2026-06-27 02:58:54
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.
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.
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.
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.
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.
The post Orbital Data Centers Are Seductive on Paper, but They Face Daunting Challenges in Reality appeared first on SingularityHub.
2026-06-26 00:02:56
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.”
The post Companies Could Soon Staff ‘Stubbornly Local’ Jobs With Workers 4,000 Miles Away appeared first on SingularityHub.
2026-06-24 02:35:30
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.”
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.
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.”
The post AI Collapses on a Classic Psychology Test. What It Reveals Could Stall Human-Level AI. appeared first on SingularityHub.
2026-06-23 06:46:18
An audacious trial will test psilocybin in people over age sixty to see if increases plasticity in healthy aging brains.
A handful of healthy senior citizens are about to trip on psilocybin—to see if the psychedelic protects aging brains.
Psilocybin, the active ingredient in magic mushrooms, is best known for its ties to 1960s counterculture. But now it may also herald a new genre of mental health treatment. From severe depression to post-traumatic stress disorder, studies have highlighted psychedelic drugs’ ability to reshape brain networks and relieve debilitating symptoms.
Most of these studies have focused on younger people with mental health conditions that don’t respond to standard treatment. The field’s success is prompting scientists to ask if psychedelics could also help healthy brains age better.
A team from the UC Berkeley Center for the Science of Psychedelics is about to find out. In a first-of-its-kind study focused on adults between the ages of 60 and 85, they’ll investigate how psilocybin affects perception, emotion, and memory using a battery of psychological tests.
Multiple scans before and after dosing will track changes in the brain. And detailed surveys will gauge broader shifts in well-being: Do participants feel more “in tune” with their emotions, feel less isolated, or experience a renewed sense of wonder about the world?
“What really excites me is that we’re focused on healthy older adults,” said Tyler Toueg, who co-led the study’s design, in a press release. “Most clinical trials with older adults are focused on people who already have a diagnosis. We’re asking whether we can actually promote positive outcomes in older adults who are healthy.”
Called PLASTICITY, the trial could also open a rare window into how a psychedelic experience reshapes healthy brain networks. And because the drug alters our sense of self, psilocybin could help researchers probe the ways in which the brain constructs reality.
“I’m very interested in psilocybin as a potential mental health treatment, but I’m also interested in it as a way to shed light on these central mysteries in neuroscience and psychology,” said study designer Michael Silver.
Psychedelic research was highly restricted for decades. But advocates, including the non-profit Multidisciplinary Association for Psychedelic Studies, have steadily pushed to reopen the field, arguing that these drugs might keep mental health symptoms at bay.
Early results helped usher psychedelics into the mainstream. In 2023, a randomized, placebo-controlled trial found that a single dose of psilocybin, paired with therapy, eased depression. Oregon later approved supervised psilocybin therapy—though the drug remains federally illegal in the US—and Australia became the first country to greenlight it for depression and post-traumatic stress disorder. More recently, two late-stage studies reported strong effects in severe depression, potentially paving the way for FDA approval.
Scientists still don’t fully understand how psilocybin works in the brain. But there are hints. The drug appears to rapidly reorganize the connections between brain cells, particularly in the hippocampus, a region of the brain central to learning, memory, and navigation.
Neurons constantly change their connections in a process called plasticity that encodes experiences into neural networks, allowing the brain to process information, learn, and lock in memories. In youth, these connections are flexible and expansive. But with age and in conditions like depression, the brain’s flexibility wanes.
The birth of new neurons, or neurogenesis, also contributes to plasticity. Neurogenesis only occurs in two brain regions, one of which is the hippocampus. Although whether it actually takes place in humans is controversial, it is strongly linked to learning, memory, and emotion, and it declines in both psychiatric disorders and aging.
Psilocybin may reset brain plasticity to a more youthful state.
In rats modeling depression, for example, a study showed the drug shifted dark moods into behavior that was more exploratory and engaging. Where traditional antidepressants, like Prozac, tend to blunt symptoms, psilocybin seems to overwrite entrenched negative patterns. This suggests deeper circuit-level changes.
In another study, the drug reopened a critical window for learning in mice. During adolescence, the brain is highly plastic, but it begins to stiffen in adulthood. Psilocybin temporarily restored malleability and changed the mice’s social behavior. In some brain regions, the drug increased sensitivity to oxytocin, the so-called “love hormone.” The authors suggested the drug induced a state called metaplasticity, in which neurons are more likely to respond to oxytocin and other regulators to rewire, form new connections, and grow their branches.
Psilocybin’s effects may extend beyond the brain. Depression, chronic stress, and the immune system are tightly linked. In a third study, researchers identified a brain-spleen connection that drives fear and anxiety. Psilocybin suppressed inflammatory immune cells associated with brain inflammation and dampened anxiety-like behavior in stressed mice, even under threat.
Both effects—dialing up plasticity and lowering inflammation—make psilocybin an intriguing way to potentially counter changes in the aging brain.
“We know that with age, we lose synaptic connections, especially in certain brain regions like the hippocampus and prefrontal cortex,” said Toueg. “There’s a lot of overlap between the mental states that psychedelics influence and those associated with successful aging.”
The PLASTICITY trial is designed to test whether psilocybin can produce lasting changes in neuroplasticity in healthy adults aged 60 to 85. Participants will first undergo assessments of cognition, visual perception, and brain structure using advanced MRI techniques.
Diffusion MRI scans will focus on the hippocampus to capture microscopic changes in its structure. Functional MRI will focus on brain activity as participants perform learning and memory tasks, offering a dynamic view of how activity shifts after dosing.
The study will also see if psilocybin increases vagus nerve activity, which has been linked to better stress recovery. Participants will complete detailed surveys about the experience ranging from emotional responses, like wonder, to potential shifts in outlook and social cognition.
“Things like depression, anxiety, stress and rumination are all associated with worse aging outcomes,” said Toueg. “Things like having purpose in life, emotional regulation, and awe are all associated with more successful aging.”
The trial began enrolling participants in November last year. Two volunteers have already completed the tests, and the team aims to dose 20 people by the end of 2026.
Although psilocybin trials are now widespread, older adults remain underrepresented. One estimate suggests just 1.4 percent of participants are 65 or older, despite potentially being among those most likely to benefit from interventions that enhance plasticity.
“This study allows us to directly test whether the promising findings from animal models translate to older humans and to generate data that will inform future research on aging, cognition, and mental health,” said Silver.
Toueg agrees. “I think that no matter what we find, this study will have implications for how we think about intervening in the aging brain,” he said.
The post Can Psychedelics Reboot Aging Brains? We’re About to Find Out appeared first on SingularityHub.
2026-06-20 22:00:00
A Startup Claims It Broke Through a Bottleneck That’s Holding Back LLMsWill Douglas Heaven | MIT Technology Review ($)
“According to Subquadratic, it has developed a new kind of LLM, called SubQ, that is faster and cheaper and uses a lot less energy than any other model on the market. The company also claims that SubQ is able to process up to 12 times as much text at once than most other models, allowing it to carry out a range of data-heavy tasks, such as analyzing hundreds of documents or entire code bases.”
The Next Humanoid Robot Might Not Look Human at AllRobert Hart | The Verge
“The next humanoid robot might not have a head. It might not have legs. It might even sit on a wheeled base and fold down like a deck chair. But, as Genesis AI puts it, ‘humanoid robots don’t need to look human.’ …Genesis says Eno is designed ‘around human capability’ rather than human appearance and is intended as a fully ‘general-purpose’ robot rather than a machine built around a single task, like folding laundry.”
Chilling the Body With Drugs Could Limit Brain Damage From StrokeAlice Klein | New Scientist ($)
“A combination of two drugs used to treat hay fever and psychosis cooled down the core body temperature of mice and monkeys, reducing brain damage after a stroke. These medications have also undergone preliminary testing in people, and will now be evaluated in a follow-up clinical trial.”
A Court Has Ruled That Google Is Liable for False Statements Generated by AI OverviewsFernanda González | Wired ($)
“The authorities found that, unlike traditional search engines, which merely display lists of links with statements made by third parties, Google’s tool produced ‘independent, new, and substantial statements’ based on a misinterpretation of information available on the internet. …Google is the only entity with the ability to modify the technology underpinning its AI-generated summaries and, therefore, ‘must be held accountable.'”
Estonia Is Giving AI Agents ‘Personal Identification Codes’Webb Wright | Gizmodo
“Estonia is trying to bring some law and order to the Wild West that is the world of AI agents. The small Baltic nation plans to assign each AI agent a ‘personal identification code,’ hoping to track what agents do across the internet and identify the people or companies behind them.”
Why the Human Genome’s Tangled Physicality May Confound AIPhilip Ball | Quanta Magazine
“[The AI] approach is likely to be useful, but for those who crave real understanding of how the genome, and ultimately life itself, works, a computational black box will never suffice. And perhaps more to the point, the genome might not submit to the kind of straightforward input-output approach that such AI models ultimately assume. That’s because the genome is no blueprint or algorithm. It is something else.”
The Inevitable Weakness of MetricsBryan Gardiner | MIT Technology Review ($)
“What I think many of us miss—what I know I certainly missed—is that there are always trade-offs when you try to distill something important down to a data point. When we turn to metrics to understand ourselves, our social world, and culture as a whole, they will never come close to capturing what matters. Even worse, they’ll often actively obscure it.”
Just 16% of Americans Believe AI Will Positively Impact Society, Pew Poll FindsMatt Novak | Gizmodo
“Half of adult Americans use AI chatbots, with a quarter using them daily, according to new polling from Pew Research released Wednesday. That’s up from 33% of Americans who used AI chatbots in the summer of 2024. But a small minority, 16%, believe AI will have a positive impact on society.”
Sooner Than Expected? Useful Quantum Error Correction Promised for 2028.John Timmer | Ars Technica
“‘By 2028, we will bring Libra, a Megaquop-scale device, capable of executing one million quantum operations over hundreds of logical qubits, to our customers, enabling first scientific applications in quantum chemistry, high-energy physics, and materials simulation that are beyond the reach of classical and Noisy Intermediate-Scale Quantum (NISQ) computers today,’ Amazon’s statement said.”
Brain-Computer Interface Trials Are Taking OffJessica Hamzelou | MIT Technology Review ($)
“Over the past couple of years, the number of BCI trial volunteers has soared. This year, China became the first country to approve a BCI for medical use. Advances in technology are allowing engineers to provide more features than ever. BCI research is properly taking off.”
Why Waymo’s Driverless Taxis Won’t Be on Your Streets Anytime SoonDavid McCabe | The New York Times ($)
“Waymo is increasingly facing political roadblocks as it tries to roll out its self-driving taxis powered by artificial intelligence nationwide. After early successes winning over politicians in California—its home state—and elsewhere, Waymo has stumbled in unlocking some of the biggest markets in the country.”
The post This Week’s Awesome Tech Stories From Around the Web (Through June 20) appeared first on SingularityHub.
2026-06-20 04:57:26
Coal’s share has nearly halved over the last five years, while solar’s has more than doubled. But tariffs and permitting delays could slow growth in the years ahead.
The transition away from fossil fuels is often framed as a long-term process, but recent data suggests the shift is already happening. Solar power has now crossed a major threshold in the US, surpassing coal in the electricity generation mix for the first time.
Despite the Trump administration’s attempts to drive a coal revival, it has been steadily losing ground to other energy sources in recent years, squeezed out by cheap natural gas and rapidly falling renewable energy prices. Solar power, in particular, has been on a tear as prices drop exponentially.
Last month, the two lines finally crossed. Solar supplied 12.8 percent of US electricity in May, edging past coal’s 12.2 percent share to become the country’s third-largest source of power behind natural gas and nuclear, according to recent data from energy think tank Ember.
“Overtaking coal for the first month on record shows just how far solar has come, from a niche contributor to the third-largest and fastest-growing source of power in the US electricity system,” Nicolas Fulghum, senior data analyst at Ember, said in a press release.
The transition is as much about coal’s waning importance in the US energy system, as it is about solar’s growth. Coal’s share has nearly halved in five years, falling from 19.7 per cent in May 2021 to 12.2 percent today and hitting an all-time monthly low in April.
Over the same period, solar’s share of electricity generation has more than doubled from 5.4 percent to 12.8 percent. And it hit an all-time high of 45.5 terawatt-hours in May, up 17 percent compared to the same month last year and above the previous record set in July 2025. Ember gets its data from the US Energy Information Administration.
The industry does face some headwinds though. A separate report from the Solar Energy Industries Association and analytics firm Wood Mackenzie found that the 7.8 gigawatts of new solar capacity added in the first quarter of 2026 is a 27 percent decline compared to the previous year.
This is partly due to regular seasonal patterns for the industry, says the report, but was also thanks to the expiry of a tax credit for residential installations and trade restrictions and tariffs targeting imported solar components from Asia initiated by the Trump administration. The government has also made it more difficult to get permits for new projects.
But despite the apparent slowdown, solar and battery storage together accounted for 91 percent of all new electricity-generating capacity added to the grid in the first quarter of the year. And the number of new utility-scale projects signed in the first quarter hit 6.3 gigawatts, a rise of 15 percent. Tellingly, the SEIA notes that states President Trump won in 2024 make up 74 percent of new solar capacity installed in that period.
“In a world of fluctuating fuel prices, energy buyers have made it clear that they want the security, low cost, and speed of solar and storage,” Darren Van’t Hof, interim president and CEO at SEIA, said in a press release.
“Impeding the only sector that is actively building new power is a reckless gamble that will only drive electricity bills higher. The stakes are simply too high for Washington’s permitting gridlock to continue.”
As a result of these barriers, Wood Mackenzie’s five-year forecast predicts that annual additions will plateau around 43 gigawatts. That’s still an impressive pace of installation, but also a significant slowdown from the breakneck growth seen over the last couple of decades. So, while solar may have knocked one fossil fuel competitor off the podium, without a change in energy policy it may struggle to maintain its impressive momentum.
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