Seriously, What’s Making All These Mysterious Space Signals

In astronomy, the study of fast radio bursts can sometimes feel like a game of Clue. Astronomy can be, in some ways, a bit like the classic board game Clue. Scientists explore a sprawling but ultimately contained world, collecting pieces of information and testing out theories about a big mystery. You can’t cover every corner, but with the right combination of strategy and luck, you can gather enough clues to make a reasonable guess at the tidy answer—who, where, and how—enclosed in a little yellow envelope at the center of it all.

Only, instead of a fictional killer, astronomers are trying to track down the source of strange flashes of radio signals that reach Earth from the depths of space. Scientists have discovered hundreds of such flashes, known as fast radio bursts (FRBs), over the past 15 years. The signals are intense and fleeting things. They come from all directions in the night sky and sneak up on our telescopes. Most are one-offs, never to be seen again. A few “repeating FRBs” have shown up more than once.

Astronomers have gathered as much evidence as they can and have traced the approximate origins of FRBs in the enormous mansion that is our universe. Nearly all of them spring from distant galaxies, while just one so far arose from somewhere in our own Milky Way. But astronomers still haven’t figured out whodunit, or how; they don’t know for sure what kind of astrophysical objects produce these powerful emissions of radio waves. But astronomers have found a new, tantalizing clue.

A team of researchers has detected a new FRB from a galaxy several billion light-years from Earth, and this one is weirder than all the rest. Most bursts last for just a few milliseconds, pulsing with such intensity that they shine as brightly as galaxies before vanishing. But this emission of radio waves lasted about 1,000 times longer: three whole seconds. And there was something unusual about the signal itself.

Astronomers detected little pulses, peaking about every 0.2 seconds, within the three-second burst. Researchers had previously detected an FRB source that followed a discernible pattern, producing millisecond-long flickers for several days before quieting down and then starting back up again. But the flashes themselves were random. This was the first time that the signal itself exhibited such a precise rhythm.

“In FRB world, this is certainly big news,” Sarah Burke-Spolaor, an astronomer at West Virginia University who studies FRBs and was not involved in the new detection, told me. “The main question we are still after with FRB is: What is making them? A strict periodicity like this would be major.” The existence of such a pattern supports the growing evidence that suggests the culprit behind FRBs is a neutron star, the leftover core of a once-giant star that has burned through its fuel.

Professor Plum could be a pulsar, a type of neutron star that rotates fast and spits beams of radiation from its poles. Or Miss Scarlet could be a magnetar, another kind of neutron star, known for its powerful magnetic fields. “It is very difficult to contrive a natural clock like that, but pulsars are the only known emitting objects with enough momentum to behave that way,” Burke-Spolaor said.

The researchers behind the detection didn’t have enough to definitively pin the FRB on a pulsar, Shami Chatterjee, an astrophysicist at Cornell University and a co-author on the new research, told me. Nor do they have a good explanation for why this signal was so intense. Perhaps invisible gravitational forces magnified a pulsar’s emissions as they headed our way, making them appear brighter to radio telescopes. Or maybe a magnetar is undergoing a giant flare.

The latest detection bears some similarities to the radio emissions of pulsars and magnetars found in our own Milky Way galaxy, but the weird new signal seems, well, weirder. “The whole thing is just very peculiar,” Chatterjee said. Around this time, you might be thinking, Okay, so astronomers have their suspicions about what’s responsible for FRBs, but they haven’t solved the case. Add in the discovery of a surprisingly clear-cut pattern, and you might wonder: Could it be aliens?

Sorry, no. “Periodic signals are very, very common from normal astronomical sources,” Sofia Sheikh, an astronomer at the SETI Institute who works on the search for signs of advanced technology beyond Earth, told me. Such sources include pulsars and magnetars. “If the source was pulsing out the digits of pi or the Fibonacci sequence or something, then it would be a SETI story,” Sheikh said. If pulsars can indeed produce FRBs, astronomers can study these flashes to help them solve other cosmic mysteries.

Scientists have already used the rhythms of less mysterious pulsars in the Milky Way as a kind of astrophysical clock, allowing them to do such various tasks as measure the mass of Jupiter, study the properties of the space between stars, and even discover an exoplanet made of diamond, Burke-Spolaor said. In the case of the diamond planet, which also began with an unusual signal, the clues quickly added up:

When astronomers noticed some intriguing variation in the radio emissions of a pulsar 4,000 light-years away, they realized that the best explanation was the presence of a nearby planet. The planet, according to their analysis, was mostly made of carbon and oxygen, and dense enough to crystallize into a diamond world.

Astronomers hope they’ll stumble across more FRBs like this one in their search of our cosmic grounds. The Canadian telescope that detected this burst is constantly looking out for more, and future observatories may discover thousands of them every month. “Every step of the way with FRBs, every answer we have gotten comes with so many more questions,” Burke-Spolaor said. “This detection does the same.”

Astronomers have so far looked only for FRBs that last a few milliseconds because they didn’t think the flashes could last much longer, and it’s possible that “we could be missing a heap of FRBs that are seconds long,” Vikram Ravi, an astronomer at Caltech who wasn’t involved in the new research but who studies FRBs, told me. The story of FRBs is a long game, and scientists now know to expect sudden twists.

The secret envelope remains unopened, but astronomers still have plenty of cosmic rooms to search, and every turn promises to reveal a new clue.

By Marina Koren

Source: Seriously, What’s Making All These Mysterious Space Signals? – The Atlantic

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There Could Be 42,777 Intelligent Alien Civilizations In Our Galaxy Say Scientists

How many extraterrestrial intelligent civilizations are out there and when will one of them send us a message?

The answers, according to a paper published in The Astrophysical Journal are 42,777 and sometime in the next 2,000 years. It’s a decent explanation for the Fermi Paradox, which asks why we still haven’t received any messages from other civilizations despite there being a high probability of them existing.

It estimates the number of possible CETIs—communicating extraterrestrial intelligent civilizations—within our Milky Way galaxy. It also looks at how probable it is that one of them could contact us and when.There are, of course, some huge unknowns behind these seemingly very precise estimates that if known would make a massive difference to the results:

  • The probability of life appearing on rocky planets and eventually evolving into a civilization advanced enough to contact another.
  • At what stage of their host star’s evolution such advanced civilizations would be born.

So the figure of 42,777—which has an error rate of a few hundred each side—is on the optimistic side. It’s based on an estimate that only 0.1% of civilizations could become advanced enough to contact another. This is where the Great Filter comes in.

It also takes into account the idea that any civilization would need to survive for about three million years, give or take, to reach that point.

Even if a message is ever sent to us from an advanced civilization elsewhere in the Milky Way the question remains as to whether humans can survive long enough to receive it. The authors suggest that we will need to wait as little as 2,000 years to receive one alien signal.

That’s the optimistic calculations. The authors’ pessimistic estimates are for just 0.001% of civilizations–about 111—to become advanced enough to contact another. The upshot of that would be that humans would need to wait for 400,000 years to receive a message.

“The minimum value (0.001%) we take may also be overestimated,” write the authors, Wenjie Song and He Gao at Beijing Normal University’s Department of Astronomy. “If so, the number of CETIs would become even lower, and the opportunities for communication between CETIs would become extremely small.”

The only message ever received on Earth that could have come from an extraterrestrial intelligence is the Wow! Signal, which was received in 1977 at the Big Ear radio telescope, Ohio. It was heard for 72 seconds—the maximum possible at the time—and was never repeated.

The source of that signal remains unknown though a recent paper found only one Sun-like star (called 2MASS 19281982- 2640123) in a sample of 66 in the region of the night sky that Wow! came from. It’s 18,000 light-years away.

In 2012 a paper estimated that the closest civilization to the solar system could be 1,933 light years away. So let’s send a reply? Sure—and why not, considering the chances of an advanced alien civilization being malicious are really low—though there is one problem. Any radio or laser transmission will travel at the speed of light, so would take 1,800 years to get there.

The authors note that astronomers did send the “Arecibo message” to the Great Hercules Globular Cluster (M13) in 1974 using the now collapse Arecibo radio telescope. However, it wasn’t much good. “If there are indeed CETIs in M13, their detection ability needs to be 21 orders of magnitude higher than ours to detect our signal,” write the authors. “Conversely, if they transmit a similar signal, we need to improve the detection ability by 21 orders of magnitude to detect it.”

Space is big—really big—and even in-galaxy messaging is completely impractical. Even if we’re not alone it’s doubtful we’ll ever find out.But that doesn’t stop us looking for Earth-like exoplanets around 2MASS 19281982- 2640123.Wishing you clear skies and wide eyes.

Source: There Could Be 42,777 Intelligent Alien Civilizations In Our Galaxy Say Scientists

Critics

Here’s a good sign for alien hunters: More than 300 million worlds with similar conditions to Earth are scattered throughout the Milky Way galaxy. A new analysis concludes that roughly half of the galaxy’s sunlike stars host rocky worlds in habitable zones where liquid water could pool or flow over the planets’ surfaces.

“This is the science result we’ve all been waiting for,” says Natalie Batalha, an astronomer with the University of California, Santa Cruz, who worked on the new study.

The finding, which has been accepted for publication in the Astronomical Journal, pins down a crucial number in the Drake Equation. Devised by my father Frank Drake in 1961, the equation sets up a framework for calculating the number of detectable civilizations in the Milky Way. Now the first few variables in the formula—including the rate of sunlike star formation, the fraction of those stars with planets, and the number of habitable worlds per stellar system—are known.

The number of sunlike stars with worlds similar to Earth “could have been one in a thousand, or one in a million—nobody really knew,” says Seth Shostak, an astronomer at the Search for Extraterrestrial Intelligence (SETI) Institute who was not involved with the new study.

Astronomers estimated the number of these planets using data from NASA’s planet-hunting Kepler spacecraft. For nine years, Kepler stared at the stars and watched for the brief twinkles produced when orbiting planets blot out a portion of their star’s light. By the end of its mission in 2018, Kepler had spotted some 2,800 exoplanets—many of them nothing like the worlds orbiting our sun.

But Kepler’s primary goal was always to determine how common planets like Earth are. The calculation required help from the European Space Agency’s Gaia spacecraft, which monitors stars across the galaxy. With Gaia’s observations in hand, scientists were finally able to determine that the Milky Way is populated by hundreds of millions of Earth-size planets orbiting sunlike stars—and that the nearest one is probably within 20 light-years of the solar system…..

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Shortness of Breath Could Signal Heart Attack With Worst Survival Rate Study

Shortness of breath may be a sign of heart attack and lead to less survival than those with typical symptoms of chest pain, according to a study.

The researchers from Braga Hospital in Portugal, showed that just 76 per cent of heart attack patients with dyspnoea or fatigue as their main symptom are alive at one year compared to 94 per cent of those with chest pain as the predominant feature.

“Patients presenting with shortness of breath or fatigue had a worse prognosis than those with chest pain. They were less likely to be alive one year after their heart attack and also less likely to stay out of hospital for heart problems during that 12-month period,” said Dr. Paulo Medeiros from the Hospital.

“Dyspnoea and extreme tiredness were more common heart attack symptoms in women, older people and patients with other conditions such as high blood pressure, diabetes, kidney disease and lung disease,” Medeiros added.

Chest pain is the hallmark presentation of myocardial infarction but other complaints such as shortness of breath, upper abdominal or neck pain, or transient loss of consciousness (blackouts) may be the reason to attend the emergency department.

The study focused on non-ST-elevation myocardial infarction (NSTEMI), a type of heart attack in which an artery supplying blood to the heart becomes partially blocked.

The study included 4,726 patients aged 18 years and older admitted with NSTEMI between October 2010 and September 2019.

Patients were divided into three groups according to their main symptom at presentation. Chest pain was the most common presenting symptom (4,313 patients; 91 per cent), followed by dyspnoea/fatigue (332 patients; 7 per cent) and syncope (81 patients; 2 per cent). Syncope is a temporary loss of consciousness caused by a fall in blood pressure.

Patients with dyspnoea/fatigue were significantly older than those in the other two groups, with an average age of 75 years compared with 68 years in the chest pain group and 74 years in the syncope group.

Those with dyspnoea/fatigue were also more commonly women (42 per cent) compared to patients with chest pain as the main symptom (29 per cent women) or syncope (37 per cent women).

Compared to the other two groups, patients with dyspnoea/fatigue as their main symptom were more likely to also have high blood pressure, diabetes, chronic kidney disease and chronic obstructive pulmonary disease (COPD).

“This study highlights the need to consider a diagnosis of myocardial infarction even when the primary complaint is not chest pain. In addition to the classic heart attack symptom of chest pain, pressure, or heaviness radiating to one or both arms, the neck or jaw, people should seek urgent medical help if they experience prolonged shortness of breath,” Medeiros said.

Source: Shortness of breath could signal heart attack with worst survival rate Study

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How The Chip Shortage Could Help High-Tech Manufacturing In The US

Manufacturers haven’t overcome the worldwide semiconductor shortage. Gaming consoles like the PlayStation 5 are still scarce, automakers are delivering cars with missing features, and Apple may end up producing 10 million fewer iPhones in 2021. For a few companies, however, these supply chain woes may have an unexpected upside.

The manufacturing delays abroad and relentless demand for consumer electronics have turned into a windfall for some chipmakers in the United States. Even lesser-known American manufacturers with aging or secondhand equipment have seen a surge in sales for the legacy chips, or microcontrollers, they produce.

These parts are inexpensive to make but are a critical component for many devices, and as supply chain troubles have affected larger companies that focus on more advanced technologies, demand for the more basic chips has grown. Flush with customers, the companies that make these microcontrollers are now on a spending spree to boost their overall manufacturing capacity.

One Arizona-based semiconductor supplier, Microchip Technology, is investing in expensive new equipment and hiring more staff because its profits tripled last quarter, according to the New York Times. GlobalFoundries, a chipmaker based in Malta, New York, announced in July that it would build another chip plant nearby in a bid to double its production capabilities.

And just last month, a manufacturer in North Carolina announced its pivot to semiconductors and changed its name from Cree to Wolfspeed. The company is also in the process of building a new manufacturing facility in upstate New York. GM has already signed up as a strategic customer, another clear sign that the chip supply crunch is benefiting certain US sellers and opening up access to new customers.

Taken together, these developments point to a trend that industry leaders hope will become a renaissance for US chip manufacturing. Last May, Texas Instruments started construction on a $3.1 billion chip plant near its Dallas headquarters and may finalize plans for another facility soon.

Intel announced this past March that it would spend more than $20 billion to build two new chip manufacturing factories in Arizona, and the company says it could create more than 3,000 jobs. The world’s biggest chipmaker, Taiwan-based TSMC, has already started construction on a $12 billion plant in Arizona. Now, local economic leaders are wooing other companies that work with TSMC to start operations there, too.

“We just want to make sure that more of the manufacturing facilities that are being built in the future, that more of them are built here,” John Neuffer, the CEO of the Semiconductor Industry Association, told Recode. “It’s about making sure that, going forward, we have a better-balanced supply chain.”

The US government wants to capitalize on this momentum.  Joe Biden is eager to bolster the resilience of the country’s chip supply, which government officials believe is critical to national security. At the same time, politicians on both sides of the aisle are eager to boost high-tech manufacturing in the US, which has declined over the past several decades after many companies opted to build new factories abroad.

Whether a new wave of chip manufacturing could help the US expand its role as a global high-tech manufacturing center is unclear. Despite the Biden administration’s effort to address the chip shortage, chipmakers in the US and abroad have signaled that, without direct financial incentives, they will send their new manufacturing elsewhere in the future.

Even Idaho-based Micron Technology, the last major manufacturer of semiconductors for computer memory left in the US, has said that the future of its domestic production hinges on financial incentives. The company plans to spend more than $150 billion on chip research and development over the next decade, but has made it clear that it will build new plants abroad if it doesn’t receive proper support from the US government.

These companies want Congress to approve $52 billion in funding to boost incentives for domestic chip production and help companies buy more manufacturing equipment. These subsidies may be critical to preventing the US’s share of global chip manufacturing from declining even further. Right now, only 12 percent of the world’s chip production occurs in the US, a steep drop from the 37 percent share of chip manufacturing that took place in the US in 1990.

While Republican and Democratic leaders have said they’re eager to support high-tech manufacturing, chip industry leaders say the government has yet to provide the same financial incentives as other countries, including China and Japan, which are also beefing up their chip production.

Time is of the essence. Right now, companies are racing to build the facilities around the world that will manufacture chips for future technologies, including 5G devices and electric vehicles. Once these billion-dollar facilities start production, they’re unlikely to pick up and move.

Rebecca Heilweil

Source: How the chip shortage could help high-tech manufacturing in the US

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The Science of Mind Reading

One night in October, 2009, a young man lay in an fMRI scanner in Liège, Belgium. Five years earlier, he’d suffered a head trauma in a motorcycle accident, and since then he hadn’t spoken. He was said to be in a “vegetative state.” A neuroscientist named Martin Monti sat in the next room, along with a few other researchers. For years, Monti and his postdoctoral adviser, Adrian Owen, had been studying vegetative patients, and they had developed two controversial hypotheses.

First, they believed that someone could lose the ability to move or even blink while still being conscious; second, they thought that they had devised a method for communicating with such “locked-in” people by detecting their unspoken thoughts.

In a sense, their strategy was simple. Neurons use oxygen, which is carried through the bloodstream inside molecules of hemoglobin. Hemoglobin contains iron, and, by tracking the iron, the magnets in fMRI machines can build maps of brain activity. Picking out signs of consciousness amid the swirl seemed nearly impossible. But, through trial and error, Owen’s group had devised a clever protocol.

They’d discovered that if a person imagined walking around her house there was a spike of activity in her parahippocampal gyrus—a finger-shaped area buried deep in the temporal lobe. Imagining playing tennis, by contrast, activated the premotor cortex, which sits on a ridge near the skull. The activity was clear enough to be seen in real time with an fMRI machine. In a 2006 study published in the journal Science, the researchers reported that they had asked a locked-in person to think about tennis, and seen, on her brain scan, that she had done so.

With the young man, known as Patient 23, Monti and Owen were taking a further step: attempting to have a conversation. They would pose a question and tell him that he could signal “yes” by imagining playing tennis, or “no” by thinking about walking around his house. In the scanner control room, a monitor displayed a cross-section of Patient 23’s brain. As different areas consumed blood oxygen, they shimmered red, then bright orange. Monti knew where to look to spot the yes and the no signals.

He switched on the intercom and explained the system to Patient 23. Then he asked the first question: “Is your father’s name Alexander?” The man’s premotor cortex lit up. He was thinking about tennis—yes.

“Is your father’s name Thomas?”

Activity in the parahippocampal gyrus. He was imagining walking around his house—no.

“Do you have any brothers?”

Tennis—yes.

“Do you have any sisters?”

House—no.

“Before your injury, was your last vacation in the United States?”

Tennis—yes.

The answers were correct. Astonished, Monti called Owen, who was away at a conference. Owen thought that they should ask more questions. The group ran through some possibilities. “Do you like pizza?” was dismissed as being too imprecise. They decided to probe more deeply. Monti turned the intercom back on.

That winter, the results of the study were published in The New England Journal of Medicine. The paper caused a sensation. The Los Angeles Times wrote a story about it, with the headline “Brains of Vegetative Patients Show Life.” Owen eventually estimated that twenty per cent of patients who were presumed to be vegetative were actually awake. This was a discovery of enormous practical consequence: in subsequent years, through painstaking fMRI sessions, Owen’s group found many patients who could interact with loved ones and answer questions about their own care.

The conversations improved their odds of recovery. Still, from a purely scientific perspective, there was something unsatisfying about the method that Monti and Owen had developed with Patient 23. Although they had used the words “tennis” and “house” in communicating with him, they’d had no way of knowing for sure that he was thinking about those specific things. They had been able to say only that, in response to those prompts, thinking was happening in the associated brain areas. “Whether the person was imagining playing tennis, football, hockey, swimming—we don’t know,” Monti told me recently.

During the past few decades, the state of neuroscientific mind reading has advanced substantially. Cognitive psychologists armed with an fMRI machine can tell whether a person is having depressive thoughts; they can see which concepts a student has mastered by comparing his brain patterns with those of his teacher. By analyzing brain scans, a computer system can edit together crude reconstructions of movie clips you’ve watched. One research group has used similar technology to accurately describe the dreams of sleeping subjects.

In another lab, scientists have scanned the brains of people who are reading the J. D. Salinger short story “Pretty Mouth and Green My Eyes,” in which it is unclear until the end whether or not a character is having an affair. From brain scans alone, the researchers can tell which interpretation readers are leaning toward, and watch as they change their minds.

I first heard about these studies from Ken Norman, the fifty-year-old chair of the psychology department at Princeton University and an expert on thought decoding. Norman works at the Princeton Neuroscience Institute, which is housed in a glass structure, constructed in 2013, that spills over a low hill on the south side of campus. P.N.I. was conceived as a center where psychologists, neuroscientists, and computer scientists could blend their approaches to studying the mind; M.I.T. and Stanford have invested in similar cross-disciplinary institutes.

At P.N.I., undergraduates still participate in old-school psych experiments involving surveys and flash cards. But upstairs, in a lab that studies child development, toddlers wear tiny hats outfitted with infrared brain scanners, and in the basement the skulls of genetically engineered mice are sliced open, allowing individual neurons to be controlled with lasers. A server room with its own high-performance computing cluster analyzes the data generated from these experiments.

Norman, whose jovial intelligence and unruly beard give him the air of a high-school science teacher, occupies an office on the ground floor, with a view of a grassy field. The bookshelves behind his desk contain the intellectual DNA of the institute, with William James next to texts on machine learning. Norman explained that fMRI machines hadn’t advanced that much; instead, artificial intelligence had transformed how scientists read neural data.

This had helped shed light on an ancient philosophical mystery. For centuries, scientists had dreamed of locating thought inside the head but had run up against the vexing question of what it means for thoughts to exist in physical space. When Erasistratus, an ancient Greek anatomist, dissected the brain, he suspected that its many folds were the key to intelligence, but he could not say how thoughts were packed into the convoluted mass.

In the seventeenth century, Descartes suggested that mental life arose in the pineal gland, but he didn’t have a good theory of what might be found there. Our mental worlds contain everything from the taste of bad wine to the idea of bad taste. How can so many thoughts nestle within a few pounds of tissue?

Now, Norman explained, researchers had developed a mathematical way of understanding thoughts. Drawing on insights from machine learning, they conceived of thoughts as collections of points in a dense “meaning space.” They could see how these points were interrelated and encoded by neurons. By cracking the code, they were beginning to produce an inventory of the mind. “The space of possible thoughts that people can think is big—but it’s not infinitely big,” Norman said. A detailed map of the concepts in our minds might soon be within reach.

Norman invited me to watch an experiment in thought decoding. A postdoctoral student named Manoj Kumar led us into a locked basement lab at P.N.I., where a young woman was lying in the tube of an fMRI scanner. A screen mounted a few inches above her face played a slide show of stock images: an empty beach, a cave, a forest.

“We want to get the brain patterns that are associated with different subclasses of scenes,” Norman said.

As the woman watched the slide show, the scanner tracked patterns of activation among her neurons. These patterns would be analyzed in terms of “voxels”—areas of activation that are roughly a cubic millimetre in size. In some ways, the fMRI data was extremely coarse: each voxel represented the oxygen consumption of about a million neurons, and could be updated only every few seconds, significantly more slowly than neurons fire.

But, Norman said, “it turned out that that information was in the data we were collecting—we just weren’t being as smart as we possibly could about how we’d churn through that data.” The breakthrough came when researchers figured out how to track patterns playing out across tens of thousands of voxels at a time, as though each were a key on a piano, and thoughts were chords.

The origins of this approach, I learned, dated back nearly seventy years, to the work of a psychologist named Charles Osgood. When he was a kid, Osgood received a copy of Roget’s Thesaurus as a gift. Poring over the book, Osgood recalled, he formed a “vivid image of words as clusters of starlike points in an immense space.” In his postgraduate days, when his colleagues were debating how cognition could be shaped by culture, Osgood thought back on this image. He wondered if, using the idea of “semantic space,” it might be possible to map the differences among various styles of thinking.

Osgood conducted an experiment. He asked people to rate twenty concepts on fifty different scales. The concepts ranged widely: BOULDER, ME, TORNADO, MOTHER. So did the scales, which were defined by opposites: fair-unfair, hot-cold, fragrant-foul. Some ratings were difficult: is a TORNADO fragrant or foul? But the idea was that the method would reveal fine and even elusive shades of similarity and difference among concepts.

“Most English-speaking Americans feel that there is a difference, somehow, between ‘good’ and ‘nice’ but find it difficult to explain,” Osgood wrote. His surveys found that, at least for nineteen-fifties college students, the two concepts overlapped much of the time. They diverged for nouns that had a male or female slant. MOTHER might be rated nice but not good, and COP vice versa. Osgood concluded that “good” was “somewhat stronger, rougher, more angular, and larger” than “nice.”

Osgood became known not for the results of his surveys but for the method he invented to analyze them. He began by arranging his data in an imaginary space with fifty dimensions—one for fair-unfair, a second for hot-cold, a third for fragrant-foul, and so on. Any given concept, like TORNADO, had a rating on each dimension—and, therefore, was situated in what was known as high-dimensional space. Many concepts had similar locations on multiple axes: kind-cruel and honest-dishonest, for instance. Osgood combined these dimensions. Then he looked for new similarities, and combined dimensions again, in a process called “factor analysis.”

When you reduce a sauce, you meld and deepen the essential flavors. Osgood did something similar with factor analysis. Eventually, he was able to map all the concepts onto a space with just three dimensions. The first dimension was “evaluative”—a blend of scales like good-bad, beautiful-ugly, and kind-cruel. The second had to do with “potency”: it consolidated scales like large-small and strong-weak. The third measured how “active” or “passive” a concept was. Osgood could use these three key factors to locate any concept in an abstract space. Ideas with similar coördinates, he argued, were neighbors in meaning.

For decades, Osgood’s technique found modest use in a kind of personality test. Its true potential didn’t emerge until the nineteen-eighties, when researchers at Bell Labs were trying to solve what they called the “vocabulary problem.” People tend to employ lots of names for the same thing. This was an obstacle for computer users, who accessed programs by typing words on a command line. George Furnas, who worked in the organization’s human-computer-interaction group, described using the company’s internal phone book.

“You’re in your office, at Bell Labs, and someone has stolen your calculator,” he said. “You start putting in ‘police,’ or ‘support,’ or ‘theft,’ and it doesn’t give you what you want. Finally, you put in ‘security,’ and it gives you that. But it actually gives you two things: something about the Bell Savings and Security Plan, and also the thing you’re looking for.” Furnas’s group wanted to automate the finding of synonyms for commands and search terms.

They updated Osgood’s approach. Instead of surveying undergraduates, they used computers to analyze the words in about two thousand technical reports. The reports themselves—on topics ranging from graph theory to user-interface design—suggested the dimensions of the space; when multiple reports used similar groups of words, their dimensions could be combined.

In the end, the Bell Labs researchers made a space that was more complex than Osgood’s. It had a few hundred dimensions. Many of these dimensions described abstract or “latent” qualities that the words had in common—connections that wouldn’t be apparent to most English speakers. The researchers called their technique “latent semantic analysis,” or L.S.A.

At first, Bell Labs used L.S.A. to create a better internal search engine. Then, in 1997, Susan Dumais, one of Furnas’s colleagues, collaborated with a Bell Labs cognitive scientist, Thomas Landauer, to develop an A.I. system based on it. After processing Grolier’s American Academic Encyclopedia, a work intended for young students, the A.I. scored respectably on the multiple-choice Test of English as a Foreign Language. That year, the two researchers co-wrote a paper that addressed the question “How do people know as much as they do with as little information as they get?”

They suggested that our minds might use something like L.S.A., making sense of the world by reducing it to its most important differences and similarities, and employing this distilled knowledge to understand new things. Watching a Disney movie, for instance, I immediately identify a character as “the bad guy”: Scar, from “The Lion King,” and Jafar, from “Aladdin,” just seem close together. Perhaps my brain uses factor analysis to distill thousands of attributes—height, fashion sense, tone of voice—into a single point in an abstract space. The perception of bad-guy-ness becomes a matter of proximity.

In the following years, scientists applied L.S.A. to ever-larger data sets. In 2013, researchers at Google unleashed a descendant of it onto the text of the whole World Wide Web. Google’s algorithm turned each word into a “vector,” or point, in high-dimensional space. The vectors generated by the researchers’ program, word2vec, are eerily accurate: if you take the vector for “king” and subtract the vector for “man,” then add the vector for “woman,” the closest nearby vector is “queen.”

Word vectors became the basis of a much improved Google Translate, and enabled the auto-completion of sentences in Gmail. Other companies, including Apple and Amazon, built similar systems. Eventually, researchers realized that the “vectorization” made popular by L.S.A. and word2vec could be used to map all sorts of things. Today’s facial-recognition systems have dimensions that represent the length of the nose and the curl of the lips, and faces are described using a string of coördinates in “face space.” Chess A.I.s use a similar trick to “vectorize” positions on the board.

The technique has become so central to the field of artificial intelligence that, in 2017, a new, hundred-and-thirty-five-million-dollar A.I. research center in Toronto was named the Vector Institute. Matthew Botvinick, a professor at Princeton whose lab was across the hall from Norman’s, and who is now the head of neuroscience at DeepMind, Alphabet’s A.I. subsidiary, told me that distilling relevant similarities and differences into vectors was “the secret sauce underlying all of these A.I. advances.”

In 2001, a scientist named Jim Haxby brought machine learning to brain imaging: he realized that voxels of neural activity could serve as dimensions in a kind of thought space. Haxby went on to work at Princeton, where he collaborated with Norman. The two scientists, together with other researchers, concluded that just a few hundred dimensions were sufficient to capture the shades of similarity and difference in most fMRI data. At the Princeton lab, the young woman watched the slide show in the scanner.

With each new image—beach, cave, forest—her neurons fired in a new pattern. These patterns would be recorded as voxels, then processed by software and transformed into vectors. The images had been chosen because their vectors would end up far apart from one another: they were good landmarks for making a map. Watching the images, my mind was taking a trip through thought space, too.

The larger goal of thought decoding is to understand how our brains mirror the world. To this end, researchers have sought to watch as the same experiences affect many people’s minds simultaneously. Norman told me that his Princeton colleague Uri Hasson has found movies especially useful in this regard. They “pull people’s brains through thought space in synch,” Norman said. “What makes Alfred Hitchcock the master of suspense is that all the people who are watching the movie are having their brains yanked in unison. It’s like mind control in the literal sense.”

One afternoon, I sat in on Norman’s undergraduate class “fMRI Decoding: Reading Minds Using Brain Scans.” As students filed into the auditorium, setting their laptops and water bottles on tables, Norman entered wearing tortoiseshell glasses and earphones, his hair dishevelled.

He had the class watch a clip from “Seinfeld” in which George, Susan (an N.B.C. executive he is courting), and Kramer are hanging out with Jerry in his apartment. The phone rings, and Jerry answers: it’s a telemarketer. Jerry hangs up, to cheers from the studio audience.

“Where was the event boundary in the clip?” Norman asked. The students yelled out in chorus, “When the phone rang!” Psychologists have long known that our minds divide experiences into segments; in this case, it was the phone call that caused the division.

Norman showed the class a series of slides. One described a 2017 study by Christopher Baldassano, one of his postdocs, in which people watched an episode of the BBC show “Sherlock” while in an fMRI scanner. Baldassano’s guess going into the study was that some voxel patterns would be in constant flux as the video streamed—for instance, the ones involved in color processing. Others would be more stable, such as those representing a character in the show.

The study confirmed these predictions. But Baldassano also found groups of voxels that held a stable pattern throughout each scene, then switched when it was over. He concluded that these constituted the scenes’ voxel “signatures.” Norman described another study, by Asieh Zadbood, in which subjects were asked to narrate “Sherlock” scenes—which they had watched earlier—aloud.

The audio was played to a second group, who’d never seen the show. It turned out that no matter whether someone watched a scene, described it, or heard about it, the same voxel patterns recurred. The scenes existed independently of the show, as concepts in people’s minds.

Through decades of experimental work, Norman told me later, psychologists have established the importance of scripts and scenes to our intelligence. Walking into a room, you might forget why you came in; this happens, researchers say, because passing through the doorway brings one mental scene to a close and opens another.

Conversely, while navigating a new airport, a “getting to the plane” script knits different scenes together: first the ticket counter, then the security line, then the gate, then the aisle, then your seat. And yet, until recently, it wasn’t clear what you’d find if you went looking for “scripts” and “scenes” in the brain.

In a recent P.N.I. study, Norman said, people in an fMRI scanner watched various movie clips of characters in airports. No matter the particulars of each clip, the subjects’ brains all shimmered through the same series of events, in keeping with boundary-defining moments that any of us would recognize. The scripts and the scenes were real—it was possible to detect them with a machine. What most interests Norman now is how they are learned in the first place.

How do we identify the scenes in a story? When we enter a strange airport, how do we know intuitively where to look for the security line? The extraordinary difficulty of such feats is obscured by how easy they feel—it’s rare to be confused about how to make sense of the world. But at some point everything was new. When I was a toddler, my parents must have taken me to the supermarket for the first time; the fact that, today, all supermarkets are somehow familiar dims the strangeness of that experience.

When I was learning to drive, it was overwhelming: each intersection and lane change seemed chaotic in its own way. Now I hardly have to think about them. My mind instantly factors out all but the important differences.

Norman clicked through the last of his slides. Afterward, a few students wandered over to the lectern, hoping for an audience with him. For the rest of us, the scene was over. We packed up, climbed the stairs, and walked into the afternoon sun.

Like Monti and Owen with Patient 23, today’s thought-decoding researchers mostly look for specific thoughts that have been defined in advance. But a “general-purpose thought decoder,” Norman told me, is the next logical step for the research. Such a device could speak aloud a person’s thoughts, even if those thoughts have never been observed in an fMRI machine. In 2018, Botvinick, Norman’s hall mate, co-wrote a paper in the journal Nature Communications titled “Toward a Universal Decoder of Linguistic Meaning from Brain Activation.”

Botvinick’s team had built a primitive form of what Norman described: a system that could decode novel sentences that subjects read silently to themselves. The system learned which brain patterns were evoked by certain words, and used that knowledge to guess which words were implied by the new patterns it encountered.

The work at Princeton was funded by iARPA, an R. & D. organization that’s run by the Office of the Director of National Intelligence. Brandon Minnery, the iARPA project manager for the Knowledge Representation in Neural Systems program at the time, told me that he had some applications in mind. If you knew how knowledge was represented in the brain, you might be able to distinguish between novice and expert intelligence agents. You might learn how to teach languages more effectively by seeing how closely a student’s mental representation of a word matches that of a native speaker.

Minnery’s most fanciful idea—“Never an official focus of the program,” he said—was to change how databases are indexed. Instead of labelling items by hand, you could show an item to someone sitting in an fMRI scanner—the person’s brain state could be the label. Later, to query the database, someone else could sit in the scanner and simply think of whatever she wanted. The software could compare the searcher’s brain state with the indexer’s. It would be the ultimate solution to the vocabulary problem.

Jack Gallant, a professor at Berkeley who has used thought decoding to reconstruct video montages from brain scans—as you watch a video in the scanner, the system pulls up frames from similar YouTube clips, based only on your voxel patterns—suggested that one group of people interested in decoding were Silicon Valley investors. “A future technology would be a portable hat—like a thinking hat,” he said.

He imagined a company paying people thirty thousand dollars a year to wear the thinking hat, along with video-recording eyeglasses and other sensors, allowing the system to record everything they see, hear, and think, ultimately creating an exhaustive inventory of the mind. Wearing the thinking hat, you could ask your computer a question just by imagining the words. Instantaneous translation might be possible. In theory, a pair of wearers could skip language altogether, conversing directly, mind to mind. Perhaps we could even communicate across species.

Among the challenges the designers of such a system would face, of course, is the fact that today’s fMRI machines can weigh more than twenty thousand pounds. There are efforts under way to make powerful miniature imaging devices, using lasers, ultrasound, or even microwaves. “It’s going to require some sort of punctuated-equilibrium technology revolution,” Gallant said. Still, the conceptual foundation, which goes back to the nineteen-fifties, has been laid.

Recently, I asked Owen what the new thought-decoding technology meant for locked-in patients. Were they close to having fluent conversations using something like the general-purpose thought decoder? “Most of that stuff is group studies in healthy participants,” Owen told me. “The really tricky problem is doing it in a single person. Can you get robust enough data?” Their bare-bones protocol—thinking about tennis equals yes; thinking about walking around the house equals no—relied on straightforward signals that were statistically robust.

It turns out that the same protocol, combined with a series of yes-or-no questions (“Is the pain in the lower half of your body? On the left side?”), still works best. “Even if you could do it, it would take longer to decode them saying ‘it is in my right foot’ than to go through a simple series of yes-or-no questions,” Owen said. “For the most part, I’m quietly sitting and waiting. I have no doubt that, some point down the line, we will be able to read minds. People will be able to articulate, ‘My name is Adrian, and I’m British,’ and we’ll be able to decode that from their brain. I don’t think it’s going to happen in probably less than twenty years.”

In some ways, the story of thought decoding is reminiscent of the history of our understanding of the gene. For about a hundred years after the publication of Charles Darwin’s “On the Origin of Species,” in 1859, the gene was an abstraction, understood only as something through which traits passed from parent to child. As late as the nineteen-fifties, biologists were still asking what, exactly, a gene was made of. When James Watson and Francis Crick finally found the double helix, in 1953, it became clear how genes took physical form. Fifty years later, we could sequence the human genome; today, we can edit it.

Thoughts have been an abstraction for far longer. But now we know what they really are: patterns of neural activation that correspond to points in meaning space. The mind—the only truly private place—has become inspectable from the outside. In the future, a therapist, wanting to understand how your relationships run awry, might examine the dimensions of the patterns your brain falls into.

Some epileptic patients about to undergo surgery have intracranial probes put into their brains; researchers can now use these probes to help steer the patients’ neural patterns away from those associated with depression. With more fine-grained control, a mind could be driven wherever one liked. (The imagination reels at the possibilities, for both good and ill.) Of course, we already do this by thinking, reading, watching, talking—actions that, after I’d learned about thought decoding, struck me as oddly concrete. I could picture the patterns of my thoughts flickering inside my mind. Versions of them are now flickering in yours.

On one of my last visits to Princeton, Norman and I had lunch at a Japanese restaurant called Ajiten. We sat at a counter and went through the familiar script. The menus arrived; we looked them over. Norman noticed a dish he hadn’t seen before—“a new point in ramen space,” he said. Any minute now, a waiter was going to interrupt politely to ask if we were ready to order.

“You have to carve the world at its joints, and figure out: what are the situations that exist, and how do these situations work?” Norman said, while jazz played in the background. “And that’s a very complicated problem. It’s not like you’re instructed that the world has fifteen different ways of being, and here they are!” He laughed. “When you’re out in the world, you have to try to infer what situation you’re in.” We were in the lunch-at-a-Japanese-restaurant situation. I had never been to this particular restaurant, but nothing about it surprised me. This, it turns out, might be one of the highest accomplishments in nature.

Norman told me that a former student of his, Sam Gershman, likes using the terms “lumping” and “splitting” to describe how the mind’s meaning space evolves. When you encounter a new stimulus, do you lump it with a concept that’s familiar, or do you split off a new concept? When navigating a new airport, we lump its metal detector with those we’ve seen before, even if this one is a different model, color, and size. By contrast, the first time we raised our hands inside a millimetre-wave scanner—the device that has replaced the walk-through metal detector—we split off a new category.

Norman turned to how thought decoding fit into the larger story of the study of the mind. “I think we’re at a point in cognitive neuroscience where we understand a lot of the pieces of the puzzle,” he said. The cerebral cortex—a crumply sheet laid atop the rest of the brain—warps and compresses experience, emphasizing what’s important. It’s in constant communication with other brain areas, including the hippocampus, a seahorse-shaped structure in the inner part of the temporal lobe.

For years, the hippocampus was known only as the seat of memory; patients who’d had theirs removed lived in a perpetual present. Now we were seeing that the hippocampus stores summaries provided to it by the cortex: the sauce after it’s been reduced. We cope with reality by building a vast library of experience—but experience that has been distilled along the dimensions that matter. Norman’s research group has used fMRI technology to find voxel patterns in the cortex that are reflected in the hippocampus. Perhaps the brain is like a hiker comparing the map with the territory.

In the past few years, Norman told me, artificial neural networks that included basic models of both brain regions had proved surprisingly powerful. There was a feedback loop between the study of A.I. and the study of the real human mind, and it was getting faster. Theories about human memory were informing new designs for A.I. systems, and those systems, in turn, were suggesting ideas about what to look for in real human brains. “It’s kind of amazing to have gotten to this point,” he said.

On the walk back to campus, Norman pointed out the Princeton University Art Museum. It was a treasure, he told me.

“What’s in there?” I asked.

“Great art!” he said

After we parted ways, I returned to the museum. I went to the downstairs gallery, which contains artifacts from the ancient world. Nothing in particular grabbed me until I saw a West African hunter’s tunic. It was made of cotton dyed the color of dark leather. There were teeth hanging from it, and claws, and a turtle shell—talismans from past kills. It struck me, and I lingered for a moment before moving on.

Six months later, I went with some friends to a small house in upstate New York. On the wall, out of the corner of my eye, I noticed what looked like a blanket—a kind of fringed, hanging decoration made of wool and feathers. It had an odd shape; it seemed to pull toward something I’d seen before. I stared at it blankly. Then came a moment of recognition, along dimensions I couldn’t articulate—more active than passive, partway between alive and dead. There, the chest. There, the shoulders. The blanket and the tunic were distinct in every way, but somehow still neighbors. My mind had split, then lumped. Some voxels had shimmered. In the vast meaning space inside my head, a tiny piece of the world was finding its proper place. ♦

Source: The Science of Mind Reading | The New Yorker

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