Scientists used AI to find targets shared by thousands of related viruses and build what they hope is a universal vaccine.

Researchers at the University of Cambridge have developed what they describe as a fundamentally new type of vaccine using artificial intelligence. The vaccine’s key component was designed entirely by AI and has now been tested in people for the first time.

The goal is ambitious: a single vaccine that works not just against all known human coronavirus variants, but against related bat viruses that could jump from animals to humans and cause future pandemics.

Traditional vaccines train our immune system to recognize one specific virus. The problem is that viruses mutate. When they change enough, the vaccine stops working, which is why we need a new flu shot every year and why Covid vaccines have been updated repeatedly since 2021.

AI offers a way around this. By analyzing genetic data from thousands of related viruses, it can identify the parts that stay the same across different strains and that are unlikely to change over time. Target those stable features, and you have a vaccine that should work against the whole family, not just the strain you started with.

This is exactly what the Cambridge team did. They used AI to scan viruses from the sarbecovirus family, which includes the viruses that cause both SARS and Covid, as well as a range of animal coronaviruses—looking for shared features that evolution has left largely untouched. Those features became the basis of the vaccine.

DNA Vaccines

While many people are familiar with the mRNA shots used during the pandemic, this new vaccine uses DNA. DNA vaccines are generally more stable than mRNA vaccines, making them easier to store and transport. This is a significant advantage in lower-income countries where “cold-chain” infrastructure is limited.

They can also be administered without needles. A high-pressure stream of liquid delivers the vaccine through the skin, making administration less painful and easier to scale up during an outbreak.

Could It Protect Against Future Pandemics?

These practical advantages matter most if the vaccine itself can do something no existing jab can: protect against viruses we haven’t encountered yet.

Broad-spectrum vaccines could change the way the world responds to emerging infectious diseases. By offering much wider protection than traditional vaccines, they could provide rapid immunity against new and emerging viral threats. This would equip public health officials with tools to stop future outbreaks in their tracks before they have a chance to turn into global pandemics.

They could also transform our approach to more familiar diseases. Influenza is a prime target because it exists in many different strains and evolves so rapidly. Scientists have to predict which strains will dominate each flu season, and if they guess wrong, vaccine effectiveness can suffer. A universal flu vaccine that targets features shared across multiple strains could eventually end the annual race to keep up with the virus.

The Ebola virus shows why this matters right now. The recent outbreak in the Democratic Republic of the Congo and Uganda is driven by the Bundibugyo strain, which bypasses existing vaccines. While researchers rush to create a new vaccine specifically for this strain, local communities remain at high risk. A broad-spectrum vaccine designed to cover an entire virus family could transform that picture.

What the Trial Found

This is the first human trial of an AI-designed vaccine. The results showed that this DNA vaccine was able to stimulate the immune system to produce antibodies that can recognize different types of sarbecoviruses. The technology was found to be safe and well tolerated.

This is an exciting advance because it demonstrates how AI has the potential to design variant-proof vaccines against future pandemic threats. The needle-free delivery system could also make the vaccine easier to administer and distribute worldwide.

However, there is more work to do. Although the results in this study are encouraging, the immune responses following vaccination were modest. It was also uncertain how long the protection lasts and whether further boosters will be required. Larger trials are also needed to determine whether the vaccine can prevent or reduce viral infections in the real world.

A universal vaccine remains a few years away. And any new vaccine must still pass larger trials to prove it is safe, effective, and provides lasting protection. But this study shows the goal is getting closer—and AI may help us get there faster.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post The First AI‑Designed Vaccine Has Been Tested in People. Here’s What Happened. appeared first on SingularityHub.

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.

Bridging Worlds

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.

Mapping It Out

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.

A Language Landscape

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.

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.

A Cataclysmic Collision

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.

How the Milky Way Was Rewired

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.

A New Galactic Dance

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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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.

Brain Under Fire

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.

Tale of One

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.

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.

The post This DNA Switch Could Control Molecular Machines appeared first on SingularityHub.

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.

Computing

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.”

The post This Week’s Awesome Tech Stories From Around the Web (Through June 27) appeared first on SingularityHub.

There’s a vast difference between launching satellites and operating an industrial-scale computing infrastructure in orbit.

Imagine if one company could become the railroad, electric utility, and cloud-computing provider of the emerging space economy. That potential fueled excitement around the long-anticipated initial public offering of SpaceX. Investors are not simply betting on rockets anymore. They are betting on an entire orbital ecosystem.

Among the most ambitious and challenging ideas riding this wave of enthusiasm is something that sounds almost like science fiction: orbital data centers. SpaceX may be one of the most well-known companies seeking to build them, but it is not the only one.

The logic is seductive: Launch the data centers into orbit, where solar energy is abundant and land, water, and local power grids are no longer constraints. As artificial intelligence drives an explosion in computing demand, companies are pitching orbital data centers as a way to escape the growing environmental and infrastructure pressures of Earth-based computing. Data centers often also face backlash from the public at having these centers located in their communities.

But there is a vast difference between launching satellites and operating an industrial-scale computing infrastructure in orbit. Space is unforgiving. Radiation damages electronics. The electronics generate enormous amounts of heat, and getting rid of that heat is surprisingly difficult in space. Repairs are extraordinarily expensive, and every pound launched into orbit still carries a significant cost.

We are engineering professors who study data-center design and space systems engineering. Building a space-based data center will involve considerations from both sides.

What Goes Into a Data Center on Earth

First off, consider what goes into an Earth-based data center, like those that you’ve probably begun to see pop up everywhere. These facilities power cloud computing, video streaming, online banking, scientific computing, and increasingly, artificial intelligence. But a data center is much more than a room full of servers.

A data center needs several things to operate reliably. The first is electric power. Servers, networking equipment, and storage devices consume large amounts of electricity, and that power demand is growing rapidly with AI.

The second is cooling. Almost all the electricity consumed by servers eventually becomes heat. If that heat is not removed quickly and reliably, equipment performance drops, failures increase, and the data center can shut down. Cooling systems often include air handling units, chillers, cooling towers, pumps, and increasingly, liquid-cooling equipment. In many facilities, cooling is the largest energy consumer after the computing equipment itself.

The third is physical infrastructure, including the necessary land, buildings, structural support, backup power, water systems, communication networks, and maintenance access. Data centers also need to be close enough to users and network backbones to provide fast digital services.

In short, Earth-based data centers are large electrical and thermal infrastructure systems built around computing hardware.

Placing Them in Space

So what would it take to build these data centers in space, and why are companies finding this possibility such an interesting business proposition?

As on Earth, these data centers would require massive amounts of power. In space, this power would come from solar panels. The sun always shines in space and can’t be blocked by clouds. However, depending on the orbit the solar panels are put in, the Earth may shadow them for some portion of the orbit.

And even the best solar cells available today can convert only about half the sunlight that hits them to electricity.

Another potential advantage found in space is cooling. The cold background of space (roughly -455 degrees Fahrenheit, or -270 degrees Celsius) creates an opportunity: Waste heat from the data center could escape into space through radiators, keeping the electronics cool.

In principle, that design could eliminate some of the bulky and water-intensive cooling infrastructure used on Earth. However, those thermal radiators would require a large amount of surface area, and that would be in addition to the area required by the solar panels.

In space, there is no air to blow across hot equipment and help heat escape. The heat has to leave as infrared radiation, which is a relatively slow process. As a result, removing 10 megawatts of waste heat can require radiator surfaces comparable to the size of two football fields.

Space-based data centers could also avoid some of the local conflicts that come with building large data centers on the ground. Many communities resist new data center developments because of their land use, energy and water demand, and noise and environmental impact.

A space-based system would avoid competing for local land and water resources, and it would not generate neighborhood noise or require local zoning approval in the same way.

However, space is already getting crowded, and launching thousands of large orbital data centers would accelerate this issue. Orbital debris and micrometeorites are hazards because they can puncture the space data center, and a worst-case collision could destroy it and create even more space debris.

The frequency of space launches necessary to send all the equipment to orbit may also become a concern for some communities. SpaceX has had protests at its launch complex in Boca Chica, Texas from local activists who argue its rocket testing and launches damage the surrounding environment.

All that data would need to be sent between Earth and these data centers—and between the data centers themselves—using radio waves or laser communications systems. Although satellite constellations such as Starlink and Amazon Leo have demonstrated that doing this is possible, the amount of data sent to and from space would balloon.

Additional Challenges

These data centers, along with their solar panels and radiators, cannot be launched in one piece and would need to be assembled in space. This process would require new equipment for in-space servicing, assembly, and manufacturing.

Another key challenge is the refresh cycle of computing hardware. Data-center servers are not built to last forever. Operators on Earth usually replace or upgrade hardware every three to five years as chips improve, workloads change, and equipment ages.

And equipment failures can require replacing components. The refresh and repair processes are relatively straightforward on Earth, where workers can physically remove and replace servers.

In space, refresh and repair becomes much harder. Hardware sent to orbit may be difficult or too expensive to upgrade. If the computing platform cannot be updated, or too many components fail, it may become obsolete long before the surrounding infrastructure reaches the end of its useful life.

In a field where performance improves so rapidly and demand from computing continues to increase, this hurdle could prove a major economic and operational challenge.

Then there is the harshness of space. These data centers would be in a near vacuum, with constant radiation hitting them. And depending on their orbit, they would go from hot when in the sunlight to cold in Earth’s shadow many times a day. All of these challenges, and more, are issues that will need to be addressed.

So, Do They Still Make Sense?

Despite these challenges, companies are moving forward with designing space-based data centers. SpaceX just announced the design for its AI1 Compute Satellite, which it hopes to use as an orbital data center spacecraft. However, this satellite is 100 to 1,000 times less capable than current Earth-based data centers.

Not every computing task makes sense to do in space. Many data center applications depend on fast response times and close connections to users on Earth. Financial transactions, interactive AI services, and most cloud applications are extremely sensitive to delay.

More feasible early applications may be those that are less latency-sensitive and more tightly connected to space operations. Examples could include processing Earth observation data from satellites, military or intelligence data processing, scientific computing related to space missions, or specialized computing for satellites and other space assets.

In other words, the first viable space data centers may serve space-based customers before they compete with mainstream cloud data centers on Earth.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Companies once moved whole factories overseas to reduce labor costs. Now, workers a world away can operate local excavators, forklifts, and even humanoid robots with an internet connection.

Packaging potassium sulfate, a fertilizer vital to the planet’s food supply, is visually striking—not because of what you see, but because you don’t see much at all. In China’s Xinjiang region, home to the world’s largest deposit of the mineral, piling it up in warehouses creates dust clouds so severe that workers are forced to drive heavy machinery by feel.

Some companies are now turning to a technology that not only offers a way to see through the dust but also keeps workers from entering the warehouse at all. The system, developed by BuilderX Robotics, a Chinese tech company, uses cameras that are like night-vision for dusty areas. More significantly, operators drive excavators, loaders, and other machines from a remote office filled with rows of videogame-like stations. All they need is a 5G or satellite connection.

The ability to control physical machines from a distance is called teleoperation, and it could become a significant force of change in the global economy.

In Japan, the shelves of over 300 convenience stores are being restocked by robots monitored and sometimes controlled by workers in the Philippines. Düsseldorf airport was slated to begin testing shuttles driven by remote workers in May. A startup in Atlanta is offering robot security guards operated by remote staff, and last summer, a surgeon in France performed a teleoperated procedure on a patient in India.

While offshoring teleoperated jobs to overseas workers hasn’t yet become routine, Mark Graham, professor of internet geography at the University of Oxford, suggests the technology is worth our attention because it might enable companies to expand on their well-established habit of outsourcing jobs to places where labor is cheaper.

The use of remote labor isn’t new, Graham told SingularityHub. But teleoperation extends the logic of outsourcing to tasks that were previously thought to be “stubbornly local.”

“The novelty is less about the existence of remote labor and more about the kinds of work that can now be pulled into a planetary labor market,” he said. “Once that happens you can expect the usual pressures around labor arbitrage, control, and fragmentation to follow.”

It’s not clear we’re ready for the consequences.

BuilderX Robotics is a global leader in teleoperation for heavy machinery and a good expression of the changes ahead. Shaolong Sui, a graduate of Stanford University with a degree in mechanical engineering, founded the company in 2018 as a response to labor shortages in the construction industry in Asia.

“A shortage of trained operators isn’t a problem only in developed countries,” he told me. “Young people here in China don’t want to do this work. It’s dusty and dangerous.”

Rather than focusing on full robotic autonomy, which many construction companies have pursued over the past decade, Sui identified teleoperation as a more realistic way to move operators from harsh environments to safer conditions. Making use of the proliferation of low-cost sensors and 5G at the time, Sui completed a prototype in 2019. Today, his company offers teleoperation for 14 different industrial machines, including excavators, loaders, and bull dozers.

In our conversation, it was clear he hopes to improve working conditions for manual laborers. I lost track of the number of times he mentioned removing operators from dangerous worksites. “These workers deserve a better life,” he said.

BuilderX’s workstations do seem to have transformed some of the punishing work of an industrial site into a more white-collar experience, complete with tea and coffee break rooms and toilets down the hall. Sui said his solution allows construction firms to hire senior citizens or people with disabilities who, thanks to the videogame-like interface, can now operate heavy machinery. In another video, a Japanese woman who pilots an excavator proudly shows off her complex nail art, something she claims she couldn’t maintain when she worked in the field.

“Not only is this a much safer workplace, but the lifestyle benefits are that you can sit in an air-conditioned space, enjoy your tea, and when you go home, you’re still clean,” Sui said.

There’s no doubt the approach is safer for frontline workers like those in Xinjiang. Evidence suggests that high levels of potassium dust exposure can cause chronic bronchitis. While pulling someone from dangerous work is a good thing and that should be taken seriously, Graham told me, it doesn’t necessarily mean they’re free from exploitation.

“A worker can be removed from the physical site and still be subjected to intense surveillance, deskilling, isolation, fragmented contracts, algorithmic management, and downward pressure on wages. In other words, the risk can move rather than disappear,” he said.

Sui and Graham both agree there are plenty of forces that might slow the pace of outsourcing. Currently, none of BuilderX’s customers offshore work to overseas operators. But that doesn’t appear to be a technology constraint, as recently demonstrated by an operator in Poland controlling an excavator over 4,000 miles away in Beijing. On the technical side, latency—the delay between operator and machine—and reliability will shape the rate at which firms can choose to offshore workers. But it’s more likely to be limited by regulatory constraints in the form of licensing, insurance, and safety requirements.

That said, Graham believes the biggest force driving work overseas will be the same one that’s pushed clerical and service work offshore; the relentless pursuit to increase profit and reduce cost.

“If firms can hire people in lower-wage labor markets to operate expensive equipment thousands of miles away, many of them will try,” he said.

Most debates about AI and robotics focus on job loss due to automation. There is relatively little discussion about the risk of offshoring teleoperated work as the technology comes online. This is partly due to the hype surrounding physical AI, a Silicon Valley buzzword describing a world where fully autonomous robots cut humans out of the loop. But Graham says that when machines arrive people tend to incorrectly assume humans disappear.

“In many cases, what gets described as automation is really a reorganization of labor. Work gets broken apart, moved around, and hidden from view,” he says.

As is the case with AI,  the robotics industry’s push toward full automation is still plenty reliant on a hidden system of faraway workers. Teleoperation provides training data for robots and is needed to help them deal with unexpected events. Consumer robotics startup 1X is selling a $20,000 humanoid that will sometimes need to be  controlled by remote staff. It’s not clear how often future robots cleaning dishes in San Francisco kitchens will be steered by gig workers in Mumbai.

Robotaxi company Waymo already relies on human agents to assist, though not literally drive, vehicles stuck in difficult scenarios. The firm recently disclosed for the first time that some of these agents are based in the Philippines. This information, surfaced during US congressional testimony, immediately raised questions of oversight for safety-critical work: For instance, should a worker in Manila be required to get a California driver’s license?

Amid an already combustible US political environment, teleoperation could raise the heat even higher. Fueled by fears of Americans losing jobs to people overseas, Wyndham Hotels and Resorts, the parent company of La Quinta, was last year forced to respond to anger over a viral video depicting workers allegedly in India remotely handling check-in at one of their Miami hotels. As Graham points out, people tend to care more about outsourcing when it’s no longer hidden in a back office.

But outrage alone, he says, rarely defeats a business model that saves money. Due to network effects surrounding training, infrastructure, and other business process optimization, outsourced labor also tends to cluster in specific areas. This may already be happening in the case of Waymo, which could soon see the rise of something like a “driving district” in Manila. In the future, other types of teleoperated work could follow suit, giving companies a ready-made destination to shop for low-cost labor.

For Graham, it’s urgent that we begin requiring certification from independent bodies, which can better scrutinize a company’s production networks. At Oxford he directs Fairwork, a project aiming to improve labor practices in digital supply chains.

I asked Sui how he thinks his customers may reorganize their operations around this new ability to remotely control their machinery.

“We’re working with traditional industries, and so it’s not just about adopting a new technology. There are significant management changes they will have to navigate. You could call this transformation friction because they will need time to digest this new capability step by step,” Sui said.

Despite the fact they could use the technology to outsource work across national borders, none of his customers are doing so just yet. Sui used open pit mines as an example. In this case, where fully developed towns with schools and hospitals have built up over decades, his customers still cluster their workforce next to the sites where they operate. Instead of driving into the mine, operators work from an office and go home clean at the end of a shift.

BuilderX has deployed its technology at more than 100 sites in China, Japan, and parts of Europe. It’s now expanding into new markets including South America and the Middle East. When asked whether he thinks his technology will be used for transnational outsourcing, there’s no hesitation. “Oh yes, I think this is coming in the very near future.”

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AI needs to focus more like we do.

“Attention is all you need.”

This 2017 breakthrough idea transformed AI. The concept of self-attention became the foundation of today’s chatbots. Claude, Gemini, and ChatGPT are all large language models (LLMs), AI systems designed to focus on the matter at hand while filtering out distractions.

The results have been remarkable. From brainstorming recipes to generating code, apps, websites, and content, LLMs are being woven into our lives at breakneck speed.

But now, a City University of New York team and collaborators are asking: How closely does AI self-attention resemble human attention?

It’s not just academic curiosity. AI researchers have long looked to the brain for ideas to improve machine intelligence. In turn, AI models have offered new ways to investigate the brain. Comparing artificial and biological attention could inspire AI that concentrates more like us.

In their study, the team asked multiple chatbots to complete a classic psychology test of attention and cognitive control. Participants are shown the word for a color—such as “red”—written in either the same or a different color than the one the word describes. The challenge is to name the ink color while ignoring the word itself.

On short word lists, the chatbots performed at a high level. But as the tasks grew longer, their focus faltered. Instead of naming the ink color, they increasingly defaulted to reading the word. Under more demanding conditions—ones that also trip up people—their performance nearly collapsed.

The findings suggest today’s AI attention systems are “fundamentally limited,” wrote the authors. They go on to say that adding mechanisms similar to “those in biological attention is crucial for achieving artificial general intelligence.”

Attention, Two Ways

Doomscrolling. YouTube. Dinner plans. Family obligations. A barrage of notifications.

Life sometimes seems like everything, everywhere, all at once. Yet the brain can usually lock onto what matters most and push everything else into the background.

Far from a single, straightforward mechanism, attention emerges from multiple brain regions. According to attention network theory, three networks do most of the heavy lifting.

The alerting network keeps the brain ready for action. The orienting network selects which sights, sounds, smells, and sensations deserve attention. Finally, the executive control network resolves conflicts between competing streams of information, helping direct thoughts and actions toward a goal.

Together, these systems allocate the brain’s limited resources. Touch a hot stove, for example, and your brain immediately shifts attention to the burn over dinner. The food can wait; cooling your hand can’t.

AI works very differently.

Rather than processing language as complete sentences, LLMs break text into smaller units called “tokens.” Attention mechanisms then determine which tokens matter most for generating the next word, sentence, or response.

Self-attention is the key breakthrough behind modern chatbots. For each token, the model weighs and incorporates information from other tokens in a sequence, allowing it to track context across long stretches of text. This mechanism helps AI connect words and ideas, and underpins virtually all frontier LLMs today.

Researchers have since built on the concept. One approach, multi-head attention, runs several attention systems in parallel, with each “head” learning different patterns, such as grammar, syntax, or meaning. Another, cross attention, links information across different chunks of inputs and their outputs, making it especially useful for tasks such as translation and summarization.

But attention comes at a steep computational cost. To make models more efficient, researchers are also exploring sparse attention, which limits how many tokens a model considers at once. Another approach draws on information learned in the past to keep AI “focused.”

Despite the name, AI attention is ultimately a mathematical system. It helps determine what information is relevant in a specific context. But it lacks executive control, the network that keeps humans continuously focused on a goal despite distractions for long periods of time.

Color Blind

To test the limits of AI attention, the team pitted OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet against the Stroop task.

Invented by John Ridley Stroop in 1935, the test measures attention and cognitive control by forcing participants to resolve conflicting information. The challenge is simple: Name the color of a word while ignoring what the word means. In a congruent trial, the word “blue” appears in blue ink. In an incongruent trial, “blue” might appear in red or green, creating a conflict between what the eyes see and what the brain reads.

Humans are consistently slowed down by this interference. Even with practice, the effect remains, suggesting it taps into fundamental mechanisms of executive control.

In the study, the researchers created word lists of varying lengths and difficulty. Some were entirely congruent. Others were fully incongruent. A third set mixed the two conditions.

At first, the AI models excelled. On five-word tests, GPT-4o was over 90 percent accurate across all conditions. But as the number of words increased, performance plummeted. On 40-word incongruent tests, the model’s accuracy fell to roughly 15 percent. Claude showed a similar decline. In mixed-condition tests, both models’ performance nearly collapsed to zero.

“The sharp decline in color-naming accuracy with increasing list length indicates that transformer-based attention mechanisms are vulnerable to scaling demands,” wrote the team.

Perhaps most intriguing, some models correctly recognized they were taking the Stroop test and could even explain its rules. But that apparent awareness did nothing to improve their scores. In other words, a “book smart” understanding of the task wasn’t enough to execute it well.

The study joins a growing effort to borrow psychological tests for research in machine cognition, especially when AI is challenged with complex, dynamic decision-making tasks. Theory of mind tests, for example, let researchers gauge whether a system can track others’ beliefs, emotions, and intentions. Personality tests are helping shape model behavior and reduce sycophancy. And some LLMs are readily solving emotional intelligence tests, which measure how well the algorithms recognize and respond to social cues.

According to the authors, the new results point to a missing ingredient in AI attention: A mechanism similar to the brain’s executive control network, which helps us stick to a task and adapt when priorities change.

Future AI systems could benefit from higher-level executive control that continuously tracks progress toward a goal, detects when attention has drifted, and pulls it back on course, if necessary.

Rather than simply weighing which tokens are most relevant in the moment, a more human-like form of attention could help AI stay focused during complex tasks, such as long conversations, multi-step reasoning problems, or high-stakes use in scientific research and drug discovery.

“The ultimate goal of AI research is to develop artificial general intelligence comparable to human abilities,” wrote the team. “AI systems, like humans, may need to master fundamental attention mechanisms…before achieving the generalized problem-solving abilities characteristic of mature executive functions.”

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