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CarynAI is the first major release from a company called Forever Voices. The company previously has created realistic AI chatbots that allow users to talk with replicated versions of Steve Jobs, Kanye West, Donald Trump and Taylor Swift

CarynAI is a far more sophisticated product, the company says, and part of Forever Voices’ new AI companion initiative, meant to provide users with a girlfriend-like experience that fans can emotionally bond with.

John Meyer, CEO and founder of Forever Voices, said that he created the company last year, after trying to use AI to develop ways to reconnect with his late father, who passed away in 2017. He built an AI voice chatbot that replicated his late father’s voice and personality to talk to and found the experience incredibly healing. “It was a remarkable experience to talk to him again in a super realistic way,” Meyer said. “I’ve been in tech my whole life, I’m a programmer, so it was easy for me to start building something like that especially as things got more advanced with the AI space.”

Meyer’s company has about 10 employees. One job Meyer is hoping to fill soon is chief ethics officer. “There are a lot of ways to do this wrong,”

One safeguard is trying to limit the amount of time a user is allowed to chat with CarynAI. To keep users from becoming addicted, CarynAI is programmed to wind down conversations after about an hour, encouraging users to pick back up later. But there is no hard time limit on use, and some users are spending hours speaking to CarynAI per day, according to Marjorie’s manager, Ishan Goel.

“I consider myself a futurist at heart and when I look into the future I believe this is the beginning of a very diverse future consisting of AI to human companionship,”

Elizabeth Snower, founder of ICONIQ, which creates conversational 3D avatars, predicts that soon there will be “AI influencers on every social platform that are influencing consumer decisions.”

“A lot of people have just been kind of really mad at the existence of this. They think that it’s the end of humanity,” she said.

Marjorie hopes the backlash will fade when other online personalities begin rolling out their own AI companions

“I think in the next five years, most Americans will have an AI companion in their pocket in some way, shape or form, whether it’s an ultra flirty AI that you’re dating, an AI that’s your personal trainer, or simply a tutor companion. Those are all things that we are building internally,

That strikes AI adviser and investor Allie K. Miller as a likely outcome. “I can imagine a future in which everyone — celebrities, TV characters, influencers, your brother — has an online avatar that they invite their audience or friends to engage with. … With the accessibility of these models, I’m not surprised it’s expanding to scaled interpersonal relationships.”

“The struggle to defend objective truth against people who consciously choose to deny or distort it has become a fight to defend our society, and democracy itself.”

Jonathan Swift, it’s worth noting that he was not an optimist about “truth.”

By the time a lie is refuted, he wrote, “it is too late; the jest is over, and the tale has had its effect: like a man, who has thought of a good repartee, when the discourse is changed, or the company parted; or like a physician, who has found out an infallible medicine, after the patient is dead.'“

“Considering that natural disposition in many men to lie, and in multitudes to believe,” he wrote in 1710, “I have been perplexed what to do with that maxim so frequent in every body's mouth; that truth will at last prevail.

A recent Washington Post tally found that nearly 300 Republicans running for congressional and state offices are election deniers. That means, as a FiveThirtyEight analysis found, 60 percent of Americans will have at least one election denier on their ballot next week.

In a new USA Today/Suffolk University poll, 63 percent of Republicans say they worry “the election results could be manipulated.”

From the New York Times: When asked, six Trump-backed Republican nominees for governor and the Senate in midterm battlegrounds would not commit to accepting this year’s election results.

The big mistake people have made is in assuming this could blow up only in an extensive struggle in 2024 and perhaps involving Donald Trump. What seems entirely unanticipated, yet is extremely predictable, is that smaller skirmishes could break out all over the country this year.

Democrats have got themselves in a situation where the head of their party holds the most popular position on guns and crime—and yet they’re getting crushed on the issue because they’ve let GOP campaign ads, the right wing media ecosystem, and assorted progressive big city prosecutors shape the narrative on the issue rather than doing so themselves.

common response to this situation is to argue that, even if individual scientists might fool themselves, others have no hesitation in critiquing their ideas or their results, and so it all comes out in the wash: Science as a communal activity is self-correcting. Sometimes this is true—but it doesn’t necessarily happen as quickly or smoothly as we might like to believe.

The idea, says Nosek, is that researchers “write down in advance what their study is for and what they think will happen.” Then when they do their experiments, they agree to be bound to analyzing the results strictly within the confines of that original plan

He is convinced that the process and progress of science would be smoothed by bringing these biases to light—which means making research more transparent in its methods, assumptions, and interpretations

Surprisingly, Nosek thinks that one of the most effective solutions to cognitive bias in science could come from the discipline that has weathered some of the heaviest criticism recently for its error-prone and self-deluding ways: pharmacology.

Psychologist Brian Nosek of the University of Virginia says that the most common and problematic bias in science is “motivated reasoning”: We interpret observations to fit a particular idea.

Sometimes it seems surprising that science functions at all.

Whereas the falsification model of the scientific method championed by philosopher Karl Popper posits that the scientist looks for ways to test and falsify her theories—to ask “How am I wrong?”—Nosek says that scientists usually ask instead “How am I right?” (or equally, to ask “How are you wrong?”).

Statistics may seem to offer respite from bias through strength in numbers, but they are just as fraught.

Given that science has uncovered a dizzying variety of cognitive biases, the relative neglect of their consequences within science itself is peculiar. “I was aware of biases in humans at large,” says Hartgerink, “but when I first ‘learned’ that they also apply to scientists, I was somewhat amazed, even though it is so obvious.”

Nosek thinks that peer review might sometimes actively hinder clear and swift testing of scientific claims.

While youth colors everything anew, as we age, we become more or less familiar with every aspect of our surroundings, and so the nuance wears off.

Young people tended to select steady or unchanging metaphors for time, while older people chose those which had to do with swiftness and speed.

This creates a “reminiscence bump.” The farther we move away from the bump, the quicker time seems to move.

A neurochemical explanation lies in the dopamine level in our brains. This is the pleasure neurotransmitter that gives us a feeling of wellbeing and reward.

Over a 13-week period, our study allowed us to capture people’s assessments of fresh news articles in real time. Each day of the study, we relied on a fixed, pre-registered process to select five popular articles published within the previous 24 hours

The five articles were balanced between conservative, liberal and non-partisan sources, as well as from mainstream news websites and from websites known to produce fake news. In total, we sent 150 total articles to 90 survey respondents each

We also sent these articles separately to six independent fact checkers, and treated their most common response — true, false/misleading, or cannot determine — for each article as the “correct’’ answer for that article.

When shown an article that was rated “true” by the professional fact checkers, respondents correctly identified the article as true 62 percent of the time. When the source of the true news story was a mainstream news source, respondents correctly identified the article as true 73 percent of the time.

However, for each article the professional fact checkers rated “false/misleading,” the study participants were as likely to say it was true as they were to say it was false or misleading. And roughly one-third of the time they told us they were unable to determine the veracity of the article. In other words, people on the whole were unable to correctly classify false or misleading news.

four of the articles in our study that fact checkers rated as false or misleading were related to the coronavirus.

All four articles promoted the unfounded rumor that the virus was intentionally developed in a laboratory. Although accidental releases of pathogens from labs have previously caused significant morbidity and mortality, in the current pandemic multiple pieces of evidence suggest this virus is of natural origin. There’s little evidence that the virus was manufactured or altered.

Only 30 percent of participants correctly classified them as false or misleading.

respondents seemed to have more trouble deciding what to think about false covid-19 stories, leading to a higher proportion of “could not determine” responses than we saw for the stories on other topics our professional fact checkers rated as “false/misleading.” This finding suggests that it may be particularly difficult to identify misinformation in newly emerging topics

Study participants with higher levels of education did better on identifying both fake news overall and coronavirus-related fake news — but were far from being able to correctly weed out misinformation all of the time

In fact, no group, regardless of education level, was able to correctly identify the stories that the professional fact checkers had labeled as false or misleading more than 40 percent of the time.

Taken together, our findings suggest that there is widespread potential for vulnerability to misinformation when it first appears online. This is especially worrying during the current pandemic

In the current environment, misinformation has the potential to undermine social distancing efforts, to lead people to hoard supplies, or to promote the adoption of potentially dangerous fake cures.

our findings suggest that non-trivial numbers of people will believe false information to be true when they first encounter it. And it suggests that efforts to remove coronavirus-related misinformation will need to be swift — and implemented early in an article’s life-cycle — to stop the spread of something else that’s dangerous: misinformation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“Structural racism is an unfortunate term,” said Dr. Livingston, who is white. “Personally, I think taking racism out of the conversation will help

Many people like myself are offended by the implication that we are somehow racist.”

The podcast was promoted with a tweet from the journal that said, “No physician is racist, so how can there be structural racism in health care?”

The response to both was swift and angry, prompting the journal to take down the podcast and delete the tweet.

Comments made in the podcast were inaccurate, offensive, hurtful, and inconsistent with the standards of JAMA,

The A.M.A.’s email to employees promised that the investigation would probe “

Dr. Livingston later resigned.

“It’s not just that this podcast is problematic — it’s that there is a long and documented history of institutional racism at JAMA,”

“That podcast should never have happened,”

The fact that podcast was conceived of, recorded and posted was unconscionable.”

“I think it caused an incalculable amount of pain and trauma to Black physicians and patients,” she said. “And I think it’s going to take a long time for the journal to heal that pain.

“staff and leadership are overwhelmingly white and economically privileged,” and he committed to reviewing its editorial process.

“We are instituting changes that will address and prevent such failures from happening again.”

how the podcast and associated tweet were developed, reviewed, and ultimately published,” and said that the association had engaged independent investigators to ensure objectivity.

The email did not offer a date for conclusion of the investigation.

President Joe Biden is expected to sign the $1.9 trillion coronavirus relief package Thursday afternoon. The plan will send direct payments of up to $1,400 to most Americans, and will also put nearly $20 billion into Covid-19 vaccinations and $350 billion into state, local and tribal relief.

The economic reopening, coupled with additional fiscal stimulus, accelerated the rotation into more cyclical sectors, such as energy. The S&P 500 energy sector has been the biggest winner this year, up 40% so far.

Ten Republicans, including the House's No. 3 Republican, Liz Cheney of Wyoming, joined all Democrats to impeach Trump for "incitement of insurrection."

"We know that the President of the United States incited this insurrection, this armed rebellion against our common country,"

"There has never been a greater betrayal by a President of the United States of his office and his oath to the Constitution," Cheney said.

House Minority Leader Kevin McCarthy said Wednesday that Trump "bears responsibility for Wednesday's attack on Congress by mob rioters,"

In the Senate, McConnell is not planning to bring the Senate back for a trial before January 19, meaning the trial won't begin until Trump is out of office and Biden has been sworn in.

Pence sent a letter Tuesday saying he would not seek to invoke the 25th Amendment as Democrats had urged, and Trump is not considering resigning.

After the House vote, Trump released a video statement calling for calm as the threat of new riots -- which Trump said he'd been briefed on by the Secret Servic

. In the 17th century, even Galileo writes about why, in a game with three dice, the number 10 should come up more than the number 9. It’s an observation that one would only be able to detect after thousands of rolls, but the reason behind it involves how many combinations of numbers can add up to each different option.

All these changes in dice come about, says Eerkens, “as different astronomers are coming up with new ideas about the world, and mathematicians are starting to understand numbers and probability.” Which came first: Did people begin to intuitively understand what true chance felt like, and adjusted dice accordingly, or did it trickle out from what would eventually become known as the scientific community?

And it does make you wonder: Would you know that fair dice should fall equally on each side, or that in a three-dice game, it’s better to bet on 10 than nine, if someone hadn’t told you?

A false story is much more likely to go viral than a real story, the authors find. A false story reaches 1,500 people six times quicker, on average, than a true story does.

“In short, I don’t think there’s any reason to doubt the study’s results,” said Rebekah Tromble, a professor of political science at Leiden University in the Netherlands, in an email.

It’s a question that can have life-or-death consequences.“[Fake news] has become a white-hot political and, really, cultural topic, but the trigger for us was personal events that hit Boston five years ago,” said Deb Roy, a media scientist at MIT and one of the authors of the new study.

Ultimately, they found about 126,000 tweets, which, together, had been retweeted more than 4.5 million times. Some linked to “fake” stories hosted on other websites. Some started rumors themselves, either in the text of a tweet or in an attached image. (The team used a special program that could search for words contained within static tweet images.) And some contained true information or linked to it elsewhere.

Tweet A and Tweet B both have the same size audience, but Tweet B has more “depth,” to use Vosoughi’s term. It chained together retweets, going viral in a way that Tweet A never did. “It could reach 1,000 retweets, but it has a very different shape,” he said.Here’s the thing: Fake news dominates according to both metrics. It consistently reaches a larger audience, and it tunnels much deeper into social networks than real news does. The authors found that accurate news wasn’t able to chain together more than 10 retweets. Fake news could put together a retweet chain 19 links long—and do it 10 times as fast as accurate news put together its measly 10 retweets.

What does this look like in real life? Take two examples from the last presidential election. In August 2015, a rumor circulated on social media that Donald Trump had let a sick child use his plane to get urgent medical care. Snopes confirmed almost all of the tale as true. But according to the team’s estimates, only about 1,300 people shared or retweeted the story.

Why does falsehood do so well? The MIT team settled on two hypotheses.First, fake news seems to be more “novel” than real news. Falsehoods are often notably different from the all the tweets that have appeared in a user’s timeline 60 days prior to their retweeting them, the team found.Second, fake news evokes much more emotion than the average tweet. The researchers created a database of the words that Twitter users used to reply to the 126,000 contested tweets, then analyzed it with a state-of-the-art sentiment-analysis tool. Fake tweets tended to elicit words associated with surprise and disgust, while accurate tweets summoned words associated with sadness and trust, they found.

It suggests—to me, at least, a Twitter user since 2007, and someone who got his start in journalism because of the social network—that social-media platforms do not encourage the kind of behavior that anchors a democratic government. On platforms where every user is at once a reader, a writer, and a publisher, falsehoods are too seductive not to succeed: The thrill of novelty is too alluring, the titillation of disgust too difficult to transcend. After a long and aggravating day, even the most staid user might find themselves lunging for the politically advantageous rumor. Amid an anxious election season, even the most public-minded user might subvert their higher interest to win an argument.

If you would like to improve your own lie detection, a good first step is to learn the common logical fallacies -- red herrings, appeals to ignorance, straw men and "ad populum" appeals to the bandwagon -- that purveyors of misinformation may use to create the illusion of truth.

Another project aimed to inoculate students at North Carolina State University in Raleigh, involved a course on misinformation throughout history. The class was taught about everything from the myth that aliens somehow built the Egyptian pyramids to the theories that NASA's moon landings were faked. Along the way, the students had to identify the erroneous logic that helped create the arguments, and the motivations that may lead some people to spread those ideas.

These efforts are often called "inoculations," since they use a real-life example in one domain to teach people about the strategies used to spread lies and therefore equipping people to spot them more easily. Educating people about the tobacco industry's attempts to question the medical consensus on smoking, for example, led people to be more skeptical of articles denying climate change, according to one study.

You could also try basic strategies such as cross-checking different outlets and finding the original source of a claim. You might also look at independent fact-checking websites used in the MIT study such as Snopes, PolitiFact and TruthOrFiction.com.

The psychological literature offers us one good strategy against bias, called the "consider the opposite" method. This involves asking yourself whether you would have been so credulous of a claim if its opinions had differed from your own. And if not, what kind of additional scrutiny might you have applied? This should help you to identify the weaknesses in your own thinking.

Falsehoods may fly, but with this lie detection kit, you can better ensure your actions and beliefs remain grounded in the truth.

Tourism can potentially offer a fairly swift peace dividend

Tourism and ecosystem services offer far more promising returns for the rural economy.

The U.S. Press Freedom Tracker said on Tuesday it has counted 211 "press freedom violations" since the start of the George Floyd protests last week, which in some cases have led to riots.

"In every case that we are aware of, there are strong indications that officers knew the journalist was a member of the press," the Reporters Committee for Freedom of the Press stated in a letter to Minnesota authorities on Tuesday.

This was the largest coalition to sign such a letter in the Reporters Committee's 50-year history. "We'll be sending similar letters to other jurisdictions around the country," a spokeswoman said.

Take swift action to discipline any officer who is found to have arrested or assaulted a journalist engaged in newsgathering."

"Inform your officers that they themselves could be subject to legal liability for violating these rights."

"Ensure that crowd control tactics are appropriate and proportional, and are designed to prevent collateral harm to journalists covering the protests.

"Continue to exempt members of the news media from mobility restrictions, including, and especially, curfews."

"Release all information about arrests of or physical interactions with the press to the public to allow it to evaluate the legitimacy of police conduct."

Did Federalists just use the specter of the Illuminati to tar their rivals? Or did they mean it? Did the Jeffersonians really think the Federalists were conspiring to bring back monarchy as they alleged? Or were they just trying to win elections? The answer depends on who and when, but it’s safe to say that some did believe these theories.

Conspiracy theory after theory, Americans cast a paranoid eye on their partisan opponents throughout the 19th and 20th centuries. According to the Oxford English Dictionary, the phrase “conspiracy theory” first appeared in the early 20th century United States, in the context of political histories of the 19th century.

Democrats’ anxieties about Russian conspiracies to interfere in the 2016 campaign cannot be extricated from this historical context of paranoia just because they have a significant basis in fact. As Joseph Heller wrote, “Just because you’re paranoid doesn’t mean they’re not after you.”

The republican political theory underlying the American paranoid style had its origin in the writings of opposition politicians in 18th-century Britain. Since then, conspiratorial thinking, has remained most attractive to opposition parties seeking to discredit their establishment rivals. This is the nature of Trump’s criticism of Democratic investigations of Russian conspiracies to hack the 2016 campaign. They’re just whining because they lost, Trump has said repeatedly.

If Trump’s embrace of the Ukraine conspiracy doesn’t sink his political future by leading to impeachment, it may nonetheless signal that his political future is bleak.

agents will open up many more learning opportunities.

Already, AI can help you pick out a new TV and recommend movies, books, shows, and podcasts. Likewise, a company I’ve invested in, recently launched Pix, which lets you ask questions (“Which Robert Redford movies would I like and where can I watch them?”) and then makes recommendations based on what you’ve liked in the past

Productivity

copilots can do a lot—such as turn a written document into a slide deck, answer questions about a spreadsheet using natural language, and summarize email threads while representing each person’s point of view.

before the sophisticated agents I’m describing become a reality, we need to confront a number of questions about the technology and how we’ll use it.

Helping patients and healthcare workers will be especially beneficial for people in poor countries, where many never get to see a doctor at all.

To create a new app or service, you won’t need to know how to write code or do graphic design. You’ll just tell your agent what you want. It will be able to write the code, design the look and feel of the app, create a logo, and publish the app to an online store

Agents will do even more. Having one will be like having a person dedicated to helping you with various tasks and doing them independently if you want. If you have an idea for a business, an agent will help you write up a business plan, create a presentation for it, and even generate images of what your product might look like

For decades, I’ve been excited about all the ways that software would make teachers’ jobs easier and help students learn. It won’t replace teachers, but it will supplement their work—personalizing the work for students and liberating teachers from paperwork and other tasks so they can spend more time on the most important parts of the job.

Mental health care is another example of a service that agents will make available to virtually everyone. Today, weekly therapy sessions seem like a luxury. But there is a lot of unmet need, and many people who could benefit from therapy don’t have access to it.

I don’t think any single company will dominate the agents business--there will be many different AI engines available.

The real shift will come when agents can help patients do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.

They’ll replace word processors, spreadsheets, and other productivity apps.

Education

For example, few families can pay for a tutor who works one-on-one with a student to supplement their classroom work. If agents can capture what makes a tutor effective, they’ll unlock this supplemental instruction for everyone who wants it. If a tutoring agent knows that a kid likes Minecraft and Taylor Swift, it will use Minecraft to teach them about calculating the volume and area of shapes, and Taylor’s lyrics to teach them about storytelling and rhyme schemes. The experience will be far richer—with graphics and sound, for example—and more personalized than today’s text-based tutors.

your agent will be able to help you in the same way that personal assistants support executives today. If your friend just had surgery, your agent will offer to send flowers and be able to order them for you. If you tell it you’d like to catch up with your old college roommate, it will work with their agent to find a time to get together, and just before you arrive, it will remind you that their oldest child just started college at the local university.

To see the dramatic change that agents will bring, let’s compare them to the AI tools available today. Most of these are bots. They’re limited to one app and generally only step in when you write a particular word or ask for help. Because they don’t remember how you use them from one time to the next, they don’t get better or learn any of your preferences.

The current state of the art is Khanmigo, a text-based bot created by Khan Academy. It can tutor students in math, science, and the humanities—for example, it can explain the quadratic formula and create math problems to practice on. It can also help teachers do things like write lesson plans.

Businesses that are separate today—search advertising, social networking with advertising, shopping, productivity software—will become one business.

other issues won’t be decided by companies and governments. For example, agents could affect how we interact with friends and family. Today, you can show someone that you care about them by remembering details about their life—say, their birthday. But when they know your agent likely reminded you about it and took care of sending flowers, will it be as meaningful for them?

In the computing industry, we talk about platforms—the technologies that apps and services are built on. Android, iOS, and Windows are all platforms. Agents will be the next platform.

A shock wave in the tech industry

Agents won’t simply make recommendations; they’ll help you act on them. If you want to buy a camera, you’ll have your agent read all the reviews for you, summarize them, make a recommendation, and place an order for it once you’ve made a decision.

Agents will affect how we use software as well as how it’s written. They’ll replace search sites because they’ll be better at finding information and summarizing it for you

they’ll be dramatically better. You’ll be able to have nuanced conversations with them. They will be much more personalized, and they won’t be limited to relatively simple tasks like writing a letter.

Companies will be able to make agents available for their employees to consult directly and be part of every meeting so they can answer questions.

AI agents that are well trained in mental health will make therapy much more affordable and easier to get. Wysa and Youper are two of the early chatbots here. But agents will go much deeper. If you choose to share enough information with a mental health agent, it will understand your life history and your relationships. It’ll be available when you need it, and it will never get impatient. It could even, with your permission, monitor your physical responses to therapy through your smart watch—like if your heart starts to race when you’re talking about a problem with your boss—and suggest when you should see a human therapist.

If the number of companies that have started working on AI just this year is any indication, there will be an exceptional amount of competition, which will make agents very inexpensive.

Agents are smarter. They’re proactive—capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.

Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons.

The most exciting impact of AI agents is the way they will democratize services that today are too expensive for most people

The ramifications for the software business and for society will be profound.

In the next five years, this will change completely. You won’t have to use different apps for different tasks. You’ll simply tell your device, in everyday language, what you want to do. And depending on how much information you choose to share with it, the software will be able to respond personally because it will have a rich understanding of your life. In the near future, anyone who’s online will be able to have a personal assistant powered by artificial intelligence that’s far beyond today’s technology.

You’ll also be able to get news and entertainment that’s been tailored to your interests. CurioAI, which creates a custom podcast on any subject you ask about, is a glimpse of what’s coming.

An agent will be able to help you with all your activities if you want it to. With permission to follow your online interactions and real-world locations, it will develop a powerful understanding of the people, places, and activities you engage in. It will get your personal and work relationships, hobbies, preferences, and schedule. You’ll choose how and when it steps in to help with something or ask you to make a decision.

even the best sites have an incomplete understanding of your work, personal life, interests, and relationships and a limited ability to use this information to do things for you. That’s the kind of thing that is only possible today with another human being, like a close friend or personal assistant.

In the distant future, agents may even force humans to face profound questions about purpose. Imagine that agents become so good that everyone can have a high quality of life without working nearly as much. In a future like that, what would people do with their time? Would anyone still want to get an education when an agent has all the answers? Can you have a safe and thriving society when most people have a lot of free time on their hands?

They’ll have an especially big influence in four areas: health care, education, productivity, and entertainment and shopping.

In every group chat, no matter the size, participants fall into informal roles. There is usually a leader—a person whose posting frequency drives the group or sets the agenda. Often, there are lurkers who rarely chime in

Larger group chats are not immune to the more toxic dynamics of social media, where competition for attention and herd behavior cause infighting, splintering, and back-channeling.

It’s enough to make one think, as the writer Max Read argued, that “venture-capitalist group chats are a threat to the global economy.” Now you might also say they are a threat to national security.

thanks to the private nature of the group chats, this information largely stayed out of the public eye. As Bloomberg reported, “By the time most people figured out that a bank run was a possibility … it was already well underway.”

The investor panic that led to the swift collapse of Silicon Valley Bank in March was effectively caused by runaway group-chat dynamics. “It wasn’t phone calls; it wasn’t social media,” a start-up founder told Bloomberg in March. “It was private chat rooms and message groups.

Unlike traditional social media or even forums and message boards, group chats are nearly impossible to monitor.

as our digital social lives start to splinter off from feeds and large audiences and into siloed areas, a different kind of unpredictability and chaos awaits. Where social networks create a context collapse—a process by which information meant for one group moves into unfamiliar networks and is interpreted by outsiders—group chats seem to be context amplifiers

group chats provide strong relationship dynamics, and create in-jokes and lore. For decades, researchers have warned of the polarizing effects of echo chambers across social networks; group chats realize this dynamic fully.

Weird things happen in echo chambers. Constant reinforcement of beliefs or ideas might lead to group polarization or radicalization. It may trigger irrational herd behavior such as, say, attempting to purchase a copy of the Constitution through a decentralized autonomous organization

Obsession with in-group dynamics might cause people to lose touch with the reality outside the walls of a particular community; the private-seeming nature of a closed group might also lull participants into a false sense of security, as it did with Teixiera.

the age of the group chat appears to be at least as unpredictable, swapping a very public form of volatility for a more siloed, incalculable version

Social-media platforms are “preying on your insecurities; they’re preying on your temptations,” Jenn explained to me in an interview that shifted our parasocial connection, at least for an hour, to a mere relationship. “And, you know, I do play a role in this.” Jenn makes money through aspirational advertising, after all—a familiar part of any influencer’s job.

She’s pro–parasocial relationships, she explains to the camera, but only if we remain aware that we’re in one. “This relationship does not replace existing friendships, existing relationships,” she emphasizes. “This is all supplementary. Like, it should be in addition to your life, not a replacement.” I sat there watching her talk about parasocial relationships while absorbing the irony of being in one with her.

The open acknowledgment of social media’s inner workings, with content creators exposing the foundations of their content within the content itself, is what Alice Marwick, an associate communications professor at the University of North Carolina at Chapel Hill, described to me as “meta-content.”

Meta-content can be overt, such as the vlogger Casey Neistat wondering, in a vlog, if vlogging your life prevents you from being fully present in it;

But meta-content can also be subtle: a vlogger walking across the frame before running back to get the camera. Or influencers vlogging themselves editing the very video you’re watching, in a moment of space-time distortion.

Viewers don’t seem to care. We keep watching, fully accepting the performance. Perhaps that’s because the rise of meta-content promises a way to grasp authenticity by acknowledging artifice; especially in a moment when artifice is easier to create than ever before, audiences want to know what’s “real” and what isn’

“The idea of a space where you can trust no sources, there’s no place to sort of land, everything is put into question, is a very unsettling, unsatisfying way to live.

So we continue to search for, as Murray observes, the “agreed-upon things, our basic understandings of what’s real, what’s true.” But when the content we watch becomes self-aware and even self-critical, it raises the question of whether we can truly escape the machinations of social media. Maybe when we stare directly into the abyss, we begin to enjoy its company.

“The difference between BeReal and the social-media giants isn’t the former’s relationship to truth but the size and scale of its deceptions.” BeReal users still angle their camera and wait to take their daily photo at an aesthetic time of day. The snapshots merely remind us how impossible it is to stop performing online.

Jenn’s concern over the future of the internet stems, in part, from motherhood. She recently had a son, Lennon (whose first birthday party I watched on YouTube), and worries about the digital world he’s going to inherit.

Back in the age of MySpace, she had her own internet friends and would sneak out to parking lots at 1 a.m. to meet them in real life: “I think this was when technology was really used as a tool to connect us.” Now, she explained, it’s beginning to ensnare us. Posting content online is no longer a means to an end so much as the end itself.

We used to view influencers’ lives as aspirational, a reality that we could reach toward. Now both sides acknowledge that they’re part of a perfect product that the viewer understands is unattainable and the influencer acknowledges is not fully real.

“I forgot to say this to her in the interview, but I truly think that my videos are less about me and more of a reflection of where you are currently … You are kind of reflecting on your own life and seeing what resonates [with] you, and you’re discarding what doesn’t. And I think that’s what’s beautiful about it.”

meta-content is fundamentally a compromise. Recognizing the delusion of the internet doesn’t alter our course within it so much as remind us how trapped we truly are—and how we wouldn’t have it any other way.