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Conservative historians asserted that relativists were ruining everything, that truth did exist. They criticized post-modernism as a mask for moral relativism, connected this immorality with the popular movements of the 1960s, and asserted their own moral primacy (the Moral Majority).

The platform of the Republican Party about climate change and health care, two of our most pressing issues, is just one big lie

The intersection of a Republican Party which sees no value in distinguishing between truth and lies and an emerging technology that makes spreading lies incredibly easy is a great political danger. Is there truth? Not if those in power in America don’t care.

The use of a fabricated story about Ukraine and Joe Biden is a set of lies, that then led to one of the greatest scenes of collective public lying in American history, the response of Republican Representatives and Senators to the impeachment.

We are being bombarded with carefully crafted lies throughout cyberspace, designed to distort the results of the 2020 election. False stories about Joe Biden and Ukraine have already spread virally to millions of people.

Disinformation spread by bots can come from anywhere on the globe. The technology is non-partisan.

today we suffer from a multiplication of lies as a Republican tactic to win elections.

Does this mean that there is no truth? That any statement can be shown to be untrue by people with a different point of view? Objectivity is impossible, so there is no objective truth

This line of thinking was taken up especially by literary scholars, who argued that every text has multiple, perhaps infinite meanings. There is no true interpretation of a piece of writing

When this was expanded into other disciplines, it became more confusing. Some historians argued that it is impossible to make a true historical statement. Every statement can have multiple, even contradictory meanings. Excellent examples of this would be stat

Clinton played by old rules; Trump did not. He recognized the potential rewards of exploiting what the Internet offered, and he conducted his campaign through unconventional means.

“That’s what Donald Trump realized that a lot of us didn’t,” Persily said. “That it was more important to swamp the communication environment than it was to advocate for a particular belief or fight for the truth of a particular story,”

Persily notes that the Internet reacted to the Trump campaign “like an ecosystem welcoming a new and foreign species. His candidacy triggered new strategies and promoted established Internet forces. Some of these (such as the ‘alt-right’) were moved by ideological affinity, while others sought to profit financially or to further a geopolitical agenda.

The rise and power of the Internet has accelerated the decline of institutions that once provided a mediating force in campaigns. Neither the legacy media nor the established political parties exercise the power they once had as referees, particularly in helping to sort out the integrity of information.

legacy media that once helped set the agenda for political conversation now often take their cues from new media.

The Internet, however, involves characteristics that heighten the disruptive and damaging influences on political campaigns. One, Persily said, is the velocity of information, the speed with which news, including fake news, moves and expands and is absorbed. Viral communication can create dysfunction in campaigns and within democracies.

Another factor is the pervasiveness of anonymous communication, clearly greater and more odious today. Anonymity facilitates a coarsening of speech on the Internet. It has become more and more difficult to determine the sources of such information, including whether these communications are produced by real people or by automated programs known as “bots.”

“the prevalence of bots in spreading propaganda and fake news appears to have reached new heights. One study found that between 16 September and 21 October 2016, bots produced about a fifth of all tweets related to the upcoming election. Across all three presidential debates, pro-Trump twitter bots generated about four times as many tweets as pro-Clinton bots. During the final debate in particular, that figure rose to seven times as many.”

the fear of dark money and “shady outsiders” running television commercials “seems quaint when compared to networks of thousands of bots of uncertain geographic origin creating automated messages designed to malign candidates and misinform voters.”

When asked how worrisome all this is, Persily said, “I’m extremely concerned.” He was quick to say he did not believe government should or even could regulate this new environment. But, he said, “We need to come to grips with how the new communication environment affects people’s political beliefs, the information they receive and then the choices that they make.”

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.

68% reported they had received mental health diagnoses. The conditions they revealed included post-traumatic stress disorder, bipolar disorder, paranoid schizophrenia and Munchausen syndrome by proxy

Research has long revealed connections between psychological problems and beliefs in conspiracy theories. For example, anxiety increases conspiratorial thinking, as do social isolation and loneliness.

More recently, following Jeffrey Epstein’s apparent suicide in federal jail, Trump retweeted an uncorroborated theory that suggested the death of the well-connected financier, who was charged with sex trafficking of minors and conspiracy, was suspicious and somehow linked to former President Bill Clinton.

“The chief conspiratorialist of the last 10 years is now the President of the United States,” says Harvard University researcher Joseph Vitriol, who studies political psychology. “Because of that, what we might be seeing is increased influence and pervasiveness of these beliefs.”

“It used to be a lot harder for things to go viral,” says Micah Schaffer, a technology policy consultant who crafted YouTube’s first policies when he worked for the company between 2006 and 2009. “Now, without any human intervention, you could have a machine that says a lot of people are watching this and put it on blast to a mass audience.”

Facebook faced its own pressures to act in March after an Ohio teenager, who got vaccinated against his mother’s wishes, testified about the dangers of misinformation during a widely viewed Senate hearing

“Conspiracy theories are effective at doing the things that they do,” says Mike Wood, a lecturer at England’s University of Winchester, who specializes in the psychology of conspiracy theories. “They motivate people to take actions—to vote or to not vote, to vaccinate their kids or not to vaccinate their kids, to do all of these things that are important.”

Much of the searing power of this viral image comes from how it captures what appears to be the aggressive demeanour of the police officers, while also showing the outrage and defiance of Stevenson. Serious questions are now being raised about the Met’s handling of the situation.

As a research team, we are not surprised by women’s outrage. But we are surprised at the government’s response of proposing that increased infrastructure alone, like better lighting or CCTV cameras, will make women’s lives any safer.

Rather than teaching women how to protect themselves, our research dating back over a decade shows there is a need for education that tackles the gendered and sexual inequities that normalise violent, predatory forms of masculinit

Young people struggle to know how to deal with this kind of online sexual harassment and abuse. The numbers of young people who reported their experiences were staggeringly low with only 6% reporting it to the social media platform, 3% telling parents and a mere 1% reporting it to their school.

As recent petitions show, there is a demand from young people for better education, regardless of their gender, on issues such as consent, healthy relationships and sexual violence. And in an increasingly digital world, this education must account for everyday online practices, like pestering girls for nudes or sending unwanted dick pics, in addition to offline forms of sexual violence.

Education is not just needed for young people, but also for teachers, school leaders and parents.

Although our evaluation of these resources is ongoing, our preliminary findings show that schools adopting our training and policies have led to dramatic reductions in online sexual harassment, increased teacher confidence in handling these issues and improved student mental health.

Globally, the numbers can be even worse, and the stakes often higher. “Say a person comes into the hospital in Sierra Leone with a fever and flulike symptoms,” DeRisi says. “After a few days, or a week, they die. What caused that illness? Most of the time, we never find out. Because if the cause isn’t something that we can culture and test for” — like hepatitis, or strep throat — “it basically just stays a mystery.”

It would be better, DeRisi says, to watch for rare cases of mystery illnesses in people, which often exist well before a pathogen gains traction and is able to spread.

Based on a retrospective analysis of blood samples, scientists now know that H.I.V. emerged nearly a dozen times over a century, starting in the 1920s, before it went global.

Zika was a relatively harmless illness before a single mutation, in 2013, gave the virus the ability to enter and damage brain cells.

The beauty of this approach” — running blood samples from people hospitalized all over the world through his system, known as IDseq — “is that it works even for things that we’ve never seen before, or things that we might think we’ve seen but which are actually something new.”

In this scenario, an undiscovered or completely new virus won’t trigger a match but will instead be flagged. (Even in those cases, the mystery pathogen will usually belong to a known virus family: coronaviruses, for instance, or filoviruses that cause hemorrhagic fevers like Ebola and Marburg.)

And because different types of bacteria require specific conditions in order to grow, you also need some idea of what you’re looking for in order to find it.

The same is true of genomic sequencing, which relies on “primers” designed to match different combinations of nucleotides (the building blocks of DNA and RNA).

Even looking at a slide under a microscope requires staining, which makes organisms easier to see — but the stains used to identify bacteria and parasites, for instance, aren’t the same.

The practice that DeRisi helped pioneer to skirt this problem is known as metagenomic sequencing

Unlike ordinary genomic sequencing, which tries to spell out the purified DNA of a single, known organism, metagenomic sequencing can be applied to a messy sample of just about anything — blood, mud, seawater, snot — which will often contain dozens or hundreds of different organisms, all unknown, and each with its own DNA. In order to read all the fragmented genetic material, metagenomic sequencing uses sophisticated software to stitch the pieces together by matching overlapping segments.

The assembled genomes are then compared against a vast database of all known genomic sequences — maintained by the government-run National Center for Biotechnology Information — making it possible for researchers to identify everything in the mix

Traditionally, the way that scientists have identified organisms in a sample is to culture them: Isolate a particular bacterium (or virus or parasite or fungus); grow it in a petri dish; and then examine the result under a microscope, or use genomic sequencing, to understand just what it is. But because less than 2 percent of bacteria — and even fewer viruses — can be grown in a lab, the process often reveals only a tiny fraction of what’s actually there. It’s a bit like planting 100 different kinds of seeds that you found in an old jar. One or two of those will germinate and produce a plant, but there’s no way to know what the rest might have grown into.

Such studies have revealed just how vast the microbial world is, and how little we know about it

“The selling point for researchers is: ‘Look, this technology lets you investigate what’s happening in your clinic, whether it’s kids with meningitis or something else,’” DeRisi said. “We’re not telling you what to do with it. But it’s also true that if we have enough people using this, spread out all around the world, then it does become a global network for detecting emerging pandemics

One study found more than 1,000 different kinds of viruses in a tiny amount of human stool; another found a million in a couple of pounds of marine sediment. And most were organisms that nobody had seen before.

After the Biohub opened in 2016, one of DeRisi’s goals was to turn metagenomics from a rarefied technology used by a handful of elite universities into something that researchers around the world could benefit from

metagenomics requires enormous amounts of computing power, putting it out of reach of all but the most well-funded research labs. The tool DeRisi created, IDseq, made it possible for researchers anywhere in the world to process samples through the use of a small, off-the-shelf sequencer, much like the one DeRisi had shown me in his lab, and then upload the results to the cloud for analysis.

he’s the first to make the process so accessible, even in countries where lab supplies and training are scarce. DeRisi and his team tested the chemicals used to prepare DNA for sequencing and determined that using as little as half the recommended amount often worked fine. They also 3-D print some of the labs’ tools and replacement parts, and offer ongoing training and tech support

The metagenomic analysis itself — normally the most expensive part of the process — is provided free.

But DeRisi’s main innovation has been in streamlining and simplifying the extraordinarily complex computational side of metagenomics

IDseq is also fast, capable of doing analyses in hours that would take other systems weeks.

“What IDseq really did was to marry wet-lab work — accumulating samples, processing them, running them through a sequencer — with the bioinformatic analysis,”

“Without that, what happens in a lot of places is that the researcher will be like, ‘OK, I collected the samples!’ But because they can’t analyze them, the samples end up in the freezer. The information just gets stuck there.”

Meningitis itself isn’t a disease, just a description meaning that the tissues around the brain and spinal cord have become inflamed. In the United States, bacterial infections can cause meningitis, as can enteroviruses, mumps and herpes simplex. But a high proportion of cases have, as doctors say, no known etiology: No one knows why the patient’s brain and spinal tissues are swelling.

When Saha and her team ran the mystery meningitis samples through IDseq, though, the result was surprising. Rather than revealing a bacterial cause, as expected, a third of the samples showed signs of the chikungunya virus — specifically, a neuroinvasive strain that was thought to be extremely rare. “At first we thought, It cannot be true!” Saha recalls. “But the moment Joe and I realized it was chikungunya, I went back and looked at the other 200 samples that we had collected around the same time. And we found the virus in some of those samples as well.”

Until recently, chikungunya was a comparatively rare disease, present mostly in parts of Central and East Africa. “Then it just exploded through the Caribbean and Africa and across Southeast Asia into India and Bangladesh,” DeRisi told me. In 2011, there were zero cases of chikungunya reported in Latin America. By 2014, there were a million.

Chikungunya is a mosquito-borne virus, but when DeRisi and Saha looked at the results from IDseq, they also saw something else: a primate tetraparvovirus. Primate tetraparvoviruses are almost unknown in humans, and have been found only in certain regions. Even now, DeRisi is careful to note, it’s not clear what effect the virus has on people. “Maybe it’s dangerous, maybe it isn’t,” DeRisi says. “But I’ll tell you what: It’s now on my radar.

it reveals a landscape of potentially dangerous viruses that we would otherwise never find out about. “What we’ve been missing is that there’s an entire universe of pathogens out there that are causing disease in humans,” Imam notes, “ones that we often don’t even know exist.”

“The plan was, Let’s let researchers around the world propose studies, and we’ll choose 10 of them to start,” DeRisi recalls. “We thought we’d get, like, a couple dozen proposals, and instead we got 350.”

Metagenomic sequencing is especially good at what scientists call “environmental sampling”: identifying, say, every type of bacteria present in the gut microbiome, or in a teaspoon of seawater.

“When you draw blood from someone who has a fever in Ghana, you really don’t know very much about what would normally be in their blood without fever — let alone about other kinds of contaminants in the environment. So how do you interpret the relevance of all the things you’re seeing?”

Such criticisms have led some to say that metagenomics simply isn’t suited to the infrastructure of developing countries. Along with the problem of contamination, many labs struggle to get the chemical reagents needed for sequencing, either because of the cost or because of shipping and customs holdups

we’re less likely to be caught off-guard. “With Ebola, there’s always an issue: Where’s the virus hiding before it breaks out?” DeRisi explains. “But also, once we start sampling people who are hospitalized more widely — meaning not just people in Northern California or Boston, but in Uganda, and Sierra Leone, and Indonesia — the chance of disastrous surprises will go down. We’ll start seeing what’s hidden.”

Today, the average weekly screen time for an American adult – brace yourself; this is not a typo – is 74 hours (and still going up)

The soundbite has been replaced by virality, meme, hot take, tweet. Can serious national issues really be explored in any coherent, meaningful way in such a fragmented, attention-challenged environment?

how engaged can any populace be when the most we’re asked to do is to like or not like a particular post, or “sign” an online petition?

How seriously should anyone take us, or should we take ourselves, when the “optics” of an address or campaign speech – raucousness, maybe actual violence, childishly attention-craving gestures or facial expressions – rather than the content of the speech determines how much “airtime” it gets, and how often people watch, share and favorite it?

Our public discourse has become so trivialized, it’s astounding that we still cling to the word “debates” for what our presidential candidates do onstage when facing each other.

Who can be shocked by the rise of a reality TV star, a man given to loud, inflammatory statements, many of which are spectacularly untrue but virtually all of which make for what used to be called “good television”?

Who can be appalled when the coin of the realm in public discourse is not experience, thoughtfulness or diplomacy but the ability to amuse – no matter how maddening or revolting the amusement?

“Television is a speed-of-light medium, a present-centered medium,” my father wrote. “Its grammar, so to say, permits no access to the past … history can play no significant role in image politics. For history is of value only to someone who takes seriously the notion that there are patterns in the past which may provide the present with nourishing traditions.”

Later in that passage, Czesław Miłosz, winner of the Nobel prize for literature, is cited for remarking in his 1980 acceptance speech that that era was notable for “a refusal to remember”; my father notes Miłosz referencing “the shattering fact that there are now more than one hundred books in print that deny that the Holocaust ever took place”.

“An Orwellian world is much easier to recognize, and to oppose, than a Huxleyan,” my father wrote. “Everything in our background has prepared us to know and resist a prison when the gates begin to close around us … [but] who is prepared to take arms against a sea of amusements?”

I wish I could tell you that, for all his prescience, my father also supplied a solution. He did no

First: treat false allegations as an opportunity. Seek information as close to the source as possible.

Second: don’t expect “the media” to do this job for you. Some of its practitioners do, brilliantly and at times heroically. But most of the media exists to sell you things.

Finally, and most importantly, it should be the responsibility of schools to make children aware of our information environments, which in many instances have become our entertainment environments

We must teach our children, from a very young age, to be skeptics, to listen carefully, to assume everyone is lying about everything. (Well, maybe not everyone.)

“what is required of us now is a new era of responsibility … giving our all to a difficult task. This is the price and the promise of citizenship.”

we need more than just hope for a way out. We need a strategy, or at least some tactics.

He points out that if one HIV vaccine had succeeded, the company behind it would have won big. Instead, all of the competitors in the vaccine field learned from collective failure and contributed to collective wisdom. The many false starts of HIV vaccination sired an explosion of new technologies and helped usher in a possible new golden age of vaccines.

We can call our record-breaking vaccine-development process good luck. Or we can call it what it really is: a ringing endorsement for the essential role of science in the world.

As a parable of scientific progress, I sometimes imagine the life cycle of a tree. Basic scientific research plants a variety of seeds. Some of these seeds fail entirely; the research goes nowhere. Some seeds become tiny shrubs; the research doesn’t fail entirely, but it produces little of value

And some seeds blossom into towering trees with abundant fruit that scientists, companies, and technologists pluck and turn into the products that change our lives. For years, mRNA technology looked like a shrub. In 2020, it blossomed in full view.

. “Any successful virus has to get beyond that first defense system. The more successful it is at doing that, the better off the virus is.”

A lot of researchers focused their attention on the nine mutations that alter the so-called spike protein that covers the coronavirus and allows it to invade cells

They found that lung cells with Alpha made drastically less interferon, a protein that switches on a host of immune defenses.

It’s making itself more invisible,”

They found that Alpha-infected cells make a lot of extra copies — some 80 times more than other versions of the virus — of a gene called Orf9b.

dampening the production of interferon and a full immune response. The virus, protected from attack, has better odds of making copies of itself.

people infected with Alpha have a more robust reaction than they would with other variants, coughing and shedding virus-laden mucus from not only their mouths, but also their noses — making Alpha even better at spreading.

. They may have independently evolved their own tricks for manipulating our immune system.

But studies on people who recover naturally from Covid-19 have shown that their immune systems learn to recognize other viral proteins, including Orf9b.

“It’s quite a tricky enterprise, but becoming more possible as we learn more,”

On Wednesday, several prominent Republicans, including Mr. Trump, repeated their calls for Congress to repeal Section 230 of the Communications Decency Act, a law that shields tech platforms from many lawsuits over user-generated content.

That leaves the companies in a precarious spot. They are criticized when they allow misinformation to spread. They are also criticized when they try to prevent it.

Perhaps the strangest idea to emerge in the past couple of days, though, is that these services are only now beginning to exert control over what we see. Representative Doug Collins, Republican of Georgia, made this point in a letter to Mark Zuckerberg, the chief executive of Facebook, in which he derided the social network for using “its monopoly to control what news Americans have access to.”

The truth, of course, is that tech platforms have been controlling our information diets for years, whether we realized it or not. Their decisions were often buried in obscure “community standards” updates, or hidden in tweaks to the black-box algorithms that govern which posts users see.

Their leaders have always been editors masquerading as engineers.

What’s happening now is simply that, as these companies move to rid their platforms of bad behavior, their influence is being made more visible.

Rather than letting their algorithms run amok (which is an editorial choice in itself), they’re making high-stakes decisions about flammable political misinformation in full public view, with human decision makers who can be debated and held accountable for their choices.

After years of inaction, Facebook and Twitter are finally starting to clean up their messes. And in the process, they’re enraging the powerful people who have thrived under the old system.

Some people find comfort in resorting to a conspiracy theory whenever they have a sense of a loss of control or they’re confronted with a major adverse event that no one has control over. So every time there’s a mass shooting in the U.S., I can guarantee you ahead of time that there will be a conspiracy theory about it.

“Usually by four to six weeks, there’s not much left,” said Deepta Bhattacharya, an immunologist at the University of Arizona. But germinal centers stimulated by the mRNA vaccines are “still going, months into it, and not a lot of decline in most people.”

The broader the range and the longer these cells have to practice, the more likely they are to be able to thwart variants of the virus that may emerge.

“Everyone always focuses on the virus evolving — this is showing that the B cells are doing the same thing,” said Marion Pepper, an immunologist at the University of Washington in Seattle. “And it’s going to be protective against ongoing evolution of the virus, which is really encouraging.”

Dr. Ellebedy’s team found that 15 weeks after the first dose of vaccine, the germinal center was still highly active in all 14 of the participants, and that the number of memory cells that recognized the coronavirus had not declined.

“The fact that the reactions continued for almost four months after vaccination — that’s a very, very good sign,” Dr. Ellebedy said. Germinal centers typically peak one to two weeks after immunization, and then wane.

After an infection or a vaccination, a specialized structure called the germinal center forms in lymph nodes. This structure is an elite school of sorts for B cells — a boot camp where they become increasingly sophisticated and learn to recognize a diverse set of viral genetic sequences.

The results suggest that a vast majority of vaccinated people will be protected over the long term — at least, against the existing coronavirus variants

But older adults, people with weak immune systems and those who take drugs that suppress immunity may need boosters; people who survived Covid-19 and were later immunized may never need them at all.

In the absence of variants that sidestep immunity, in theory immunity could last a lifetime, experts said. But the virus is clearly evolving.

“Anything that would actually require a booster would be variant-based, not based on waning of immunity,” Dr. Bhattacharya said. “I just don’t see that happening.”

The good news: A booster vaccine will probably have the same effect as prior infection in immunized people, Dr. Ellebedy said. “If you give them another chance to engage, they will have a massive response,” he said, referring to memory B cells.

Dr. Ellebedy said the results also suggested that these signs of persistent immune reaction might be caused by mRNA vaccines alone, as opposed to those made by more traditional means, like Johnson & Johnson’s

But that is an unfair comparison, because the Johnson & Johnson vaccine is given as a single dose, Dr. Iwasaki said: “If the J & J had a booster, maybe it will induce this same kind of response.”

we find information or misinformation “22 times more memorable in narrative form.”

It’s a contagion, and thanks to social media, or “participatory propaganda,” highly viral

The Fox News counternarrative model is as simple as it is cunning. The segment producer’s job is to get the answers to two questions: What is the most emotionally engaging story we have right now

The next question is, how do we construct a counternarrative that includes as many existing other believable meta-narratives as possible

The hunt for the killer narrative starts with the “Morning Memo” to the producers. It shares the interpretation from the vice president of news of the highest-trending articles on Foxnews.com and Drudge

The next move is to get a few Fox News contributor regulars — elected officials and paid pundits — not just to deliver the new counternarrative but also to wrap it inside an existing or new meta-narrative.

What do the ratings tell the producers are the most engaging meta-narratives for the over 80 million Fox News viewers on all digital platforms?

Conspiracy theories.

Why do people love conspiracy stories? It’s human behavior

The emotional payoff from being on the inside of a conspiracy is a self-esteem jolt that makes you feel smarter than your tribal foe and keeps your eyeballs glued to screens

Every successful Fox News segment producer has the conspiracy script down cold. These segments work best when the “proof” of a conspiracy against a tribal leader — in this case the Republican president — makes the viewer feel under attack as well

it helps when a proposed conservative thought leader mixes in the meta-narrative to the effect that “we are victimized again by the condescending Beltway elites.”

Believing in conspiracy theories is a psychological construct for people to take back some semblance of control in their lives. It inflates their sense of importance. It makes them feel they have access to “special knowledge” that the rest of the world is “too blind,” “too dumb” or “too corrupt” to understand.

Uncertainty produces anxiety, but you can counter that, in part, by understanding what to expect on Election Day this year.

This year, Dr. Tillery observed, there’s a significant chance the presidential election will not be called on Tuesday night.

“Voters should get some comfort from knowing that we do have counting procedures and auditing procedures and voter notification procedures that would make it better for us to be calm and let those processes play out,”

Other analog activities can help you cope, too. “Anxious thoughts race by,” Dr. Stosny said, “and the faster they go, the less realistic they get.” Instead of dwelling on them, write them out longhand; this will slow down and moderate their frenetic pace. Next to each source of fear, write down how you’ll respond to it.

Be on the lookout for viral disinformation: Check the provenance of news articles or memes making polarizing political claims or calling the election before a mainstream news outlet has done so.

“Humans are quite resilient,” she said. “There’s a strong possibility that you will be able to cope.”

f a family member approaches you with anger, try to respond with compassion. Consider setting a time limit on your political discussions, Dr. Lee said, agreeing in advance to a fun, shared activity when your time is up.

If you’re feeling anxious or overwhelmed, or need to take time out of a challenging conversation, go for a walk or run, and try to spend at least 30 minutes outside.

68 percent of adults report finding the election to be a significant source of stress. This is due, at least in part, to the vitriol and name-calling exhibited by candidates,

“One of the things Covid has made abundantly clear in so many aspects of our life is that we have a lot of cracks in the system,”

“This is the first step in a long journey of really reimagining what America is going to look like and what America is going to be about,” Ms. Holland said. “That’s not going to end when the voting does.”

the idea that in a perfectly open media market, truth will prevail may have been disproven: fake viral stories outperform real news almost every single time.

#BidenCrimeFamily, and the typo, is a crash course in how to rally supporters around a conspiracy theory — while neutering the attempts of social media companies to stop it. Mr. Trump has used this same tactic to sow doubt about mail-in ballots and the integrity of the election.

On Wednesday afternoon, with the presidential race unresolved, a protester in Nevada interrupted an election official’s news conference by yelling, “The Biden crime family is stealing the election!”

#BidenCrimeFamily is part of a yearlong, effective disinformation campaign against Joe Biden. In the final days of the presidential race, the hashtag was used on Twitter and Facebook, as well as the darker parts of the web, including 4chan and Parler. It was repeated in the right-wing media ecosystem, like Steve Bannon’s podcast and The Gateway Pundit.

In the last month, on Facebook alone, it reached at least 277,000 people, according to CrowdTangle — and that’s only on non-private pages

Stage 2: Seeding on Social MediaIn any manipulation campaign, the second stage involves campaign operators strategically spreading the hashtag across the media ecosystem.

Stage 1: OriginsFor more than a year, right-wing media and partisans have pushed “Biden crime family” as a viral slogan. Media manipulation campaigns are usually conjured in small, hidden spaces by a few operators with an agenda. But in this case, it was influential media and political personalities who got the ball rolling.

Clive Wynne, a psychologist specializing in dog behavior, contends that what makes your furry friend special is not its intellect but its ability to bond with you. And not just with you. The dog’s ability to bond across species — with sheep, goats, even (horrors!) cats

Thirteen species of salmon and steelhead trout are considered threatened or endangered in the Northwest’s Columbia basin region

The fish are a keystone food source for other species, and an endangered population of orcas may be starving for lack of enough wild salmon to eat. Many scientists favor removing dams on some rivers to save the orcas and the fish. But the idea faces resistance from government agencies that manage the rivers.

A video of salmon traveling through a long tube went viral in 2019. You probably watched it. But the Whooshh cannon — its actual name — is a serious tool that conservationists are testing not only to help fish migrate, but to contain invasive carp that foul North American waterways.

A New and Improved Table could come in handy: As more superheavy elements are discovered, their behaviors could challenge the integrity of Mendeleev’s memorable chart.

That kind of curating acts as a fertile host for falsehoods by simultaneously engaging two predigital social-science standbys: the urban myth as “meme,” or viral idea; and individual biases, the automatic, subconscious presumptions that color belief.

“My experience is that once this stuff gets going, people just pass these stories on without even necessarily stopping to read them,” Mr. McKinney said.

“The networks make information run so fast that it outruns fact-checkers’ ability to check it.

When we feel so fundamentally disenfranchised, it’s comforting to concoct a fictional universe that systemically denies you the right cards. It gives you something to fight against and makes you self-deterministic. It provides an “us and them” narrative that allows you to conceive of yourself as a little David raging against a rather haughty, intellectual establishment Goliath. This is what worries me about journalists writing columns or tweets sneering at the supposed stupidity of the pseudoscientists and con spiracy theorists – it only serves to enforce this “us and them” worldview.