in 2013, and it does appear his life was lost to CTE.

Usually there's a latent period. He seemed to develop it while he was playing. Even after he quit playing, it seemed to get worse."

factors such as environment and genetics may have a role.

I think its becoming more and more clear that the developing brain is more susceptible to concussion; it has a delayed recovery from concussion,"

"We really need to limit the amount of head contact that young children and adolescents are experiencing

became increasingly depressed and suicidal

We are taking out unnecessary head contact out of the sport, out of practice. We're enforcing rule changes

Exxon this was opposed by over 60% of investors.

needed more global warming not less

sell it and buy some solar stock?" the speaker from the floor asked, adding: "But whether you'll then have enough money to pay for the jet fuel that you used to come to visit here..."

It has been painted as the uncaring exploiter, sneaky oil seller that has dodged and denied on climate change to suit its pockets.

Wolves are hard to tame, even as puppies, and many researchers find it much more plausible that dogs, in effect, invented themselves.

gradually evolved to become tamer and tamer, producing lots of offspring because of the relatively easy pickings

researchers question whether dogs experience feelings like love and loyalty, or whether their winning ways are just a matter of instincts that evolved because being a hanger-on is an easier way to make a living than running down elk.

dogs and wolves interbreed easily and some scientists are not convinced that the two are even different species

generally agree that there is good evidence that dogs were domesticated around 15,000 years ago

“Maybe dog domestication on some level kicks off this whole change in the way that humans are involved and responding to and interacting with their environment,

most dog breeds were invented in the 19th century during a period of dog obsession that he called “the giant whirlwind blender of the European crazy Victorian dog-breeding frenzy.

“There’s hardly a person working in canine genetics that’s not working on that project

Almost every group has a different origination hypothesis

jaws and occasionally nearly complete skulls from old and recent dogs, wolves and canids that could fall into either category.

will be able to determine whether the domestication process occurred closer to 15,000 or 30,000 years ago,

major achievement in the world of canine science, and a landmark in the analysis of ancient DNA to show evolution, migrations and descent,

based on DNA evidence and the shape of ancient skulls, that dog domestication occurred well over 30,000 years ago.

he became fed up with the lack of ancient DNA evidence in papers about the origin of dogs.

identified a skull about 32,000 years old from a Belgian cave in Goyet as an early dog.

arguing that the evidence just wasn’t there to call the Goyet skull a dog,

claims are controversial and is willing, like the rest of the world of canine science, to risk damage to the fossils themselves to get more information on not just the mitochondrial DNA but also the nuclear DNA.

geneticists try to establish is how different the DNA of one animal is from another. Adding ancient DNA gives many more points of reference over a long time span.

will be able to identify changes in the skulls or jaws of those wolves that show shifts to more doglike shapes, helping to narrow the origins of domestication

the project will publish a flagship paper from all of the participants describing their general findings

a group in China was forming with the goal of sequencing 10,000 dog genomes

growing increasingly confident that they will find what they want, and come close to settling the thorny question of when and where the tearing power of a wolf jaw first gave way to the persuasive force of a nudge from a dog’s cold nose.

"Cancers develop by a lineage, too," Alex Schier told the BBC. "Our technique can be used to follow these lineages during cancer formation - to tell us the relationships of cells within a tumour, and between the original tumour and secondary tumours formed by metastasis."

This means that, even under the best circumstances, scientists can’t directly test what the results of a given gun policy are. The best you can do is to compare what was happening in a state before and after a policy was enacted, or to compare two different states, one that has the policy and one that doesn’t. And that’s a pretty inexact way of working.

Add in enough assumptions, and you can eventually come up with an estimate. But is the estimate correct? Is it even close to reality? That’s a hard question to answer, because the assumptions you made—the correlations you drew between cause and effect, what you know and what you assume to be true because of that—might be totally wrong.

It’s hard to tease apart the effect of one specific change, compared to the effects of other things that could be happening at the same time.

This process of taking the observational data we do have and then running it through a filter of assumptions plays out in the real world in the form of statistical modeling. When the NAS report says that nobody yet knows whether more guns lead to more crime, or less crime, what they mean is that the models and the assumptions built into those models are all still proving to be pretty weak.

From either side of the debate, he said, scientists continue to produce wildly different conclusions using the same data. On either side, small shifts in the assumptions lead the models to produce different results. Both factions continue to choose sets of assumptions that aren’t terribly logical. It’s as if you decided that anybody with blue shoes probably had a belly-button piercing. There’s not really a good reason for making that correlation. And if you change the assumption—actually, belly-button piercings are more common in people who wear green shoes—you end up with completely different results.

The Intergovernmental Panel on Climate Change (IPCC) produces these big reports periodically, which analyze lots of individual papers. In essence, they’re looking at lots of trees and trying to paint you a picture of the forest. IPCC reports are available for free online, you can go and read them yourself. When you do, you’ll notice something interesting about the way that the reports present results. The IPCC never says, “Because we burned fossil fuels and emitted carbon dioxide into the atmosphere then the Earth will warm by x degrees.” Instead, those reports present a range of possible outcomes … for everything. Depending on the different models used, different scenarios presented, and the different assumptions made, the temperature of the Earth might increase by anywhere between 1.5 and 4.5 degrees Celsius.

What you’re left with is an environment where it’s really easy to prove that your colleague’s results are probably wrong, and it’s easy for him to prove that yours are probably wrong. But it’s not easy for either of you to make a compelling case for why you’re right.

Statistical modeling isn’t unique to gun research. It just happens to be particularly messy in this field. Scientists who study other topics have done a better job of using stronger assumptions and of building models that can’t be upended by changing one small, seemingly randomly chosen detail. It’s not that, in these other fields, there’s only one model being used, or even that all the different models produce the exact same results. But the models are stronger and, more importantly, the scientists do a better job of presenting the differences between models and drawing meaning from them.

“Climate change is one of the rare scientific literatures that has actually faced up to this,” Charles Manski said. What he means is that, when scientists model climate change, they don’t expect to produce exact, to-the-decimal-point answers.

“It’s been a complete waste of time, because we can’t validate one model versus another,” Pepper said. Most likely, he thinks that all of them are wrong. For instance, all the models he’s seen assume that a law will affect every state in the same way, and every person within that state in the same way. “But if you think about it, that’s just nonsensical,” he said.

On the one hand, that leaves politicians in a bit of a lurch. The response you might mount to counteract a 1.5 degree increase in global average temperature is pretty different from the response you’d have to 4.5 degrees. On the other hand, the range does tell us something valuable: the temperature is increasing.

The problem with this is that it flies in the face of what most of us expect science to do for public policy. Politics is inherently biased, right? The solutions that people come up with are driven by their ideologies. Science is supposed to cut that Gordian Knot. It’s supposed to lay the evidence down on the table and impartially determine who is right and who is wrong.

Manski and Pepper say that this is where we need to rethink what we expect science to do. Science, they say, isn’t here to stop all political debate in its tracks. In a situation like this, it simply can’t provide a detailed enough answer to do that—not unless you’re comfortable with detailed answers that are easily called into question and disproven by somebody else with a detailed answer.

Instead, science can reliably produce a range of possible outcomes, but it’s still up to the politicians (and, by extension, up to us) to hash out compromises between wildly differing values on controversial subjects. When it comes to complex social issues like gun ownership and gun violence, science doesn’t mean you get to blow off your political opponents and stake a claim on truth. Chances are, the closest we can get to the truth is a range that encompasses the beliefs of many different groups.

During adolescence, the neurocircuitry strengthens and allows for multitasking, enhanced ability to solve problems, and the capability to process complex information. Furthermore, adolescent brain plasticity provides an opportunity to develop talents and lifelong interests; however, neurotoxic insult, trauma, chronic stress, drug abuse, and sedentary lifestyles may have a negative impact during this sensitive period of brain maturation

In China, the “internet hive” no longer roils at foreigners alone, but also at China’s own military; and the U.S. military is now training for the “SMEIR” or “Social Media Environment and Internet Replication.”

How is Silicon Valley voting? Clinton: 88%; Undecided: 7%; Neither: 5%; Trump: 0%.

In the state, black students like Kenny are nearly four times as likely as their white peers to be charged with disturbing school. At least 22 states have disturbing school laws on the books, varying in their definition of the crime: whether it’s acting “in an obnoxious manner,” prohibiting “boisterous” behavior, or punishing students for “annoying conduct.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ali said the system only works in controlled environments, and with rigorous training. “So, it is not a big privacy concern for now, no worries there,” wrote Ali, a Ph.D. student at Michigan State University, in an email.

Routers could soon keep kids and older adults safe, log daily activities, or make a smart home run more smoothly—but, if invaded by a malicious hacker, they could also be turned into incredibly sophisticated hubs for monitoring and surveillance.

it helped shift moral psychology away from rationalist models that dominated in the 1980s and 1990s. In its place Haidt offered an understanding of morality from an intuitive and automatic level. As Haidt says on his website, “we are just not very good at thinking open-mindedly about moral issues, so rationalist models end up being poor descriptions of actual moral psychology.”

“the mind is divided into parts that sometimes conflict. Like a rider on the back of an elephant, the conscious, reasoning part of the mind has only limited control of what the elephant does.”

In the last few decades psychology began to understand the unconscious mind not as dark and suppressed as Freud did, but as intuitive, highly intelligent and necessary for good conscious reasoning. “Elephants,” he reminded me, “are really smart, much smarter than horses.”

we are 90 percent chimp 10 percent bee. That is to say, though we are inherently selfish, human nature is also about being what he terms “groupish.” He explained to me like this:

they developed the idea that humans possess six universal moral modules, or moral “foundations,” that get built upon to varying degrees across culture and time. They are: Care/harm, Fairness/cheating, Loyalty/betrayal, Authority/subversion, Sanctity/degradation, and Liberty/oppression. Haidt describes these six modules like a “tongue with six taste receptors.” “In this analogy,” he explains in the book, “the moral matrix of a culture is something like its cuisine: it’s a cultural construction, influenced by accidents of environment and history, but it’s not so flexible that anything goes. You can’t have a cuisine based on grass and tree bark, or even one based primarily on bitter tastes. Cuisines vary, but they all must please tongues equipped with the same five taste receptors. Moral matrices vary, but they all must please righteous minds equipped with the same six social receptors.”

The questionnaire eventually manifested itself into the website www.YourMorals.org, and it has since gathered over two hundred thousand data points. Here is what they found:

This is the crux of the disagreement between liberals and conservatives. As the graph illustrates, liberals value Care and Fairness much more than the other three moral foundations whereas conservative endorse all five more or less equally. This shouldn’t sound too surprising, liberals tend to value universal rights and reject the idea of the United States being superior while conservatives tend to be less concerned about the latest United Nation declaration and more partial to the United States as a superior nation.

Haidt began reading political psychology. Karen Stenner’s The Authoritarian Dynamic, “conveyed some key insights about protecting the group that were particularly insightful,” he said. The work of the French sociologist Emile Durkheim was also vital. In contrast to John Stuart Mill, a Durkheimian society, as Haidt explains in an essay for edge.org, “would value self-control over self-expression, duty over rights, and loyalty to one’s groups over concerns for out-groups.”

He was motivated to write The Righteous Mind after Kerry lost the 2004 election: “I thought he did a terrible job of making moral appeals so I began thinking about how I could apply moral psychology to understand political divisions. I started studying the politics of culture and realized how liberals and conservatives lived in their own closed worlds.” Each of these worlds, as Haidt explains in the book, “provides a complete, unified, and emotionally compelling worldview, easily justified by observable evidence and nearly impregnable to attack by arguments from outsiders.” He describes them as “moral matrices,” and thinks that moral psychology can help him understand them.

“When I say that human nature is selfish, I mean that our minds contain a variety of mental mechanisms that make us adept at promoting our own interests, in competition with our peers. When I say that human nature is also groupish, I mean that our minds contain a variety of mental mechanisms that make us adept at promoting our group’s interests, in competition with other groups. We are not saints, but we are sometimes good team players.” This is what people who had studied morality had not realized, “that we evolved not just so I can treat you well or compete with you, but at the same time we can compete with them.”

At first, Haidt reminds us that we are all trapped in a moral matrix where

our “elephants” only look for what confirms its moral intuitions while our “riders” play the role of the lawyer; we team up with people who share similar matrices and become close-minded; and we forget that morality is diverse. But on the other hand, Haidt is offering us a choice: take the blue pill and remain happily delusional about your worldview, or take the red pill, and, as he said in his 2008 TED talk, “learn some moral psychology and step outside your moral matrix.”

The great Asian religions, Haidt reminded the crowd at TED, swallowed their pride and took the red pill millennia ago. And by stepping out of their moral matrices they realized that societies flourish when they value all of the moral foundations to some degree. This is why Ying and Yang aren’t enemies, “they are both necessary, like night and day, for the functioning of the world.” Or, similarly, why the two of the high Gods in Hinduism, Vishnu the preserver (who stands for conservative principles) and Shiva the destroyer (who stands for liberal principles) work together.

In another experiment, we found that longer response times were correlated with less diverse responses, ruling out the possibility that participants with low mental loads simply took more time to generate an interesting response.

it seems that with a high mental load, you need more time to generate even a conventional thought. These experiments suggest that the mind’s natural tendency is to explore and to favor novelty, but when occupied it looks for the most familiar and inevitably least interesting solution.

In general, there is a tension in our brains between exploration and exploitation. When we are exploratory, we attend to things with a wide scope, curious and desiring to learn. Other times, we rely on, or “exploit,” what we already know, leaning on our expectations, trusting the comfort of a predictable environment

Much of our lives are spent somewhere between those extremes. There are functional benefits to both modes: If we were not exploratory, we would never have ventured out of the caves; if we did not exploit the certainty of the familiar, we would have taken too many risks and gone extinct. But there needs to be a healthy balance

All these loads can consume mental capacity, leading to dull thought and anhedonia — a flattened ability to experience pleasure.

ancient meditative practice helps free the mind to have richer experiences of the present

your life leaves too much room for your mind to wander. As a result, only a small fraction of your mental capacity remains engaged in what is before it, and mind-wandering and ruminations become a tax on the quality of your life

Honing an ability to unburden the load on your mind, be it through meditation or some other practice, can bring with it a wonderfully magnified experience of the world — and, as our study suggests, of your own mind.

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.”

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

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

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.”