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Taste does not simply equal your taste buds. It draws on information from all our senses as well as context. As a result, food is susceptible to the same trickery as wine. Adding yellow food dye to vanilla pudding leads people to experience a lemony taste. Diners eating in the dark at a chic concept restaurant confuse veal for tuna. Branding, packaging, and price tags are equally important to enjoyment. Cheap fish is routinely passed off as its pricier cousins at seafood and sushi restaurants. 

Just like with wine and classical music, we often judge food based on very different criteria than what we claim. The result is that our perceptions are easily skewed in ways we don’t anticipate. 

What does it mean for wine that presentation so easily trumps the quality imbued by being grown on premium Napa land or years of fruitful aging? Is it comforting that the same phenomenon is found in food and classical music, or is it a strike against the authenticity of our enjoyment of them as well? How common must these manipulations be until we concede that the influence of the price tag of a bottle of wine or the visual appearance of a pianist is not a trick but actually part of the quality?

To answer these questions, we need to investigate the underlying mechanism that leads us to judge wine, food, and music by criteria other than what we claim to value. And that mechanism seems to be the quick, intuitive judgments our minds unconsciously make

this unknowability also makes it easy to be led astray when our intuition makes a mistake. We may often be able to count on the price tag or packaging of food and wine for accurate information about quality. But as we believe that we’re judging based on just the product, we fail to recognize when presentation manipulates our snap judgments.

Participants were just as effective when watching 6 second video clips and when comparing their ratings to ratings of teacher effectiveness as measured by actual student test performance. 

The power of intuitive first impressions has been demonstrated in a variety of other contexts. One experiment found that people predicted the outcome of political elections remarkably well based on silent 10 second video clips of debates - significantly outperforming political pundits and predictions made based on economic indicators.

In a real world case, a number of art experts successfully identified a 6th century Greek statue as a fraud. Although the statue had survived a 14 month investigation by a respected museum that included the probings of a geologist, they instantly recognized something was off. They just couldn’t explain how they knew.

Cases like this represent the canon behind the idea of the “adaptive unconscious,” a concept made famous by journalist Malcolm Gladwell in his book Blink. The basic idea is that we constantly, quickly, and unconsciously do the equivalent of judging a book by its cover. After all, a cover provides a lot of relevant information in a world in which we don’t have time to read every page.

Gladwell describes the adaptive unconscious as “a kind of giant computer that quickly and quietly processes a lot of the data we need in order to keep functioning as human beings.”

In a famous experiment, psychologist Nalini Ambady provided participants in an academic study with 30 second silent video clips of a college professor teaching a class and asked them to rate the effectiveness of the professor.

In follow up experiments, Chia-Jung Tsay found that those judging musicians’ auditions based on visual cues were not giving preference to attractive performers. Rather, they seemed to look for visual signs of relevant characteristics like passion, creativity, and uniqueness. Seeing signs of passion is valuable information. But in differentiating between elite performers, it gives an edge to someone who looks passionate over someone whose play is passionate

Outside of these more eccentric examples, it’s our reliance on quick judgments, and ignorance of their workings, that cause people to act on ugly, unconscious biases

It’s also why - from a business perspective - packaging and presentation is just as important as the good or service on offer. Why marketing is just as important as product. 

Gladwell ends Blink optimistically. By paying closer attention to our powers of rapid cognition, he argues, we can avoid its pitfalls and harness its powers. We can blindly audition musicians behind a screen, look at a piece of art devoid of other context, and pay particular attention to possible unconscious bias in our performance reports.

But Gladwell’s success in demonstrating how the many calculations our adaptive unconscious performs without our awareness undermines his hopeful message of consciously harnessing its power.

As a former world-class tennis player and coach of over 50 years, Braden is a perfect example of the ideas behind thin slicing. But if he can’t figure out what his unconscious is up to when he recognizes double faults, why should anyone else expect to be up to the task?

flawed judgment in fields like medicine and investing has more serious consequences. The fact that expertise is so tricky leads psychologist Daniel Kahneman to assert that most experts should seek the assistance of statistics and algorithms in making decisions.

In his book Thinking, Fast and Slow, he describes our two modes of thought: System 1, like the adaptive unconscious, is our “fast, instinctive, and emotional” intuition. System 2 is our “slower, more deliberative, and more logical” conscious thought. Kahneman believes that we often leave decisions up to System 1 and generally place far “too much confidence in human judgment” due to the pitfalls of our intuition described above.

Not every judgment will be made in a field that is stable and regular enough for an algorithm to help us make judgments or predictions. But in those cases, he notes, “Hundreds of studies have shown that wherever we have sufficient information to build a model, it will perform better than most people.”

Experts can avoid the pitfalls of intuition more easily than laypeople. But they need help too, especially as our collective confidence in expertise leads us to overconfidence in their judgments. 

This article has referred to the influence of price tags and context on products and experiences like wine and classical music concerts as tricks that skew our perception. But maybe we should consider them a real, actual part of the quality.

Losing ourselves in a universe of relativism, however, will lead us to miss out on anything new or unique. Take the example of the song “Hey Ya!” by Outkast. When the music industry heard it, they felt sure it would be a hit. When it premiered on the radio, however, listeners changed the channel. The song sounded too dissimilar from songs people liked, so they responded negatively. 

It took time for people to get familiar with the song and realize that they enjoyed it. Eventually “Hey Ya!” became the hit of the summer.

Many boorish people talking about the ethereal qualities of great wine probably can't even identify cork taint because their impressions are dominated by the price tag and the wine label. But the classic defense of wine - that you need to study it to appreciate it - is also vindicated. The open question - which is both editorial and empiric - is what it means for the industry that constant vigilance and substantial study is needed to dependably appreciate wine for the product quality alone. But the questions is relevant to the enjoyment of many other products and experiences that we enjoy in life.

Maybe the most important conclusion is to not only recognize the fallibility of our judgments and impressions, but to recognize when it matters, and when it doesn’t

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 problem is that software engineers don’t understand the problem they’re trying to solve, and don’t care to,” says Leveson, the MIT software-safety expert. The reason is that they’re too wrapped up in getting their code to work.

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.

“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.

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.

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.”

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.

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.

“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.

“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.

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.

“Is there a weight of evidence that says we can totally ignore both dietary cholesterol and LDL? Absolutely not,” he said. “You can legitimately say we’re starting to rethink some things, but ignoring LDL could absolutely result in heart attacks and strokes.

The medical community’s understanding of the danger of cholesterol is changing. Many cardiologists are starting to think that independent of other considerations, the level of LDL in our blood may not be as important as it previously seemed.

In November, the American Heart Association and the American College of Cardiology released new guidelines that redefined the use of statins. While they continue to recommend that people at high risk for heart disease and people with LDL levels above 189 take a statin, the long-standing goal of lowering one’s LDL level to 70 is no longer deemed worthwhile to monitor.

Katz acknowledges that dietary cholesterol may be an innocuous part of an overall healthy diet. “The problem is that people are going to get their dietary cholesterol from things other than fish and eggs; they’re going to get it from meats and dairies. The problem with diets like that is if you eat more of A, you’re probably going to eat less of B. So people who are eating more meat and dairy and high-fat, high-cholesterol foods are eating fewer plants—they’re not eating beans; they’re not eating lentils. So yes, I think it’s entirely confabulated and contrived, and potentially dangerous on the level of lethal.”

Having talked to all of these people and read their work, here is how I walk away from this. Oxidative stress will increasingly be the target of medical treatments and preventive diets. We’ll hear more about the role of blood sugar in Alzheimer’s and continue to focus on moderating intake of refined carbohydrates. The consensus remains that too much LDL is bad for you. We do not have reason to believe that gluten is bad for most people. It does cause reactive symptoms in some people. Peanuts can kill some people, but that does not mean they are bad for everyone

I agree with Katz that the diets consistently shown to have good long-term health outcomes—both mental and physical—include whole grains and fruits, and are not nearly as high in fat as what Perlmutter proposes.

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

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

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

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

Does he have a locker-room mentality?

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

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

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

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

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

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

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

Clearwater

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

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

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

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

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

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

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

256,000 are registered as Democrats

252,000 as Republicans.

a swing county in a swing state

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

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

lingering perceptions based on dramatic, painful events are impacting decision-making even when those events are over.

Ariely said a home country investment bias might be generated by two perceptual factors.“The first is an overly optimistic belief about one’s own economy; an expectation of performance in their country that is higher than what would be statistically realistic. The second reason is most likely due to procedural difficulties in investing outside the country – such as less knowledge about how to access these markets.”

investors may be making decisions driven more by personal bias or irrational belief than by reality and, in doing so, they may be hindering their own investment success.

The problem? These decisions may hinder their ability to reach their desired retirement or savings goals. The choice is between changing the goal—or changing the means of reaching it.

"Previous small-scale studies have failed to identify robust evidence of a benefit and larger, randomised controlled trials are not yet completed," Dr. Frank Ruschitzka, director of the Heart Center at University Hospital Zurich and the study's coauthor, said in a statement.

President Donald Trump has repeatedly touted hydroxychloroquine as a potential coronavirus cure. Earlier this week he claimed he was taking daily doses of it as a prophylaxis to prevent infection.

Items such as masks and hand sanitizer will be familiar sights in stuffed backpacks. Classes and school buses will have fewer people while some office meetings will be conducted by video conference, experts say.

Additionally, the study found serious cardiac arrhythmias were more common among patients who received any of the four treatments. The largest increase was among the group treated with hydroxychloroquine and an an antibiotic -- 8% of those patients developed a heart arrythmia, compared to 0.3% of the control group.

Children with underlying health conditions are especially vulnerable, and it's crucial that people follow rules to keep everyone safe, Altmann said. She shared other things US schools must address before unlatching their doors.

"We need to quickly test them, diagnose, isolate and then contact trace, which is a lot easier when there's fewer kids they've come into contact with throughout the day," Altmann added.

In New York, 16.6% of people have been infected, compared with 1% in California, the researchers said.

In the most severe scenario, the CDC assumes that 1% of people overall with Covid-19 and symptoms will die. In the least severe scenario, it puts that number at 0.2%.

"Go out, wear a mask, stay six feet away from anyone so you have the physical distancing," he said. "Go for a run. Go for a walk. Go fishing. As long as you're not in a crowd and you're not in a situation where you can physically transmit the virus."

Alaska is allowing all businesses to reopen, as well as houses of worship, libraries, museums and sporting activities, starting at 8 a.m. Alaska has the fewest cases of all states and has reported single-digit new cases since mid-April.

"I could do open heart surgery right now. My hands are steady, my eyes are good," Wareham said. "My blood pressure is 117. I have noticed no deterioration in my mental ability with my age. If you gave me something to memorize, I would memorize it now just as quickly as when I was 20."

Wareham passed away last year, at the age of 104. Like 10% of the Adventist community, Wareham was a vegan. Another 30% are lacto-ovo vegetarians who eat dairy and eggs, while another 8% eat fish but not other meat. Vegetarianism is so prevalent that no meat can be purchased at the cafeterias at the university and medical center.

Other key factors to longevity: Only 1% of the Seventh-day Adventist community in the study smokes. Little to no alcohol is consumed. Daily exercise out in the fresh air of nature is the norm. The church advocates a life of service, so dedication to volunteering, humanitarian and mission work is typical, which contributes to a sense of community.

"The bulk of evidence suggests that changing a few simple lifestyle factors can have a profound difference in the risk of major diseases and the likelihood of living a long life," Orlich said. "The body has an amazing ability to, um, you know, heal itself to some degree.

Such a dark age is a growing possibility. In a recent report, the National Academies of Sciences, Engineering and Medicine concluded that human-caused global warming was already altering patterns of some extreme weather events

disrupting nature’s patterns could extend well beyond extreme weather, with far more pervasive impacts.

Our foundation of Earth knowledge, largely derived from historically observed patterns, has been central to society’s progress.

Science has accelerated this learning process through advanced observation methods and pattern discovery techniques. These allow us to anticipate the future with a consistency unimaginable to our ancestors

As Earth’s warming stabilizes, new patterns begin to appear. At first, they are confusing and hard to identify. Scientists note similarities to Earth’s emergence from the last ice age. These new patterns need many years — sometimes decades or more — to reveal themselves fully, even when monitored with our sophisticated observing systems

The list of possible disruptions is long and alarming. We could see changes to the prevalence of crop and human pests, like locust plagues set off by drought conditions; forest fire frequency; the dynamics of the predator-prey food chain; the identification and productivity of reliably arable land, and the predictability of agriculture output.

Historians of the next century will grasp the importance of this decline in our ability to predict the future. They may mark the coming decades of this century as the period during which humanity, despite rapid technological and scientific advances, achieved “peak knowledge” about the planet it occupies

The intermediate time period is our big challenge. Without substantial scientific breakthroughs, we will remain reliant on pattern-based methods for time periods between a month and a decade. The problem is, as the planet warms, these patterns will become increasingly difficult to discern.

The oceans, which play a major role in global weather patterns, will also see substantial changes as global temperatures rise. Ocean currents and circulation patterns evolve on time scales of decades and longer, and fisheries change in response. We lack reliable, physics-based models to tell us how this occurs

Civilization’s understanding of Earth has expanded enormously in recent decades, making humanity safer and more prosperous. As the patterns that we have come to expect are disrupted by warming temperatures, we will face huge challenges feeding a growing population and prospering within our planet’s finite resources. New developments in science offer our best hope for keeping up, but this is by no means guaranteed

Our grandchildren could grow up knowing less about the planet than we do today. This is not a legacy we want to leave them. Yet we are on the verge of ensuring this happens.

Doing the opposite by writing a dissertation. The endless look-at-me-I’m-so-cute one-line reviews are a constant on Letterboxd, but there’s plenty that go too far the other way, too. I love a good longform review that does a deep dive and carefully considers themes, of a kind that wasn’t really possible in the era of all-print media. But this is not the venue. Start a blog like everybody else.

match your engagement to the structure of the network.

Fake contrarianism.

Comparing your taste to some other party who you know will be unpopular with other users.

Utterly superficial appeals to facile political critiques. These are usually wrong, and when right are shooting the fattest of fish in the smallest of barrels. Yes, Gone With the Wind is pretty fucked up in 2023 political terms! Where would we be without your wisdom to guide us? Using politics to inform a review is great. Explaining why the implicit or explicit political themes of a movie are trouble is fine. Arriving at a pat political condemnation as a substitute for having an aesthetic take on a movie is boring and pointless.

Inventing your own scoring system in a network with a five-star system. 78/100! B+! Three boxes of popcorn! There’s a star system right there baked into the app, jackass.

Pretending to believe (but not really believing) that people won’t recognize your name as a professional film critic