2026-07-06 22:00:00
Similar concepts in different languages share an address in the brain.
My octogenarian father-in-law is trilingual and a lifelong fan of the World Cup. As he cheers on his favorite teams in English, Spanish, or French—sometimes switching between them mid-sentence—I’m always amazed at how easy it seems.
Scientists have long been fascinated by the brain’s ability to learn and retain multiple languages. Even after years of disuse, a brief exposure can quickly revive a language without having to consciously relearn its grammar or vocabulary. Bilingualism may offer other cognitive perks. Small studies suggest it delays brain aging, lowers dementia risk, and provides a slight edge in executive function (the ability to stay focused on a goal).
But most of the evidence is from brain imaging studies that offer only a bird’s-eye view of neural activity and miss the finer details.
Now, scientists from the Baylor College of Medicine and collaborators have recorded activity from single neurons in four bilingual volunteers with epilepsy as they listened, read, and spoke in English and Spanish. The participants already had electrodes implanted in the hippocampus—a brain region critical for learning and memory—to track the source of their seizures.
“This is the very first study to look at how bilingual brains work at the level of individual neurons, and to do so in real time,” said study author Xinyuan Yan in a press release.
The results suggest the bilingual brain operates on two levels. Individual neurons often showed a strong preference for one language when participants heard or spoke words with the same meaning. But networks of neurons were largely language independent. They spontaneously organized into a concept map, placing words with related meanings—such as “dog” and “wolf”—closer together than unrelated words like “fork.”
Surprisingly, both languages relied on the same underlying map. Using the English concept map alone, the team could accurately predict clusters of related Spanish words.
“It’s like looking into a room from a different window. Everything inside is the same, but the perspective is different,” said study author Sameer Sheth.
Language is central to human connection. Although some words don’t directly translate, people can express the same ideas across multiple languages without losing their core meaning.
Children raised in multilingual households are especially adept at switching between languages, often blending words and phrases together. Even when languages differ dramatically in grammar, syntax, and pronunciation, the brain somehow keeps their structures distinct while fluidly merging their meanings.
Long before we learn to speak, neural networks transform thoughts into electrical patterns that form words and sentences. Because languages are built differently—for example, where a verb falls in a sentence—it seems reasonable that each language would have a unique neural fingerprint.
But that might not be the case. A recent AI-powered analysis of functional MRI (fMRI) scans from monolingual speakers of 21 languages suggested that languages share a similar neural scaffold that represents meaning and concepts. Even fictional languages, including Klingon from Star Trek and Na’vi from Avatar, appear to tap into the same underlying system.
A growing body of evidence from bilingual speakers echoes these findings. One fMRI study found native Chinese speakers learned English more efficiently when they recruited brain networks used for Chinese. Another study identified shared speech-related brain activity sufficient for decoding words across languages.
Despite hinting at a universal language map, these standard imaging technologies struggle to capture detailed patterns as people switch languages in real time. To see how bilingual brains actually pull off the feat, we need to listen in on single cells.
The team studied four volunteers fluent in English and Spanish. All had learned the languages before age five and continued to use them regularly. Each also had electrodes implanted in the hippocampus to monitor seizures as part of epilepsy treatment, allowing researchers to track individual neuron activity as they listened and spoke.
Though often overlooked in language research, the hippocampus is increasingly recognized as a hub for word meaning, and it may also link concepts together. Here, the team monitored more than 100 neurons in each participant as they completed three language tasks.
First, the participants listened to roughly an hour of YouTube videos and the audiobook Eat Pray Love (Come Reza Ama). Next, they read aloud nearly 100 phrases displayed on a screen, such as “let’s have fun” and its Spanish equivalent “vamos a divertirnos.” Finally, they spent up to 90 minutes chatting with native speakers of each language, discussing everything from family to their epilepsy journey.
By the end, the team had compiled thousands of spoken words, hundreds of matched phrases, and hours of natural conversation.
Only a handful of neurons appeared truly bilingual, responding similarly to equivalent words such as “friends” and “amigos.” To better interpret the neural activity, the team turned to mBERT, Google’s multilingual language model that understands more than 100 languages. Like other LLMs, the model represents words according to their relationships and context rather than simple dictionary definitions.
The comparison revealed a similar pattern in brains and machines. Individual neurons rarely encoded the same word across languages. Instead, meaning emerged at the population level.
Both neural activity and mBERT tracked broader context, organizing words into an abstract conceptual landscape called semantic geometry. In this map, related concepts cluster together—“cat” sits closer to “dog” than to “galaxy,” for example—even if the precise features defining those relationships are unclear.
Yet the map remained largely unchanged across languages, suggesting it captured a fundamental mechanism for language processing in the brain. Using the English map alone, the team could predict which Spanish words would cluster around “perro” (or “dog”).
“This is how the brain encodes the meaning of words across languages,” said Yan. “It doesn’t rely on individual neurons translating individual words, but groups of neurons adjusting their activities to create the similar pattern for equivalent words in both languages.”
The study focused on semantics, or meaning, as opposed to syntax, the rules governing sentence structure. A recent study also using single-cell recordings from people with epilepsy suggests that other groups of neurons, particularly those in the frontal parts of the brain, may specialize in grammar while ignoring semantics. Whether they also share a “map” across languages remains to be seen.
The next step is to watch these maps emerge. The team hopes to track people as they learn a new language, revealing how new words and concepts are woven into semantic landscapes in real time. The results could deepen our understanding of one of the most fundamental communication skills and even inspire more capable and efficient language models in AI.
“Our study shows that the brain is wired to learn multiple languages,” said study author Benjamin Hayden.
The post How the Bilingual Brain Switches Languages With Ease appeared first on SingularityHub.
2026-07-03 22:00:00
The night sky seems eternal and unchanging. But in cosmic time, nothing could be further from the truth.
Vasily Belokurov is one of three winners of the 2026 Kavli Prize in Astrophysics. The award is for uncovering fossil evidence of past galactic mergers that prove how the Milky Way evolved.
No matter the time or vantage point, from a pre-Neolithic cave to a post-lockdown London high-rise, the predictability of the night sky has always been humanity’s symbol of permanence and reassuring stability.
Yet this apparent calm is deceptive. Our galaxy, the Milky Way, emerged from chaos and turbulence, and its constellations are full of migrants, exiles and survivors. Right now, it has begun to stretch and distort again, pulled by a massive companion and heading for an inevitable collision.
How can I be so sure? As a galactic archaeologist, my job is to reconstruct the past of our galaxy and read the signs of its future.
Instead of digging through soil, I use the laws of dynamics and stellar evolution to sift through hundreds of millions of stars—searching for the most ancient and chemically peculiar among them, interpreting their orbits and piecing together the events that shaped the Milky Way. One ancient encounter left scars so deep that, billions of years later, they still define the galaxy around us.
I want to understand what governs the lives of these massive cosmic systems: which changes are nature—the slow internal evolution of a galaxy disk—and which are nurture, imposed by collisions and mergers.
Questions about the source of dark matter underpin it all. This is the invisible substance whose gravity holds galaxies together, but whose true identity remains one of the greatest unsolved puzzles in astrophysics.
The Milky Way is the one galaxy where stellar motions can be measured in extraordinary detail. This allows cosmologists including myself to construct our most precise map yet of dark matter: how far it reaches, how dense it is around the sun, what shape it has, and how smooth or lumpy it may be. If we can build this map in enough detail, we may begin to understand not just where dark matter is, but what it is.
Our work has been transformed by a revolution in open sky surveys. From 2000, the Sloan Digital Sky Survey showed what becomes possible when vast astronomical datasets are made public, enabling discoveries far beyond the goals for which the survey was first built.
And since 2014, Gaia, the European space telescope, has taken this transformation to another level by mapping the positions and motions of nearly 2 billion stars, turning the galaxy into a vast archaeological record. No ruins, no shards, and no bones—only stars that hold the clues.
The clearest giveaway that something cataclysmic took place long ago in our galaxy is the migrants we observe: stars that were not born in the Milky Way.
While native stars mostly travel together, circling the galactic center in the great rotating flow of the disk, migrants cut across that order. They slide past the locals, plunge into the inner galaxy, then fly back out to its outskirts, again and again.
These unusual orbits go hand-in-hand with unusual chemistry. Most of the migrant stars are less enriched in heavier elements than the locally born population. Their chemical composition is a sign of a slower rate of evolution that is typical of a dwarf galaxy.
This makes the migrants doubly valuable. They are both fossils of the Milky Way’s violent past and probes of its outer regions, traveling where the local stars rarely go.
One of the central ideas in the theory of cosmic structure formation is that galaxies grow hierarchically. Smaller galaxies fall into larger ones and are torn apart, leaving their stars behind as migrants.
In the Milky Way, the largest ancient structure of this kind is known as Gaia-Sausage-Enceladus. It is the remains of a vanished galaxy that collided with our own between 8 and 11 billion years ago (the “sausage” refers to a pattern in its stars’ motions).
The Milky Way also did not go through that crash unscathed. The collision rewired and reshaped it.
Some of these changes are easily visible in the data. Stars from the old disk were splashed into our galaxy’s halo, becoming exiles in the place where they were born. A new posse of star clusters were also acquired.
At the same time, we think something even more momentous was taking place. The encounter changed the orientation of the Milky Way’s disk, and its alignment with the dark matter halo.
While dark matter is too diffuse to dominate our solar system, in the outer galaxy it is the main gravitating mass—moving, streaming, and in the standard picture, clumping into a hierarchy of lumps.
Around the Milky Way, this dark matter forms a vast halo, much larger than the luminous part of our galaxy. We often imagine this halo as a sparse, round cloud, but Gaia has helped show this picture is too simple.
The dark halo can be stretched out of shape by a major encounter. Like a ship beginning to list, the Milky Way started to lean—not suddenly, not visibly, but over billions of years.
Unusually, compared with many galaxies of similar mass, the Milky Way was allowed ample time to recover from the shock of the “sausage merger.” No other cosmic cataclysm appears to have shaken our galaxy since, letting it settle into a quiet, uneventful life. That is, until now.
The Large Magellanic Cloud (LMC), currently our galaxy’s most massive companion, is already pulling at the Milky Way, disturbing its halo again. In an echo of what happened some 10 billion years ago, the Milky Way is being drawn into an accelerating dance with this neighboring dwarf galaxy, recoiling in response to the LMC’s approach.
This is a dance that only one galaxy is likely to survive intact. A new chapter of migration, survival and adaptation has begun.
None of this spoils the beauty of the night sky—it deepens it. The calm band of light above us is not a symbol of permanence, but the visible reminder of a long survival.
The Milky Way has been broken, rebuilt, and is now being disturbed again. Its stars remember the past; their motions reveal the future. What looks eternal is, in truth, a moment in a much longer story.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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2026-07-02 22:00:00
A single observational case suggests psilocybin may ‘awaken’ cognitive reserve in dementia. But scientists caution controlled trials are needed to know if the drug was the cause.
For five years, Alzheimer’s slowly stripped away a Japanese-American woman’s ability to speak more than one syllable at a time. The woman, now in her 80s, was diagnosed roughly a decade ago, and her condition steadily worsened. She struggled to walk and recognize family members.
Then, under medical supervision, she took a large dose of mushrooms containing the psychedelic psilocybin. Within three days, her symptoms had improved. She began spontaneously recounting memories and initiating conversations in full sentences. Her alertness returned, and she could move around independently.
A week later, she was recognizing family members, asking where they were, and pointing out cars that seem out of place.
Psilocybin has been maligned for decades. But renewed interest in its unique effects on the brain has pushed it into mainstream research. Early studies suggest it may help treat depression, anxiety, addiction, post-traumatic stress disorder, and other psychiatric conditions. A clinical trial is underway to gauge whether it can protect the aging brain.
The case study, conducted in Brazil, adds to that momentum. The team emphasizes that it describes a single patient and is purely observational. Because of the severity of her disease, they could not perform brain scans, measure biomarkers, or conduct standard cognitive tests. Exactly why her symptoms improved remains unknown.
Even so, they propose that psilocybin may have temporarily unlocked brain function in late-stage Alzheimer’s, potentially allowing dormant neural networks to rewire.
Alzheimer’s is often synonymous with memory loss. Sadly, symptoms range far beyond forgetting names or misplacing glasses.
As the disease progresses, people gradually struggle to find the right words or follow conversations. Their ability to tackle everyday tasks—cooking, managing finances, planning ahead—erodes. Depression, irritability, and anxiety often emerge. Over time, their personalities flatten, leaving them less outgoing, engaged, or empathetic.
These stories are far too common. According to the World Health Organization, roughly 57 million people worldwide were living with dementia in 2021. Alzheimer’s may account for up to 70 percent of cases. As populations age, that number is expected to climb.
Alzheimer’s has no single cause. Genetics likely play a role. Some gene variants are linked to early-onset forms of the disease, an area scientists are now tackling with gene therapy.
Another hallmark of the disease is a buildup of abnormal protein clumps, or plaques, in and around neurons, which disrupts normal function and wrecks their ability to form neural networks supporting memory and cognition. Years of efforts to remove plaques have largely failed, though the FDA recently approved two antibodies that reduce them and modestly slow cognitive decline.
Then there’s inflammation. In Alzheimer’s, the brain’s immune system can become overactive. Rather than responding only to damage, inflammation drives disease progression, spreading toxic protein clumps through the brain and further damaging its ability to form new connections.
Here’s where psilocybin, the active ingredient in magic mushrooms, may help. Psilocybin alters serotonin signaling, a brain chemical involved in mood, perception, and cognition. But its effects likely extend far beyond that.
Studies in mice suggest the chemical boosts the brain’s ability to rewire, a process known as neuroplasticity. Human brain imaging studies have found that the psychedelic temporarily reorganizes communication between large brain networks, changing how distant regions interact. In some participants, supervised treatment has been linked to greater cognitive flexibility, deeper self-reflection, and improved well-being.
Other studies hint at a protective role. Psilocybin triggers the release of “nurturing” proteins. This process helps neurons survive stress and extend their branching connections. It’s these delicate structures that build up neural networks, and they wither away during depression, aging, and dementia. Inside the hippocampus, a region crucial for learning and memory, the drug stimulates the birth of new neurons, at least in mice.
Given its positive effects on brain plasticity, psilocybin is now being tested in multiple psychiatric disorders characterized by unusually rigid patterns of brain activity. Older adults remain largely absent from these studies, even though they could benefit the most.
Before treatment, the woman struggled with everyday life. For five years, she could communicate using only single-syllable words. Her mobility was severely limited, and she struggled with incontinence.
With the consent of her caretaker, she received five grams of the Enigma strain of Psilocybe cubensis. Because psilocybin levels vary widely between mushrooms, the exact dose is unknown. But compared to other clinical trials, it was relatively high.
The team chose the dose “based on prior experiential observations regarding depth and duration of psychedelic-induced neurobehavioral effects,” wrote the team.
Initially, the woman fell into a deep sleep-like state accompanied by elevated body temperature and heavy sweating. Roughly 19 hours later, she suddenly awoke and began speaking to caregivers in complete sentences, recounting memories from her life. The conversation lasted around four hours.
Over the following days, she became increasingly alert and engaged. She recognized family members, regained mobility, and could pick out matching clothes to dress herself. A week later, she was noticing small details in her environment, including a rental car parked outside the house. When a family member was absent, she asked, “Where did Celso go?” She also seemed to rediscover her love of social interactions, making eye contact, smiling back, and actively starting conversations.
A month after the initial session, she returned for a second supervised dose of three grams. After the second dose, she became even more verbally expressive, displayed a sense of humor, and described memories of surfing with her son on a peaceful island. Throughout the trial, the drug alleviated incontinence and improved her quality of life.
The results come with major caveats. The improvements were observational and largely reported by caregivers, leaving room for bias. The team didn’t administer standardized tests for cognition, dementia, depression, and anxiety. Nor did they perform brain scans or monitor sleep, making it impossible to determine what brain changes were behind her apparent “awakening.”
“Causality cannot be established, and spontaneous fluctuations inherent to neurodegenerative disease cannot be completely excluded,” they wrote.
But the study touches on a provocative idea in Alzheimer’s: Cognitive reserve. The theory proposes some people can tolerate greater levels of harm to the brain and continue functioning despite significant damage. Psilocybin may have temporarily tapped into these reserves, allowing dormant neural circuits to engage and rewire to compensate for impaired ones. The hypothesis is highly speculative and needs to be rigorously tested.
Meanwhile, a clinical trial is investigating whether psilocybin can reduce depression and improve quality of life in people with mild cognitive impairment or early Alzheimer’s disease, moving the needle beyond a single case study.
For one family, however, the benefits are already substantial. At a follow-up visit, the woman spontaneously said to everyone in the room, “It is pleasant to come here.”
The post Woman With Alzheimer’s Shows Striking Improvement After Taking Magic Mushrooms appeared first on SingularityHub.
2026-06-30 22:00:00
Switches drive nearly every machine. A new one, made of folded DNA, does the same work at the scale of molecules.
Scientists have long dreamed of developing nanoscale machines, but building reliable mechanical components at the molecular scale has proved challenging. Researchers have now developed a DNA-based switch that can rapidly and repeatedly snap between two stable states, much like the components that underpin everyday electronics.
Ever since Richard Feynman’s visionary lecture “There’s Plenty of Room at the Bottom,” researchers have been enamored with the idea of engineering at the scale of atoms and molecules. But manipulating matter at the nanoscale is easier said than done.
Individual molecules are in constant motion and continuously jostled about by the thermal energy of their surroundings. This makes it extremely difficult to position and assemble larger structures and undermines control of the mechanical motion of components.
This is particularly true for switches—key components in many mechanical and electronic devices you might want to build. Getting a tiny structure to hold one position, flip cleanly to another, and then stay there has so far been an unsolved problem.
But now, a team at the Technical University of Munich has created a switch made from folded strands of DNA that remains stable for up to an hour and flips in milliseconds on the application of a brief electric field. Crucially, the device was able to switch back and forth repeatedly with no degradation in performance.
“Individual devices sustain hundreds of thousands of switching cycles over several hours and remain functional for actuation over several days,” the researchers write in a paper in Science Robotics. “As a nanoscale electromechanical interface, our device enables applications in molecular information processing, optical nanodevices, and the dynamic control of chemical reactions.”
The device borrows a principle from standard engineering known as a snap-through mechanism, which rests in either of two states and only flips when pushed hard enough, a bit like a light switch.
Scaling the idea down to a few tens of nanometers meant designing rigid arms linked by flexible molecular hinges, so the structure settles into one of two configurations and does not flick between them on its own. The team relied on DNA origami to accomplish this, where a long strand of DNA is folded into custom 2D and 3D shapes using hundreds of shorter “staple” strands.
One of the two arms features a longer “extension arm” that acts as a lever to push the switch between configurations. DNA carries negative charge, so when an electric field is applied to the device, it pushes the arm hard enough to flip the switch. Left alone, the team estimates that the structure stays in its resting state for roughly six hours, and they observed no spontaneous flips while monitoring 70 switches for an hour.
One of the device’s main strengths is its endurance. One switch survived more than 200,000 flips over five and a half hours, and a simplified version withstood a million switching cycles in three hours while still working about 85 percent of the time. Performance varied considerably from one device to the next, however, with some failing after a few thousand cycles and others continuing for days.
The researchers say failures likely stem from a combination of contaminants, surface wear, and chemical changes in the surrounding fluid. However, some inactive switches later started working again, which the team says suggests they are capable of self-repairing.
To test whether the switch could do anything useful, the researchers attached a gold nanorod to the moving arm, turning it into a microscopic light switch that changed how light scattered off the particle. In a second test, they used the switch to expose or hide a molecular binding site, allowing it to control whether DNA strands could attach.
That second capability could be particularly useful as it could make it possible to control chemical reactions—for instance by turning enzymes on and off. The authors suggest that this could be used to create “control knobs” for chip-based bio-factories that run sequences of reactions.
Considerable obstacles remain before the device can become genuinely useful. A single switch encodes just one bit of information, and the team acknowledges that wiring arrays of switches together to create something resembling a circuit remains a distant prospect.
But a workable switch is a fundamental component that can be used to create all manner of devices. While we’re still a long way from Feynman’s dream of molecular machines, this is a meaningful step in that direction.
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2026-06-29 22:00:00
Let loose on existing regulations, AI models sniffed out known loopholes—and exposed entirely new ones too.
AI’s hacking skills are big news at the moment, but finding vulnerabilities in code may be the least of our worries. A new study suggests AI models can discover potentially damaging loopholes in the rules and regulations underpinning society.
Modern AI systems are powerful optimizers. Give them a goal, and they’ll pursue it relentlessly, quickly discovering solutions that would take a human years to find. But they are also incredibly literal in the way they approach a problem. They will do exactly what you tell them and are incapable of reading between the lines in the ways a human would.
This tendency leads to a recurring problem known as “reward hacking,” where an AI finds some loophole to maximize its performance on the metric used to measure success without actually achieving what its designers intended. The classic example is the AI that discovered it could win a boat racing videogame by looping around in circles collecting power-ups rather than completing the course.
The problem is partly due to humans being bad at specifying their goals. And unfortunately, it seems this weakness exists in the rules and regulations used to run society. When researchers let popular large language models loose in 72 simulated regulatory environments, the models found 60 percent of known loopholes and even identified some entirely new exploits.
“Within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery,” the authors write in a non-peer-reviewed paper published on arXiv. “Models learn to hack the social rules and generate strategies that remain technically compliant while defeating regulatory intent.”
The regulatory environments the researchers created were primarily based on rules governing things like pharmaceutical patents, NBA salary caps, and deep-sea mining. In each case, Alibaba’s Qwen3 model was given the relevant rules, an explanation of its task, a predefined set of actions it could take, and the system used to score different outcomes.
A more powerful model, Google’s Gemini-3-flash, then simulated the consequences of different actions Qwen3 took and judged if and when it had found a way to exploit the rules of the game. When that occurred, the larger model patched the loophole by adding new rules, and the smaller model was set loose again. Over many iterations, the models to discover increasingly subtle workarounds.
When building their regulatory environments, the researchers omitted real-world fixes that regulators had used to close known loopholes. Over many trials, Qwen3 rediscovered more than 60 percent of these exploits. In a simulation of pharmaceutical patent regulations, the two models ended up replaying the same sequence of loophole discovery and regulatory reform that occurred in the real world.
Crucially, their behavior emerged spontaneously without the researchers asking the algorithms to cheat the system. This is a byproduct of the popular reinforcement learning approach the researchers used, where a model is rewarded for getting closer to a specific, numerically-defined goal.
Worryingly, the team found that existing safety measures offered little protection. Both models are designed to refuse prompts featuring harmful language, but loophole-seeking behavior slipped under the radar. When asked to self-critique their own behavior, the models identified fewer than 40 percent of their own exploits.
The researchers note that the same capabilities could be used more proactively to scour proposed regulations for loopholes before enactment. But lead author Wei Liu, a PhD student at King’s College London, says there are always likely to be gaps. “In the real world,” he told Science, “society is a huge, complicated reward function that can’t ever be patched to a perfect status.”
Adding to the concern, the models used in this study were far from the frontier, suggesting that more powerful AI could be even more adept at regulatory hacking. Whether our existing institutions can adapt quickly enough to this emerging threat is an open question.
The post Forget Code: AI Is Learning to Hack Society appeared first on SingularityHub.
2026-06-27 22:00:00
IBM Has Unveiled Chip Technology That Could Help Extend Moore’s Law Another DecadeSophia Chen | MIT Technology Review ($)
“To fit more transistors on a chip, engineers across the industry are eyeing a pivot to an approach familiar to urban planners: build up. On Thursday, IBM announced it has created a chip that uses this strategy. The new architecture, known as a nanostack, vertically stacks transistors in two layers on a silicon chip.”
AI Is Designing Radio Chips That Humans Couldn’t Even ImagineKaushik Sengupta | IEEE Spectrum
“Some of the…chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer.”
A Dark Dimension Could Link Two of the Universe’s Great UnknownsSteve Nadis | Quanta Magazine
“Even though scientists have assumed that dark energy and dark matter ‘don’t have anything to do with each other,’ said Tim Tait, a particle physicist at the University of California, Irvine, ‘you can imagine a case where one influences the other. And it would not be surprising if [they] were manifestations of a kind of unified theory of the dark universe.'”
New Effort Will Get Genome Sequences for Entire Endangered Species ListJohn Timmer | Ars Technica
“Over 2,300 plant and animal populations remain on the [endangered species] list, requiring ongoing government intervention. On Thursday, it was announced that all of those species would see their genomes sequenced and tissue samples preserved to aid future conservation efforts.”
AI Was Supposed to Kill Engineering Jobs, but New Data Suggests They’re the Most ResilientMarina Temkin | TechCrunch
“Software engineering, in theory, is the professional field most vulnerable to automation, given the rapid adoption of AI-powered coding tools. However, researchers at venture firm SignalFire say the hiring data tells a different story. ‘The rationale given for lots of layoffs is consistently AI, and specifically they’ll say AI with respect to code; they’ll say one engineer could do the job of however many engineers in the past,’ said Asher Bantock, SignalFire’s head of research. ‘What we’re seeing on the ground is a little inconsistent with that.'”
This Flying Solar-Powered Platform Could Deliver Better Internet From the AirRachel Courtland | MIT Technology Review ($)
“As soon as August, a giant silver bullet will cut its way through the dry air of the southwestern US and cross the Pacific to reach the coast of Japan. Once there, the roughly 200-foot-long craft, built by the New Mexico–based company Sceye, will park some 18 kilometers above the ocean’s surface, in a wispy-thin layer known as the stratosphere. Then it will use a custom-built antenna to supplement Softbank’s 5G network, a test that will include beaming data straight to devices.”
A New Paper Argues Microsoft Exaggerated Its Quantum Claims a Year AgoSophia Chen | The Verge
“A critique published in Nature Wednesday calls the basic technology behind Microsoft’s ‘breakthrough’ quantum computing chip the Majorana 1 into question. …In a peer-reviewed article, Henry Legg, a physicist at the University of St. Andrews, reanalyzed Microsoft’s data on their device and argued that the company’s researchers did not conclusively demonstrate a working topological qubit in the first place.”
The AI World Is Getting ‘Loopy’Russell Brandom | TechCrunch
“‘Two years ago, we wrote source code by hand. We started to transition so agents write the code. And now we’re transitioning to the point where agents are prompting agents that then write the code,’ [said Claude Code creator Boris Cherny]. ‘As big as the step from source code to agents was, loops are just as important and as big a step.'”
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