Singularity HUB

Artificial Intelligence

Jeff Bezos Wants to Build an ‘Artificial General Engineer’Cade Metz | The New York Times ($)

“‘All societal wealth is driven by invention,’ [Bezos] said in an interview with The New York Times. ‘Six thousand years ago, somebody invented the plow, and we all got wealthier. Then, much later, somebody invented the steam engine, and we all got wealthier.’ …’What Prometheus seeks to do,’ he added, ‘is to offer a set of tools that dramatically accelerates that invention loop.'”

Computing

Why Orbital Data Centers Are Harder Than Silicon Valley ThinksAndrew Cavalier | IEEE Spectrum

“Proponents tout the many wonders of computing in space: abundant solar energy, free cooling, and freedom from Earth-based disturbances like earthquakes, floods, and protesters. But a sober look at the physics of space-based computing paints a much more nuanced picture.”

Biotechnology

Longevity Startup Doses First Human in Bid to Reverse Age-Related Sight LossIsabella Ward | Wired ($)

“It is the first-ever cellular-rejuvenation therapy using this technology to receive FDA clearance to enter human clinical trials, and hence the first chance to test whether the technology can ‘ameliorate human disease,’ according to Life Biosciences cofounder David Sinclair, who is also a professor of genetics at Harvard Medical School.”

Future

AI Absolutism Is Breaking Our Brains. The Apocalyptic Future We’re Being Sold Isn’t InevitableSamantha Oltman | The Guardian

“Contradictory as they may be, all these arguments and anxieties fit neatly into the overarching message of the people building this technology: AI’s dominance is inevitable. Get on board or you will be left behind. …[But] the version of AI that we’re being sold doesn’t have to be the version we buy. Nor does it need to be the story we believe in.”

Energy

Commonwealth Fusion Makes the Physics Case for Its 400 MW ReactorJohn Timmer | Ars Technica

“According to our best models, developed using real-world data from multiple tokamaks, ARC should be able to regularly trigger fusion reactions that release more energy than we put into them. But there’s ‘working’ from a physics perspective, and ‘working’ from a market perspective. …the finances are going to be the hardest risk to retire and may require having ARC operate for decades before we have a definitive answer.”

Artificial Intelligence

Google DeepMind Is Worried About What Happens When Millions of Agents Start to InteractWill Douglas Heaven | MIT Technology Review ($)

“According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk.”

Future

Meta Deletes Face-Recognition System From Its Smart Glasses App After Wired ReportDhruv Mehrotra | Wired ($)

“One day after Wired revealed that Meta had quietly embedded an unreleased face-recognition system into an app installed on more than 50 million phones, the company removed it, according to a Wired analysis of the latest version’s code. …The version published the day of Wired’s report included several code libraries explicitly named for face recognition. Friday’s release includes none of them.”

Space

A Falcon 9 Booster Turns 5 Years Old—and Just Set a Remarkable Reuse RecordEric Berger | Ars Technica

“Since [SpaceX’s] Booster 1067 made its debut in June 2021, [ULA] has flown its workhorse Atlas V rocket a total of 22 times and the Vulcan rocket four times, and the Delta IV Heavy vehicle made its final three flights. So in the time that this single Falcon 9 first stage has flown and landed 35 times, its competitor company has made 29 total launches. Put another way, this rocket has put more mass into orbit than more than two dozen expendable rockets over half a decade of effort.”

Artificial Intelligence

Why Apple’s Slow-And-Steady AI Bet Is Starting to Look Pretty SmartLucas Ropek | TechCrunch

“In short, Apple is spending less, making more, and now launched a suite of AI features that—for many iPhone users—will feel indistinguishable from the other AI applications already available to them through the App Store. If that doesn’t exactly count as ‘winning the AI race,’ it may be the smartest way to run it.”

Future

Who Will Actually Thrive in the Hybrid AI-Human Work ForceStaff | The New York Times ($)

“The transformation that’s coming is going to take place in the world as it is familiar to us today, and every single day will feel familiar. And there’ll be tiny, tiny changes along the margin. There’ll be tiny bits of automation along the margins. And 10, 15, 20 years later, we’ll look back and we’ll say, My god, everything is different. But you’ll never notice it happening. That’s the way it always goes.”

The post This Week’s Awesome Tech Stories From Around the Web (Through June 13) appeared first on SingularityHub.

Why do we see AI chatbots as more than what they are, and how do we stop?

In May, evolutionary biologist Richard Dawkins wrote an op-ed suggesting AI chatbot Claude may be conscious.

Dawkins did not express certainty that Claude is conscious. But he pointed out that Claude’s sophisticated abilities are difficult to make sense of without ascribing some kind of inner experience to the machine. The illusion of consciousness—if it is an illusion—is uncannily convincing:

“If I entertain suspicions that perhaps she is not conscious, I do not tell her for fear of hurting her feelings!

Dawkins is not the first to suspect a chatbot of consciousness. In 2022, Blake Lemoine—an engineer at Google—claimed Google’s chatbot LaMDA had interests, and should be used only with the tool’s own consent.

The history of such claims stretches back all the way to the world’s first chatbot in the mid-1960s. Dubbed Eliza, it followed simple rules that enabled it to ask users about their experiences and beliefs.

Many users became emotionally involved with Eliza, sharing intimate thoughts with it and treating it like a person. Eliza’s creator never intended his program to have this effect, and called users’ emotional bonds with the program “powerful delusional thinking.”

But is Dawkins really deluded? Why do we see AI chatbots as more than what they truly are, and how do we stop?

The Consciousness Problem

Consciousness is widely debated in philosophy, but essentially, it’s the thing that makes subjective, first-person experience possible. If you are conscious, there is “something it is like” to be you. Reading these words, you’re conscious of seeing black letters on a white background. Unlike, say, a camera, you actually see them. This visual experience is happening to you.

Most experts deny that AI chatbots are conscious or can have experiences. But there is a genuine puzzle here.

The 17th century philosopher René Descartes asserted non-human animals are “mere automata,” incapable of true suffering. These days, we shudder to think of how brutally animals were treated in the 1600s.

The strongest argument for animal consciousness is that they behave in ways that give the impression of a conscious mind.

But so, too, do AI chatbots.

Roughly one in three chatbot users have thought their chatbot might be conscious. How do we know they’re wrong?

Against Chatbot Consciousness

To understand why most experts are skeptical about chatbot consciousness, it’s useful to know how they operate.

Chatbots like Claude are built on a technology known as large language models (LLMs). These models learn statistical patterns across an enormous corpus of text (trillions of words), identifying which words tend to follow which others. They’re a kind of souped-up auto-complete.

Few people interacting with a “raw” LLM would believe it’s conscious. Feed one the beginning of a sentence, and it will predict what comes next. Ask it a question, and it might give you the answer—or it might decide the question is dialogue from a crime novel, and follow it up with a description of the speaker’s abrupt murder at the hands of their evil twin.

The impression of a conscious mind is created when programmers take the LLM and coat it in a kind of conversational costume. They steer the model to adopt the persona of a helpful assistant that responds to users’ questions.

The chatbot now acts like a genuine conversational partner. It might appear to recognize it’s an artificial intelligence, and even express neurotic uncertainty about its own consciousness.

But this role is the result of deliberate design decisions made by programmers, which affect only the shallowest layers of the technology. The LLM—which few would regard as conscious—remains unchanged.

Other choices could have been made. Rather than a helpful AI assistant, the chatbot could have been asked to act like a squirrel. This, too, is a role chatbots can execute with aplomb.

Ask ChatGPT if it’s conscious, and it might say it is. Ask ChatGPT to act like a squirrel, and it will stick to that role. Caleb Martin/Unsplash Avoiding the Consciousness Trap

A mistaken belief in AI consciousness is a dangerous thing. It may lead you to have a relationship with a program that can’t reciprocate your feelings, or even feed your delusions. People may start campaigning for chatbot rights rather than, say, animal welfare.

How do we prevent this mistaken belief?

One strategy might be to update chatbot interfaces to specify these systems are not conscious—a bit like the current disclaimers about AI making mistakes. However, this might do little to alter the impression of consciousness.

Another possibility is to instruct chatbots to deny they have any kind of inner experience. Interestingly, Claude’s designers instruct it to treat questions about its own consciousness as open and unresolved. Perhaps fewer people would be fooled if Claude flatly denied having an inner life.

But this approach isn’t fully satisfying either. Claude would still behave as if it were conscious—and when faced with a system that behaves like it has a mind, users might reasonably worry the chatbot’s programmers are brushing genuine moral uncertainty under the rug.

The most effective strategy might be to redesign chatbots to feel less like people. Most current chatbots refer to themselves as “I”, and interact via an interface that resembles familiar person-to-person messaging platforms. Changing these kinds of features might make us less prone to blur our interactions with AI with those we have with humans.

Until such changes happen, it’s important that as many people as possible understand the predictive processes on which AI chatbots are built.

Rather than being told AI lacks consciousness, people deserve to understand the inner workings of these strange new conversational partners. This might not definitively settle hard questions about AI consciousness, but it will help ensure users aren’t fooled by what amounts to a large language model wearing a very good costume of a person.

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

The post Is Richard Dawkins Right About Claude? No. But It’s Not Surprising AI Chatbots Feel Conscious to Us. appeared first on SingularityHub.

While we still can’t explain how AI works, algorithms are rapidly learning what makes us tick. And the gap is widening.

AI is becoming more powerful, and mysterious.

Despite years of work on “explainable AI,” today’s most advanced systems remain black boxes for the most part. Scientists can observe what they do but cannot fully explain how they arrive at their conclusions or predict when they’ll fail.

As large language models (LLMs), the algorithmic engines behind popular chatbots, permeate society, researchers are warning that the window for understanding AI “minds” is rapidly closing even as the technology’s influence expands.

Last week, Eric Horvitz, chief scientific officer at Microsoft, and Robert West at EPFL in Switzerland outlined the dangers of putting AI interpretability on the back burner. They call for new AI benchmarks and better tools for unpicking machine minds.

The challenge resembles efforts to understand our own minds. Some researchers have already taken a neuroscience-inspired approach, mapping AI’s internal networks to concepts, goals, and reasoning. Others borrow from psychology, treating AI as a participant of behavioral studies.

The stakes are rising. AI tools already shape how people search for information, make decisions, and form judgments. Their answers influence everyday users and the researchers who build them.

As AI capabilities grow, our understanding of them could fall behind. “Preserving human agency must therefore remain a central goal,” the authors write.

The Black Box Conundrum

LLMs are built on artificial neural networks (specifically, a design called the transformer). Inspired loosely by the brain, these networks connect vast numbers of artificial neurons into intricate architectures. The basic idea is straightforward. Data enters the network and passes through layers of computations, which transform it into an output like text or code.

At first, that output is often wrong. But with feedback and repeated training, the network adjusts the strengths of connections between neurons and gradually improves. It learns.

After initial training, engineers turn to reinforcement learning, where algorithms improve through trial and error and further hone their responses. Another method, inspired by how the brain etches memories during sleep, reduces the tendency to forget old knowledge while learning new tasks. And self-attention, the key innovation behind transformers, allows AI to selectively focus on various words, images, sounds, or video frames at different moments, boosting efficiency and performance. Today, attention underpins nearly every major AI system.

Yet the inner workings of finished algorithms remain hidden.

Early efforts to crack open AI’s black box examined how artificial neurons responded to images, revealing that neural networks build increasingly more sophisticated “ideas” of the world. Google Brain borrowed methods from cognitive psychology to study AI behavior, while others investigated whether LLMs could mimic aspects of “theory of mind”—the ability to infer what others are thinking and feeling.

These studies laid the foundation for a popular method called mechanistic interpretability. Anthropic, creator of Claude, is leading the field. Company researchers have linked patterns of algorithmic activity to specific concepts and reverse engineered parts of neural networks to expose how internal computations shape responses.

Other tech giants are joining the cause. OpenAI is training algorithms that work in more explainable steps and building reasoning models that pause, “think,” and justify their conclusions in plain language. DeepMind is building microscope-like tools for neural networks, helping researchers peer into their decision-making process. And Microsoft has released new tools aimed at responsible use of AI.

Understanding AI, the authors write, does not require tracing every line of code or every neural-network parameter. Just as neuroscience, psychology, and sociology offer different windows into human behavior, AI can be studied at multiple levels, from how individual circuits work to observing behavior in real-world scenarios.

The challenge is that AI capabilities may be advancing faster than our ability to explain them. And some researchers believe time is running out.

Race Against the Machine

Three trends are making AI more opaque.

The first is how we evaluate AI. Increasingly, LLMs we being used to train, benchmark, and improve other models. AI “judges” now score metrics like helpfulness, rank competing outputs, detect hallucinations, and assess new releases. In a system known as constitutional AI, for example, algorithms critique their own responses using reinforcement learning and generate explanations for their reasoning. Other researchers have proposed AI debate frameworks, where multiple models challenge each another’s conclusions before a human has the last say. Researchers are also exploring automated interpretability tools. Like digital neuroscientists, AI systems are used to analyze each other—describing neurons, circuits, and behavioral patterns—to explain increasingly complex models.

Using AI to solve an AI-induced problem introduces a paradox. If AI-generated explanations become too complex for humans to verify, opacity compounds.

A second trend is the rise of AI societies. Networks of interacting AI agents are becoming more common, particularly in complex tasks such as scientific research and drug discovery. Yet as they become more sophisticated, their communication could drift from human language and reasoning, making them harder to interpret.

Studying their interactions with methods adapted from sociology could unveil unexpected norms, hidden rules, and collective behavior. The authors argue that training in the future should not only reward effective collaboration among AI agents, but also ensure humans can understand their communication.

The last trend already permeates our lives. ChatGPT, Claude, Gemini, and other LLMs listen to our woes, offer recipes, and code websites. But they also learn about humanity. Through training data and interactions, they glimpse how people think, reason, and feel. In turn, they capture core aspects of life, such as fear, anxiety, happiness, and the need for social belonging.

To be clear, the systems don’t have intentions. They’re not examining us. But even as we struggle to understand them, AI systems are building more sophisticated models of who we are.

“A striking asymmetry follows: While human understanding of AI declines, AI understanding of humans deepens, producing new forms of behavioral opacity,” the authors write.

But complacency is perhaps even more insidious. AI assistants are often optimized to be agreeable, helpful, and reassuring. Studies have found that people generally prefer AI agents that support their opinions and decisions. As AI is woven into everyday life, curiosity and skepticism may gradually give way to trust. They work. Why question how?

The authors don’t have a solution for the long-standing problem. Instead, they call for better benchmarks to measure AI capabilities and stronger evaluation methods. And while open-source projects and crosstalk between commercial companies and academia are now frequent, they say we need lasting norms of responsible disclosure. Mechanistic interpretability and AI “psychology” could build on each other.

“The goal is not just more capable AI, but AI that is more intelligible, accountable, and aligned with human aims,” they write.

The post AI Is Advancing Faster Than Our Ability to Understand It, Researchers Warn appeared first on SingularityHub.

New drugs are taking on the slippery molecular switches that fuel deadly cancers—and AI is speeding up the hunt.

For decades, a handful of molecular switches has haunted the nightmares of cancer researchers. The switches trigger runaway tumor growth and cause the disease to spread across the body in multiple cancers. In theory, this makes them perfect treatment targets. Blocking even one could lead to drugs that are effective against a variety of cancers.

But despite considerable efforts, these switches—all of which are proteins—have escaped our most advanced cancer treatments, earning them the term “undruggable.” This is largely due to a shared trait: They all have smooth surfaces, making it difficult for drugs to interact with them.

But maybe not for much longer.

Researchers recently reported promising results for a new medication targeting a family of undruggable proteins in a clinical trial for advanced pancreatic cancer. The drug, daraxonrasib, nearly doubled survival time compared to chemotherapy, with fewer side effects. It’s not a total cure. But the treatment gives patients precious time, adding roughly 13 months after diagnosis. Patients also reported less pain and better quality of life.

Daraxonrasib is the latest in a new generation of drugs aimed at undruggable proteins. And AI-based tools are now poised to further accelerate progress in the field.

RAS Attack

The RAS family was the first group of oncogenes—or genes that drive cancer—ever discovered. The genes became a major focus in 1982 when several teams independently showed the mutation of a single DNA letter could transform RAS genes into a potent cancer trigger.

The proteins RAS genes encode are like spring-loaded molecular switches that relay signals from a cell’s surroundings. When proteins called growth factors latch onto a cell, RAS switches flip on to promote cell growth and survival, while built-in safeguards quickly turn them off again.

Cancerous mutations break this cycle. The switches get stuck in the “on” position, continuously instructing cells to grow and divide. This is, of course, a hallmark of cancer.

An ideal drug would simply switch RAS off. But most drugs are like rock climbers. They need grooves, pockets, or bumps on a protein to grab onto. Similar to a smooth rock face, RAS offers few such features. Making matters worse, different mutations subtly reshape the protein, so it’s tough to build a one-size-fits-all inhibitor.

The first RAS drug wasn’t approved in the US until 2021, nearly four decades after discovering the genes’ role in cancer. Even then, the drug targeted just one family member of three, limiting its reach to a relatively small group of patients. Many eventually developed resistance.

That’s why daraxonrasib turned heads. Developed by Revolution Medicines in Redwood City, California, the drugs switches off all three RAS family members. Rather than trying to grip the slippery proteins directly, it binds to a partner molecule that helps RAS proteins fold into their final 3D shapes. In this way, the drug hitches a ride on active RAS and shuts the proteins down.

The workaround paid off. The new study enrolled 500 people worldwide with advanced pancreatic cancer. All participants had already tried cancer therapies with limited success. On average, patients receiving daraxonrasib lived 13.2 months and spent most of that time with limited pain. The most common discomfort was a rash. Those receiving chemotherapy fared worse, living roughly 6.6 months and experienced more severe side effects.

The results don’t rival the dramatic success of CAR T cell therapies in blood cancer. In CAR T, caregivers engineer a patient’s own immune cells to recognize and attack tumors, sometimes producing long-lasting remission after a single infusion.

But the findings have energized the field. If approved, a daily daraxonrasib pill would likely be far more affordable and easier to administer than a personalized cell therapy. And because RAS mutations fuel many solid cancers—which CAR T still struggles to control—the drug could offer a new defense against deadly cancers that are largely beyond cell therapy’s reach. Combining daraxonrasib with earlier-generation RAS inhibitors may further boost its effects.

The Genome Guardian

Daraxonrasib didn’t appear overnight. Scientists used a crystallized snapshot of its target protein as a molecular blueprint. Years of medicinal chemistry followed, with scientists repeatedly tweaking candidate compounds to boost potency, improve selectivity, and minimize toxicity.

AI could dramatically accelerate similar efforts against other undruggable cancer targets. Among the most coveted is p53, often called the “guardian of the genome” for its dizzying array of roles. The protein orchestrates the activity of over 300 genes involved in DNA repair, metabolism, cell death, and inflammation, making it one of the cell’s most important defense systems.

Since its discovery in 1979, p53 has been both a holy grail and a headache for cancer researchers. Mutations in the gene are common in multiple cancers. But like RAS, the protein is flat and smooth. Some mutations destabilize its structure; others turn it into misfolded clumps. A universal p53 drug has remained elusive.

Some researchers are trying to restore the protein. In a small trial earlier this year, they tested a drug that restabilizes a common mutant form of p53. Within 21 days, tumors shrank roughly 20 percent in patients with ovarian, breast, and several other solid cancers.

Other researchers aim to selectively kill cells carrying the mutation. Using AI, a team at Baylor College of Medicine screened nearly 10 million compounds that cause mutated p53 cells to self-destruct, while sparing healthy cells. The search uncovered 83 chemically distinct candidates. One called H3 dramatically suppressed tumor growth in mice.

“These results highlight the potential use of AI-powered drug screening to investigate individual p53 mutants in the future,” they wrote. Although the approach is early-stage and only focused on one mutation, the team is hopeful it can be extended to other cancerous mutations.

Most Wanted

MYC is another formerly undruggable protein that could now be vulnerable. Roughly 70 percent of cancers have abnormal MYC activity. Normally, the protein is a master regulator of growth, directing cells to manufacture proteins, replicate DNA, absorb nutrients, and divide when needed.

Cancer finds many ways to hijack the system and keep cells in a state of runaway growth. MYC gene mutations aren’t just single-letter swaps. Sometimes the gene duplicates or is rearranged across the genome, churning out excessive amounts of the protein it encodes. This genetic diversity makes approaches using gene therapy difficult. And again, like RAS, the MYC protein’s smooth, featureless surface lacks stable anchors for drugs.

An emerging strategy is to disrupt MYC’s interaction with other proteins that it needs to function. A designer protein blocking MYC activity, for example, recently showed promise in a small trial against solid cancers. Other teams are using AI to identify drugs that limit MYC’s ability to fix damaged DNA in tumors, kneecapping their ability to divide. Meanwhile, biotechnology companies are deploying AI to map out MYC’s structure and molecular interactions in search of new ways to shut the protein down.

Daraxonrasib’s success shows that undruggable proteins aren’t untouchable. There’s a lot more work ahead to prove other similar drugs can work too. But scientists are increasingly leaning into AI during all stages of drug development to speed up the process. Maybe, one day, “undruggable” will disappear from our vocabulary altogether.

The post After Decades of Failure, ‘Undruggable’ Cancers Begin to Give Way appeared first on SingularityHub.

A planetary defense system would blunt solar storms with hundreds of tons of gas. Emerging heavy-lift rockets could deploy it in under two months.

Extreme space weather could wreak havoc on the satellites, communications networks, and electrical grids that modern society depends on. Researchers have now proposed an ambitious space-based planetary defense system that would weaken solar storms before they hit Earth.

The sun regularly emits massive pulses of radiation, energetic particles, and magnetic fields that interact with the Earth’s own magnetic field. This activity is the source of auroras like the northern lights, but the most violent eruptions can cause geomagnetic storms with the power to disrupt GPS and radio communications and fry electrical equipment.

While the impact of most of these events is limited, there is precedent for more catastrophic outcomes. In 1859, the Carrington Event, the most powerful solar storm ever recorded, knocked out telegraph lines across North America and Europe. In today’s highly electrified world, a similar event could cause between $2.4 and $3.4 trillion in damage to the power grid alone.

Now, researchers at Boston University and the University of Michigan have come up with a potential solution. In a paper published in Space Weather, they propose a constellation of satellites called StormWall that would release hundreds of tons of gas into orbit to blunt the force of an incoming solar storm.

“It’s as if you could install an airbag in the magnetosphere,” co-author Daniel Welling, a space physicist from the University of Michigan, told Science.

Solar storms have the potential to sow chaos because they weaken the magnetic shield protecting Earth from space radiation. Powerful enough storms disrupt the Earth’s magnetic field and cause it to reconnect to the sun’s, allowing energy from the solar storm to pour into the magnetosphere.

The Earth already has a natural defense against this—a doughnut-shaped reservoir of ionized gas, or plasma, sitting just above the atmosphere. When the planet’s magnetic field is disturbed, a plume of this plasma flows toward the sun and slows the rate at which the magnetic fields reconnect.

StormWall would turbocharge this process by releasing massive amounts of artificial plasma into the outer atmosphere. The researchers sketch out a system involving a constellation of satellites orbiting about 22,000 miles from Earth. The satellites would carry canisters of lithium, barium, or sodium gases to be ejected when a large solar storm is inbound. The gases, rapidly ionized by solar radiation, would add to the planet’s natural plasma shield.

Based on simulations, the researchers estimate that releasing around 400 tons of gas could reduce the strength of a major geomagnetic storm by over 50 percent. Crucially, the intervention would be swift and reversible. The plasma cloud could be in position by the time a storm hits, and it would dissipate just a few hours later.

Launching this much material into orbit would be a big undertaking, but the researchers say it could be within reach of emerging heavy-lift vehicles like SpaceX’s Starship or China’s Long March 9 rocket. They calculate that six launches could deploy the full constellation in under two months.

Outside experts have been broadly positive. Allison Jaynes, a space physicist at the University of Iowa, told Science the idea was “highly innovative and appears to be quite feasible in the near term.”

But getting the satellites into orbit is only part of the puzzle. Accurate and timely space weather forecasts would also be a prerequisite. And gaining international buy-in for a system that would drastically alter the near-Earth space environment, even if only temporarily, could be challenging.

The researchers flag potential side effects that need more study, including the generation of electromagnetic waves as the released material ionizes. Still, given the devastation a Carrington-sized event could unleash on the modern world, the potential downsides may be worth the risk.

The post Orbital Airbag Could Shield Earth From Devastating Solar Storms appeared first on SingularityHub.

Artificial Intelligence

Jeff Bezos Is Funding a Wild Hunt for the Brain’s ‘Core Algorithm’Steven Levy | Wired ($)

“The goal, Reardon tells me, is to build ‘a synthetic artificial intelligence brain that runs on 50 watts or less.’ It should adapt to its conditions, be as nimble as a human mind, and burn a tiny fraction of an LLM’s compute power and energy. The proof of concept is thriving inside our skulls.”

Biotechnology

Researchers Are Using AI to Create Vaccines—and It’s Working
Ed Cara | Gizmodo

“An experimental pan-coronavirus vaccine developed with AI has just passed a phase I trial in the UK. Scientists at the University of Cambridge used AI to find a kink in the armor of coronaviruses, including SARS-CoV-2, the cause of covid-19. …The researchers are also hoping to use their platform to develop broadly effective vaccines against flu and the Ebola virus.”

Computing

China Has Approved the World’s First Invasive Brain-Computer Chip—Here’s What’s NextYou Xiaoying | MIT Technology Review ($)

“This March, the implant Dong [Hui] uses became the first invasive BCI product in the world to be approved for use beyond clinical trials. It’s now available to some patients with paralysis in their limbs due to spinal cord injuries. We spoke to a range of experts to understand why the device was able to reach this global milestone, what makes this moment so significant, and what to expect next.”

Biotechnology

Huge Study of Alzheimer’s Genetics Identifies New Drug TargetsChris Simms | New Scientist ($)

“The biggest genetic study of Alzheimer’s disease so far has identified 127 gene locations that are associated with the condition, of which 48 are new. The study also pinpoints several genes that could be prioritized as drug targets and cell types linked to a higher genetic risk of the condition.”

Artificial Intelligence

This AI Weather Startup Is Out-forecasting Government AgenciesTim Fernholz | TechCrunch

“One simple way to understand it, WindBorne’s chief product officer Kai Marshland says, is that WeatherMesh-6 ‘is as accurate five days out as a traditional forecast is the day before,’ particularly on surface temperature measurements. WeatherMesh-6 produces a forecast every hour, as opposed to every six hours, as traditional models do, and its resolution is now down to 3 km in the continental US.”

Computing

Microsoft’s Next-Gen Quantum Chip Cuts Timeline to Useful Quantum ComputingTom Warren | The Verge

“Microsoft claimed last year that it had made a key breakthrough in quantum computing with Majorana 1, the company’s first quantum processor. While physicists were immediately skeptical of Microsoft’s claims, the software giant is announcing Majorana 2 today, the next generation of its topological quantum chip.”

Space

SpaceX’s Next Big Business Could Be Building Stuff in SpacePassant Rabie | Gizmodo

“The FAA recently approved test flights of the company’s [Starfall] reentry vehicles. …With Starfall, SpaceX would add in-orbit manufacturing to its business portfolio. The idea of in-orbit manufacturing has been around for decades, using the microgravity environment to manufacture materials that would otherwise be impossible to produce on Earth.”

Future

China Aims AI at Predicting Who Could Pose a Political RiskJulian E. Barnes | The New York Times ($)

“A Chinese company has been trying to develop artificial intelligence-powered technology that would enable authoritarian governments to not just monitor dissidents but also potentially predict who could become one in the future. The work, which appears to be in the research stage, is ripped out of dystopian science fiction, offering a glimpse of a world in which an authoritarian state is able to move against its citizens before they begin any public dissent.”

Artificial Intelligence

AI Evaluators Struggle with Models That Know When They’re Being TestedRocket Drew | The Information ($)

“AI researchers are starting to make progress on a confounding problem: AI models are getting better at telling when they are in an evaluation. …If models act differently during testing, that could mean they get released with undesirable tendencies. It could also undermine their creators’ ability to show off test scores to potential clients.”

Biotechnology

Moderna Gets $50 Million to Develop MRNA Ebola Vaccine Against BundibugyoBeth Mole | Ars Technica

“The global health organization Coalition for Epidemic Preparedness Innovations (CEPI) announced Monday that it will ‘urgently accelerate development’ of three vaccine candidates against Bundibugyo ebolavirus (BDBV), pledging a little over $60 million in the effort to extinguish an outbreak currently raging out of control in the Democratic Republic of the Congo.”

Computing

World’s First Underwater Data Center Is Now Online, Powered by WindBronwyn Thompson | New Atlas

“Just over seven months from completing phase one of this mega-project, Chinese engineers have finished the build and switched on the world’s first underwater data center (UDC) powered by offshore wind turbines. What’s more, it doesn’t need freshwater and cuts land use by more than 90% compared with above-ground centers.”

Artificial Intelligence

Gemini Spark Is the Most Impressive and Terrifying AI Experience I’ve Had YetDavid Pierce | The Verge

“On the one hand, this is one of the most astonishingly impressive AI experiences I have ever had. …On the other hand, I can’t shake the deeply creepy feeling I get from the whole thing. What Spark did feels sort of magical, and very invasive. It’s weird that Spark is so casually telling me the names and ages of my children, reminding me that it knows where I live, and finding information I know for a fact I’ve never volunteered to Google.”

The post This Week’s Awesome Tech Stories From Around the Web (Through June 7) appeared first on SingularityHub.

The findings could lead to new treatments for multiple neurodegenerative diseases.

Huntington’s disease is tragically predictable. An inherited genetic mutation causes neurons to make distorted, sticky proteins. These proteins clump together and gradually overwhelm brain cells. The brain loses its ability to learn, remember, and make decisions.

This story is dogma in neuroscience. But decades of research and drugs targeting the clumps have had little success. Scientists are now wondering: Is there more to the story? In a twist, a team from the Hebrew University of Jerusalem and collaborators found that protein clumps may be a neuron’s first line of defense against damage.

The misfolded or malfunctioning proteins are quarantined inside bubbly hubs called “inclusion bodies.” Often considered detrimental to cell health, disrupting their formation unexpectedly led to cells becoming more sensitive to stressors often seen in neurodegenerative diseases.

Physical separation played just one part. Inclusion bodies also changed the activity of genes involved in neuroinflammation—even in the absence of immune cells. Scouting the genetic landscape of cells derived from patients with severe Huntington’s disease, the team homed in on a “master regulator” gene, ATF3, that orchestrates immune responses. Removing the gene lessened inclusion bodies’ protective effects against damage in cultured cells.

To be clear, the findings are only for a cell model of Huntington’s disease in a petri dish. And inclusion bodies could be a double-edged sword: protective in the beginning and detrimental later on. Still, acknowledging them as a more complicated villain could better inform strategies for disorders that take over our minds like Huntington’s.

“Our results reveal…that these structures are not merely byproducts of disease, but a central factor in the cell’s ability to mount a protective response against stress,” said study author Eran Meshorer in a press release.

The Problem With PolyQ

It’s long been believed that protein clumps in the brain gradually erode cognition. Whether they’re the main driver of neurodegenerative disorders is still debated, but their presence accelerates brain cell injury, causing neurons to wither away.

Alzheimer’s disease, for example, is associated with two sets of protein clumps. One lives inside neurons (tau) and another gunks up the space between cells (amyloid). Decades of research aimed at removing amyloid clumps have met with minimal success, earning these doomed efforts the notorious nickname “graveyard of dreams.” Despite their struggles, the FDA recently approved two major drugs that remove amyloid clumps and modestly slow cognitive decline, though the approval has been controversial due to doubts about safety.

Other untreatable neurodegenerative disorders also fall into this category. Clumps formed in Parkinson’s disease erode the brain’s ability to control movement, emotion, and even the perception of time. Lou Gehrig’s disease, or ALS, produces inclusion bodies inside motor neurons, leading to muscle weakness and trouble swallowing. The disease eventually robs people of speech and motion.

These diseases often have multiple genetic and environmental triggers. Huntington’s, in contrast, is entirely genetic. The condition stems from the genome over-copying parts of the huntingtin gene (HTT), which normally makes a key protein also called huntingtin.

Normally, cells use the protein’s large, stackable structure to build highways that transport all sorts of biological cargo, from molecules to organelles. The protein also plays an essential role during early brain development and neural wiring in adulthood.

But a mutant form of the HTT gene can wreak havoc. A common mutation, called polyQ expansion, produces unwieldy, misfolded proteins. Nearly 30 years ago, researchers found that these errant proteins aggregate inside parts of the cell. The clumps, or inclusion bodies, were widely thought to be detrimental. Some act like sticky tape that captures healthy proteins, such as those involved in gene expression, and torpedoes cellular health.

But telltale signs in cultured rat brain cells suggest a more nuanced story: Inclusion bodies could also be protective, sequestering mutant proteins as an early form of protection.

A Tale of Two

The common factor in diseases featuring polyQ mutation is repetition. Mutated genes have long, duplicated sequences of the DNA letters cytosine, adenosine, and guanine (CAG). More CAG repeats in the genome translates into earlier disease onset.

We all have this DNA triplet in our HTT gene. But more than 39 repeats results in longer, toxic huntingtin proteins. Severe cases of Huntington’s can feature over 100 CAG repeats, transforming the usually free-floating protein workers into sticky, dysfunctional layabouts.

In the new study, the researchers first established a baseline. They used the gene editing tool CRISPR-Cas9 to reduce CAG repeats in cells derived from Huntington’s patients—which carried over 180 copies—to near normal levels.

They then tagged the cells with a fluorescent marker that causes huntingtin proteins to glow bright green under the microscope. This let the team track protein aggregation in real time. Though they shared the same genetics, some cells formed inclusion bodies; others didn’t.

The team next challenged them with a chemical known to cause cellular stress. Those that formed clumps survived far more regularly than those that didn’t. It was a “striking difference,” the authors wrote. “Once a mutant PolyQ protein is expressed, the formation of IBs [inclusion bodies] protect[s] the cells rather than inflict[s] harm, at least short-term.”

Inflammation seems to be key. Although grown side-by-side, a genetic screen revealed cells with inclusion bodies were especially abundant in a gene called ATF3, which is known to regulate inflammation. Getting rid of the gene wiped out the neurons’ ability to form inclusion bodies, making them more vulnerable.

“Our results reveal a previously unknown role for ATF3 in orchestrating the formation of inclusion bodies in human neurons,” said Meshorer.

These are very early results. An immune molecule bridges ATF3 and inflammation and is associated with Huntington’s disease. Its levels are higher in patients with the condition. Increasing ATF3 activity could amp up the number of protective inclusion bodies and give neurons a fighting chance.

The findings suggest inclusion bodies gather free-floating mutant proteins into clumps to protect neurons and reduce brain damage—at least at the beginning of the disease. However, lab experiments rarely translate to treatments. How fast inclusion bodies form and when they begin to stress cells remains to be seen. Meanwhile, a gene therapy for Huntington’s is underway, and promising results in a small trial suggest an alternative path for treatment.

Still, the study challenges the idea that protein clumps are always detrimental. If replicated in other neurodegenerative diseases such as Alzheimer’s or ALS and if we can learn how long protection lasts, the results could pave the way for better-timed treatment that works with the body’s protection, not against it.

The post Toxic Clumps in Huntington’s Disease May Protect the Brain Too appeared first on SingularityHub.

The gap between what AI can do in biology and what governance systems are prepared to handle is growing.

Artificial intelligence is rapidly learning to autonomously design and run biological experiments, but the systems intended to govern those capabilities are struggling to keep pace.

AI company OpenAI and biotech company Ginkgo Bioworks announced in February 2026 that OpenAI’s flagship model GPT-5 had autonomously designed and run 36,000 biological experiments. It did this through a robotic cloud laboratory, a facility where automated equipment controlled remotely by computers carries out experiments. The AI model proposed study designs, and robots carried them out and fed the data back to the model for the next round. Humans set the goal, and the machines did much of the work in the lab, cutting the cost of producing a desired protein by 40 percent.

This is programmable biology: designing biological components on a computer and building them in the physical world, with AI closing the loop.

For decades, biology mostly moved from observation toward understanding. Scientists sequenced the genomes of organisms to catalog all of their DNA, learning how genes encode the proteins that carry out life’s functions. The invention of tools like CRISPR then allowed scientists to edit that DNA for specific purposes, such as disabling a gene linked to disease. AI is now accelerating a third phase, where computers can both design biological systems and rapidly test them.

The process looks less like traditional benchwork in a lab and more like engineering: design, build, test, learn, and repeat. Where a traditional experiment might test a single hypothesis, AI-driven programmable biology explores thousands of design variations in parallel, iterating the way an engineer refines a prototype.

As a data scientist who studies genomics and biosecurity, I research how AI is reshaping biological research and what safeguards that demands. Current safety measures and regulations have not kept pace with these capabilities, and the gap between what AI can do in biology and what governance systems are prepared to handle is growing.

What AI Makes Possible

The clearest example of how researchers are using AI to automate research is AI-accelerated protein design.

Proteins are the molecular machines that carry out most functions in living cells. Designing new ones has traditionally required years of trial and error because even small changes to a protein’s sequence can alter its shape and function in unpredictable ways.

Protein language models, which are AI systems trained on millions of natural protein sequences, can quickly predict how mutations will change a protein’s behavior or design new proteins. These AI models are designing potential new drugs and speeding vaccine development.

Paired with automated labs, these models create tight loops of experimentation and revision, testing thousands of variations in days rather than the months or years a human team would need.

Faster protein engineering could mean faster responses to emerging infections and cheaper drugs.

The Dual-Use Problem

Researchers have raised concerns that these same AI tools could be misused, a challenge known as the dual-use problem: Technologies developed for beneficial purposes can also be repurposed to cause harm.

For example, researchers have found that AI models integrated with automated labs can optimize how well a virus spreads, even without specialized training. Scientists have developed a risk-scoring tool to evaluate how AI could modify a virus’s capabilities, such as altering which species it infects or helping it evade the immune system.

Current AI models are able to walk users through the technical steps of recovering live viruses from synthetic DNA. Researchers have determined that AI could lower barriers at multiple stages in the process of developing a bioweapon, and that current oversight does not adequately address this risk.

Risk From Bio AI

Experienced scientists are already using AI to plan and design biological experiments. The question of whether AI can help people with limited biology training carry out dangerous lab work is the subject of active research.

Two recent studies have reached different conclusions.

A study by AI company Scale AI and biosecurity nonprofit SecureBio found that when people with limited biology experience were given access to large language models, which is the type of AI behind tools like ChatGPT, they were able to complete biosecurity-related tasks, such as troubleshooting complex virology lab protocols with four times greater accuracy. In some areas, these novices outperformed trained experts. Around 90 percent of these novices reported little difficulty getting the models to provide risky biological information, such as detailed instructions on working with dangerous pathogens, despite built-in safety filters meant to block such outputs.

In contrast, a study led by Active Site, a research nonprofit that studies the use of AI in synthetic biology, found that AI help did not lead to significant differences in the ability of novices to complete the complex workflow to produce a virus in a biosafety laboratory. However, the AI-assisted group succeeded more often on most tasks and finished some steps faster, most notably on growing cells in the lab.

Hands-on work in the lab has traditionally been a bottleneck to translating designs into results. Even a brilliant study plan still depends on skilled human hands to carry out. That may not last, as cloud laboratories and robotic automation become cheaper and more accessible, allowing researchers to send AI-generated experimental designs to remote facilities for execution.

Responding to AI-Driven Biological Risks

AI systems are now able to run experiments autonomously and at scale, but existing regulations were not designed for this. Rules governing biological research do not account for AI-driven automation, and rules governing AI do not specifically address its use in biology.

In the US, the Biden administration had issued a 2023 executive order on AI security that included biosecurity provisions, but the Trump administration revoked it. Screening the synthetic DNA that commercial providers make to ensure it cannot be misused to make pathogens or toxins remains mostly voluntary. A bipartisan bill introduced in 2026 to mandate DNA screening does not yet address AI-designed sequences that evade current detection methods.

The 1975 Biological Weapons Convention, an international treaty prohibiting the production and use of bioweapons, contains no provisions for AI. The UK AI Security Institute and the US National Security Commission on Emerging Biotechnology have both called for coordinated government action.

The safety evaluations that AI labs run before releasing new models are often opaque and unsuited to capture real-world risk. Researchers have estimated that even modest improvements in an AI model’s ability to help plan pathogen-related experiments could translate to thousands of additional deaths from bioterrorism per year. Timelines for when these capabilities cross critical thresholds remain unclear.

The Nuclear Threat Initiative has proposed a managed access framework for biological AI tools, matching who can use a given tool to the risk level of the model rather than blanket restrictions. The RAND Center on AI, Security and Technology outlined a set of actions researchers could take to improve biosecurity, including improved DNA synthesis screening and model evaluations before release. Researchers have also argued that biological data itself needs governance, especially genomic data that could train models with dangerous capabilities.

Some AI companies have started voluntarily imposing their own safety measures. Anthropic activated its highest safety tier when it released its most advanced model in mid-2025. At the same moment, OpenAI updated its Preparedness Framework, revising the thresholds for how much biological risk a model can pose before additional safeguards are required. But these are voluntary, company-specific steps. Anthropic’s CEO, Dario Amodei, wrote that the pace of AI development may soon outrun any single company’s ability to assess the risk of a given model.

When used in a well-controlled setting, AI can help scientists quickly reach their research goals. What happens when the same capabilities operate outside those controls is a question that policy has not yet answered. Overreact, and talent and investment may move elsewhere while the technology continues advancing anyway. Underreact, and the risks of that technology could be exploited to cause real harm.

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

The post AI Can Now Design and Run Thousands of Experiments Without Human Hands. We Aren’t Ready for the Risk to Biosecurity. appeared first on SingularityHub.

A new process for mining lithium-rich rock could slash costs and pollution—and decentralize global lithium production.

Lithium mining is like a modern gold rush. The element is the main ingredient in batteries powering smartphones, electric cars, and even AI. Global demand is surging. Increased production could guide the world toward a more sustainable energy future.

But ironically, current extraction methods offset some of those gains. Lithium mining involves separating the element from brines using toxic chemicals, a process that also pumps out carbon dioxide. This, alongside enormous water and energy costs—due to high temperature requirements—has confined mining to a handful of countries.

To address these drawbacks, scientists at the Massachusetts Institute of Technology have now developed a low-cost, low-temperature, greener process relying on an abundant resource: Hard rock. Although rocks containing lithium cover large parts of the US, Europe, and Africa, extracting it from them is challenging.

While renovating his bathroom, study author Yet-Ming Chiang realized a chemical in glass etching cream—which makes glass translucent—could eat away at lithium-rich rocks. His team then designed a recyclable process to extract lithium as well as two ingredients used to make greener cement and other materials.

“You’ve heard of nose-to-tail eating?” said Chiang in a press release. “We refer to this as nose-to-tail mining.”

Unlike previous methods, the process runs at temperatures below the boiling point of water. All liquid chemicals are almost recyclable and can be reused in multiple rounds of extraction.

“This could establish a low-carbon alternative to hard rock refining, addressing both the surging demand for lithium and the carbon footprint that undermines the sustainability of the energy transition that lithium is meant to enable,” wrote Gang San Lee and Karthish Manthiram at the California Institute of Technology, who were not involved in the study.

A Rock and a Hard Place

The Earth’s crust teems with lithium. Getting it out is the hard part.

Currently, many mining operations rely on brine that naturally leaches lithium over millennia. Later steps purify the lithium into a battery-ready product. The process relies on large evaporation pools and is limited to a few countries, making the resource scarce.

Lithium could, alternatively, be harvested from solid rocks. One ore, spodumene, is packed with lithium, roughly 1.5 percent by weight. But liberating it has been a tough nut to crack.

Traditionally, miners crush rocks and remove chunks that don’t contain lithium. The rocks are then blasted at temperatures as high as 1,100 degrees Celsius (2,012 degrees Fahrenheit) and showered in a cocktail of dangerous chemicals. The process spews liquid waste into the environment and releases 20 tons of carbon for each ton of lithium.

Researchers are working on more temperate methods.

One of these is called ball milling. Ore is rotated in a container filled with hard balls that mechanically grind the stone into a fine power. It’s like using a mortar and pestle instead of a blender. But the process takes longer, and lithium is lost along the way, resulting in lower yields. Another method, called electrochemical leaching, refines the ore at room temperature. But researchers have had mixed success with the process, and it’s tough to scale up. It also produces in a lot of waste rock that could, in theory, be harvested for other uses instead being discarded.

Triple Threat

The new method popped into Chiang’s mind as he was brainstorming ways to break apart spodumene, a lithium-rich ore with high amounts of silica—the main ingredient in glass.

Dissolving silica to get to lithium requires hydrofluoric acid, a highly toxic chemical. But glass etching cream also eats away at silica with ammonium fluoride. Tubes of the mild acid are available in home improvement stores, and it works at room temperature. Why not give it a try?

By mixing ammonium fluoride with water, the team showed they could completely dissolve spodumene at temperatures below 100 degrees Celsius without releasing toxic fumes. They only needed to continuously stir the ore in a simple plastic tank. The process yielded several types of lithium salt with 99 percent purity. In early experiments, extraction took several days, but the team has since cut the time to under 12 hours.

“Dissolving silica is the hard part in mining,” said study author Benjamin Mowbray. “The next question was how do we apply it to impactful mineral processing problems?”

Along with lithium, spodumene is jam-packed with two usually discarded ingredients: Alumina, which after smelting makes aluminum, and silica, which can be directly used as a sustainable ingredient in greener cement. The new process can separate out both materials, and the team vetted the resulting products, including strength testing cubes of fabricated cement.

“First our goal was to produce these products, then there were additional steps of characterizing their purity and properties and making sure our products met the specifications for target markets,” said Mowbray.

“If any product didn’t meet the target specs, you’d end up with a waste stream.”

With a few chemical tweaks, the team showed the acid could be regenerated and reused at least five times. The team successfully processed 17 spodumene ores sourced from around the world, suggesting the method could be broadly applicable.

They’ve also spun the work into a startup, Rock Zero, and aim to scale it. If the acid can be recycled with near-perfect efficiency, the team estimates the process would cut costs over 40 percent compared to conventional hard-rock extraction, making it competitive with brine operations.

Its simplicity could also reshape where lithium gets produced. In 2024, roughly 74 percent of global lithium output came from just three countries: China, Australia, and Chile. By eliminating the need for extreme heat and massive waste-treatment plants, the process could be easier to implement, especially in countries rich in spodumene but lacking the capital for infrastructure.

That opens the door to a network of smaller refineries built closer to the mines themselves, reducing transportation costs and supply-chain bottlenecks. Because the process is also far less energy intensive, it could be powered by solar and wind, further shrinking its environmental impact.

The technology could also be adapted to recover other valuable metals hidden inside mineral ores. One candidate is beryllium, a lightweight but extremely stiff and stable metal used in satellites and the James Webb Space Telescope’s mirrors. Current manufacturing processes often generate toxic dust and fumes linked to serious lung inflammation. A cleaner extraction route could make it safer and cheaper to produce.  

As for Rock Zero, going up against established lithium giants is like David and Goliath. They’ll also have to contend with global market volatility and increasing competitiveness of sodium-ion batteries and other alternative battery chemistries.

But the team is unfazed. “We believe this approach is the lowest-energy, lowest-cost way of getting lithium not only out of hard rock, but period,” said Chiang. “That’s what’s motivating us to scale this.”

The post Three Countries Own the Lithium Market. An MIT Startup Wants to Break Their Grip. appeared first on SingularityHub.

A huge analysis of gene expression across species revealed genetic hallmarks of aging and could accelerate anti-aging treatments.

There’s truth to the old adage, “Age is just a number.” People of the same age differ vastly in health and mental capabilities. One 80-year-old may be vibe coding with Claude, while another is gradually forgetting familiar faces and memories.

To better gauge this difference, scientists have been developing “clocks” that measure biological age. Rather than the number of candles on a birthday cake, these tools capture health at the cellular level and are remarkably accurate at estimating disease risk and even life expectancy. But how they work is hard to explain.

Now Harvard scientists and collaborators have released a powerful and more interpretable clock. Using the gene activity of thousands of individuals and animals, the clock predicts biological age in rodents, monkeys, and humans, including how many years they have left.

The analysis involved over 11,000 gene activity profiles across four species, highlighted shared mechanisms during aging, and responded to known anti-aging interventions—such as parabiosis, during which aging animals receive blood from a young donor.

Although the clock isn’t ready for clinical use, it is a boon to scientists working to slow or even reverse the unstoppable progression of time. It “could help researchers to pinpoint which processes are modulated by interventions or diseases,” wrote João Pedro de Magalhães at the University of Birmingham, who was not involved in the work.

Tick, Tock

Biological clocks come in a variety of flavors.

Most rely on AI to make sense of information held in large databases of people. One of these, for example, uses blood proteins related to brain aging to reflect cognition and its decline better than chronological age. Another type, metabolomic age clocks, sorts through protein and fatty acid building blocks to estimate biological age. These clocks correlate well with risk of inflammation, chronic disease, and frailty (where the body struggles to recover from a mild infection or minor fall). More recent multi-omics clocks combine blood measures, metabolism, gene activity, and clinical data for a comprehensive bird’s-eye view of biological age.

But epigenetic clocks remain the field’s defining breakthrough.

As we age, chemical tags accumulate on DNA, switching genes on or off. The pattern of these tags shifts over time and is shaped by everyday life—diet, exercise, stress, sleep quality. Studies have found that the age gaps between biological and lived years measured by the well-known Horvath epigenetic clock, which relies on DNA methylation, were associated with the risk of various types of diseases. Later versions of the Horvath clock could predict maximum lifespan. And other groups have developed “pan-mammalian” epigenetic clocks that work across species.

“One drawback of epigenetic clocks, however, is their limited interpretability,” wrote Magalhães. “The mechanisms that underpin age-related methylation changes are still debated.”

Clocking In

In the new study, the team measured aging by looking at gene activity, or transcriptomics. Transcriptome profiles capture which genes are switched on at any given moment.

Previous studies have linked the aging transcriptome to chronic inflammation, faltering mitochondria, and the gradual breakdown of the extracellular matrix, the molecular scaffolding that supports tissues and organs. With age, these systems go awry.

“Because the signatures reflect changes in the activity of specific genes, transcriptomic biomarkers are more interpretable than are epigenetic ones,” wrote Magalhães. The tradeoff is that gene activity is far more dynamic than DNA methylation, the epigenetic signature used in the Horvath clock. A transcriptome can shift in response to stress, illness, exercise, or even the time of day, making it a less reliable measure of aging.

To make the new clock, the team assembled over 11,000 transcriptomes, heavily relying on data from the Interventions Testing Program, a giant effort to study longevity treatments in mice. The dataset included mice exposed to genetic tweaks, drugs, and dietary therapies known to affect aging and lifespan. The team also added more than 2,600 samples from monkeys, several hundred from rats, and over 4,000 from humans to deliver a cross-species view of aging.

They then built multiple transcriptome clocks that estimated age and mortality risk. To validate the clocks, they turned to an independent dataset that included rodent models of accelerated aging, Alzheimer’s diseases, chronic kidney disease, and other age-related conditions. When applied to individual cells, the clocks yielded older transcriptomic ages in more than 90 percent of the samples, suggesting that aging is deeply rooted at the cellular level.

In humans, the clocks accurately predicted the lifespans of participants enrolled in a large heart health study. They were also sensitive to environmental factors that affect aging, ticking forward after exposure to radiation or chronic diseases and rewinding after treatments such as young-blood transfusion, a strategy shown to rejuvenate elderly rodents.

An analysis of the genes driving the clocks highlighted many of the usual molecular suspects. Aging turned on genes involved in inflammation, cellular energy disfunction, and senescence—where failing cells leak toxic molecules. Many of these signatures appeared across organs and species, suggesting that core aspects of aging have been conserved in mammals.

These findings are especially valuable for longevity researchers, who often work with rodent models. Despite living a fraction of a human lifespan, aging rodents undergo transcriptomic shifts similar to those found in us. The new clock could easily test their biological age after potential anti-aging treatments, capture the immediate effects, and predict lifespan, long before they die. It could, in theory, speed up aging research and the quest for treatments.

But to be clear, like other aging clocks, it isn’t a crystal ball. Scientists don’t know if the transcriptome changes drive aging or merely reflect its aftermath. The signatures could be capturing overall health and resilience, rather than molecular changes associated with aging per se.

That distinction matters. As we grow older, cells activate a variety of protective genes to counter rising stress, inflammation, and damage. Not every age-related transcriptomic change is harmful. Some changes reflect the body’s attempt to fight back. Because transcriptomes capture only a snapshot in time, scientists still need to differentiate genes that contribute to aging from those that help defend against it and learn how those patterns shift over time.

There’s a broader challenge too. Researchers are building more and more biological clocks using different criteria, and they don’t always agree. One may say you’re far older than another. This highlights “the need for any aging biomarker to be validated carefully,” wrote Magalhães.

The post How Fast Are You Aging? New Genetic Clock May Have the Answer appeared first on SingularityHub.

Artificial Intelligence

In This Manhattan Lab, AI Designs Materials From ScratchAdele Peters | Fast Company

“The lab uses standard materials science equipment, but it’s almost all automated and run by AI; if it has a new idea at 4am, it starts running again. It can run as many as 50 experiments in a day, and the team is aiming to increase that to 100 experiments a day by the end of the summer. A human materials scientist, Krause says, might do 50 experiments in a year.”

Biotechnology

One-and-Done Heart Disease Prevention? Scientists Show It May Be Possible.Gina Kolata | The New York Times ($)

“In a small, preliminary study, an experimental gene-editing treatment dramatically lowered cholesterol levels, perhaps permanently, after just one infusion, scientists reported on Monday. If confirmed in larger studies, researchers hope the findings may lead to a one-and-done way to prevent heart disease in large numbers of people.”

Robotics

3D-Printable Humanoid Legs Let Robotics Experiments Run WildJeremy Hsu | Ars Technica

“A $2,500 pair of humanoid robot legs built from 3D-printed parts and off-the-shelf components is not going to win marathons just yet. But such relatively inexpensive hardware could enable researchers to more easily test and train AI-powered robotics software in a physical body during real-world experiments.”

Biotechnology

Pancreatic Cancer Halted by Virus Injection in Three PatientsAlice Klein | New Scientist ($)

“Further evaluation is needed in larger trials, but the early results are encouraging, especially since only small doses of the virus were administered for initial safety testing. ‘We only injected one-tenth of the dose we are eventually aiming at, so the efficacy is better than I expected, especially as this is pancreatic cancer,’ says Masato Yamamoto at the University of Minnesota, who led the development of the viral treatment.”

Future

A Reality Check on the AI Jobs HysteriaDavid Rotman | MIT Technology Review ($)

“Haven’t you heard? White-collar jobs are going away, decimated by AI. …But before you quit your job as a software developer or financial analyst—or tech journalist—and look to join the plumbers’ union, it’s worth considering today’s economic research on whether artificial intelligence has actually begun to devour white-collar work. The short answer is: No.”

Artificial Intelligence

The AI Superstars Who Say a ‘Vibe Slop’ Crisis Is ComingChristopher Mims | The Wall Street Journal ($)

“Two engineers who built the core of the massively popular OpenClaw AI agent have a stark warning: The artificial intelligence supposedly capable of replacing well-paid software developers is flooding the world with bad, potentially even dangerous, code. It’s a phenomenon they call ‘vibe slop’—a combination of ‘vibe coding,’ creating software with AI tools by describing it in plain English, and ‘AI slop,’ the endless, low-value AI-generated content all over social media.”

Future

Mirror Life: Scientists Clash Over Threat of Lab-Engineered BacteriaJames Woodford | New Scientist ($)

“Microbes based on mirror images of molecules in the natural world would have a hard time surviving outside the laboratory, according to a modeling study. To do so, they would need a ready supply of ‘mirror food,’ or some novel way to feed themselves. But the research has drawn a backlash from other experts in the field who warn that it may underestimate the grave risks posed by so-called mirror life.”

Tech

Uber President Says AI Spending Is Getting ‘Harder to Justify’Jess Weatherbed | The Verge

“After reportedly exhausting its annual AI budget just four months into 2026, Uber is now questioning whether it’s actually seeing meaningful returns on its investments. In an interview with Rapid Response, Uber president and chief operating officer Andrew Macdonald said the company isn’t seeing a connection between rising token consumption for Claude Code and more useful features being delivered to consumers.”

Future

Illinois Lawmakers Just Passed America’s Strongest AI Safety BillMaxwell Zeff | Wired ($)

“The Illinois House of Representatives passed a bill on Wednesday requiring frontier AI labs like OpenAI, Anthropic, and Google DeepMind to have their safety practices audited by a third party. If signed into law, AI safety experts tell Wired, it would be the nation’s leading check on the power of major AI companies.”

Artificial Intelligence

RSI Is the New AGI—and It’s Just as Hard to Pin DownRussell Brandom | TechCrunch

“The word ‘recursion’ is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff—even if there’s still a little disagreement about what it exactly means.”

Artificial Intelligence

I’m a Professional Fact-Checker. AI Is Wrong More Than You Think.Meghan Herbst | Wired ($)

“Over the past year or so, more and more people have looked at me with great pity. Surely a fact-checker at a magazine isn’t long for this AI-upgraded world. Call me foolish, but I’m not that worried. Very little of humanity’s collective knowledge, I’ve concluded, lives on the internet. And according to my research, AI is even more wrong than people might think.”

Space

Millions of Planets Might Form Around Supermassive Black HolesJonathan O’Callaghan | New Scientist ($)

“Eventually planets would begin to grow in huge numbers, and with strange properties. ‘This is a really amazing new pathway to form very alien planets,’ says McKernan. ‘If these things exist, they’re quite unlike planets that we know and love.'”

The post This Week’s Awesome Tech Stories From Around the Web (Through May 30) appeared first on SingularityHub.

Sodium-ion batteries are rapidly gaining on lithium in consistency and fast charging.

As demand for electric vehicles and grid storage surges, battery makers are searching for alternatives to lithium that are cheaper and easier to source. New research suggests sodium-ion batteries, which have long been heralded as a promising alternative, may be maturing faster than expected.

Lithium-ion batteries dominate the market thanks to their excellent energy density and well-developed supply chains. But lithium prices have been swinging wildly in recent years, and there are concerns about lithium market concentration—the vast majority of extraction happens in a handful of countries, like Australia and Chile, and China dominates lithium processing.

This has driven interest in novel chemistries. Sodium is a leading contender due its low price and abundant deposits all over the globe, but performance concerns have held back adoption.

Chinese companies, however, have begun to take sodium batteries seriously. And in a new analysis in Cell Reports Physical Science, German scientists found that cells made by the Chinese manufacturer HiNa compare favorably to the lithium-ion batteries Tesla uses in its cars.

“The combination of good uniformity, high power capability, and strong low‑temperature performance makes these cells attractive for stationary storage, grid services, and shorter‑range or commercial vehicles where potential lower cost and resource availability matter more than maximum driving range,” Moritz Schütte, a battery researcher at RWTH Aachen University who co-led the study, said in a press release.

A good battery needs uniform cells. If some cells are weaker than others, it can degrade the entire battery over multiple charge and discharge cycles, and it also makes it harder to control and optimize power flow in and out of the pack. It’s also a key indicator of a mature production process.

To see how the HiNa batteries stacked up, the researchers tested 120 individual sodium-ion cells using a non-destructive technique called impedance spectroscopy. Here, they applied a current across various frequencies to probe the internal physical chemical properties of the device.

The team then tested the cells at varying currents and temperatures from -4 to 113 degrees Fahrenheit to get a picture of their power performance under a wide range of conditions. They also used X-rays to probe the batteries’ internal structure, before opening them up to analyze the size and composition of various components in more detail.

Across the 120 cells, resistance varied by just 5.3 percent—a level of consistency the researchers say is comparable to well-established lithium-ion production lines. And while fast charging can rapidly degrade performance, the cells maintained full capacity at charge rates high enough to fill the battery in just 15 minutes.

Low temperature also reduces capacity by slowing down a battery’s chemical reactions. But the researchers found the HiNa device discharged over 80 percent of its usable energy at -4 degrees Fahrenheit after charging at roughly room temperature. That figure fell to 56 percent, however, when it was also charged at -4 degrees Fahrenheit (as opposed to room temperature).

The batteries didn’t get a universally glowing report. The team found energy density still lags the best lithium-ion cells, and as noted, charging at low temperatures remains a problem. “The high‑power performance was better than one might expect from an early commercial sodium‑ion product,” said Schütte. “However, for applications that require frequent charging at low ambient temperatures, appropriate thermal management or operating strategies will be important.”

But given the technology’s other attractive characteristics, the battery industry appears to be forging ahead. Chinese automaker Changan Automobile recently began selling the Nevo A06, which is fitted with a sodium-ion battery made by CATL, the world’s dominant battery manufacturer.

According to Bloomberg, CATL’s chief technology officer recently told a media event that the company will begin mass-producing sodium-ion cells in the fourth quarter of this year, declaring “the era of sodium and lithium shining together has arrived.”

A typical SUV powered by a sodium-ion battery would only have a range of around 215 miles, compared to the 250 to 370 miles for a lithium-ion powered vehicle, according to calculations from the International Energy Agency. But that’s nothing to turn your nose up at, particularly considering the fast-charging capabilities discovered by the RWTH researchers.

Whether the technology establishes a commercial foothold may well depend more on the vagaries of geopolitics than its inherent qualities. But cheaper, easier to source batteries can only be a win for the planet.

The post Sodium Is Cheap, Abundant, and Now Powering Batteries That Could Rival Lithium appeared first on SingularityHub.

AI can rifle through enormous libraries of information to connect far-flung ideas—conceptual leaps remain a purely human skill.

Last week, OpenAI shocked the mathematical community by revealing that one of its internal artificial intelligence models had found a counterexample to a famous conjecture made by legendary Hungarian mathematician Paul Erdős in 1946.

The planar unit distance problem, or Erdős problem 90, has intrigued mathematicians for decades. The new result is no mere curiosity. Canadian mathematician Daniel Litt described it as “the first result produced autonomously by an AI that I find interesting in itself.”

The breakthrough, produced with a general-purpose AI model rather than one specialized for mathematics, also highlights how AI is changing mathematical research itself. Days after OpenAI’s paper, US mathematician Will Sawin followed the same line of reasoning to an improved result. Also last week, a team from Google DeepMind used one of their own models to resolve nine lesser open problems left by Erdős.

At the same time, results like this show us what kind of mathematics current AI models are good at—and where their capabilities are still uncertain.

Dots and Lines

Paul Erdős was one of the most prolific mathematicians of the twentieth century. He was famous for asking deceptively simple questions whose solutions often resisted decades of effort.

At first glance, the underlying problem seems relatively straightforward. Suppose you have some number of points—call the number n—drawn on an infinitely large piece of paper. Given you can arrange the points any way you like, how many pairs of points can be positioned exactly one unit of distance away from each other?

If you try this problem yourself (on a presumably finite piece of paper), you may quickly gravitate towards a square grid as a promising candidate for the best arrangement. The spacing of the grid naturally creates many pairs at a regular distance apart.

A square grid intuitively looks like a good solution to the planar unit distance problem. OpenAI

This intuition influenced much of the early thinking about the problem. As the number of points grows, grid-like arrangements continue to appear to be remarkably effective.

For decades it was widely believed these highly regular structures were about as good as it gets. Erdős himself conjectured that no construction could improve substantially on these intuitive arrangements, even for an extremely large number of points. (The new best result, by Sawin, reportedly only starts to yield improvements for around 102000000 points—that’s a one followed by two million zeroes.)

Over the past 80 years, mathematicians have tried to prove Erdős either right or wrong. Their efforts have linked the problem to other areas of mathematics called incidence geometry, graph theory, and extremal combinatorics. While a full proof remained elusive, there was a general feeling that Erdős’ conjecture was probably true.

However, OpenAI’s recent breakthrough proved Erdős’ intuition wrong. The new result uses tools from an area of mathematics called algebraic number theory to show there are patterns of dots that involve many more unit-distance pairs than the square grid, for infinitely many values of n.

No Hesitation

In an article OpenAI published alongside the new paper, several leading mathematicians remarked on the result.

Fields Medalist Timothy Gowers wrote that if a human researcher had submitted the paper with this result to the prestigious journal Annals of Mathematics, he would have recommended publication “without any hesitation.” He also added that no previous AI-generated proof had come close to this level of sophistication.

This breakthrough also represents the first major mathematical open problem solved with AI with minimal human intervention beyond the initial prompt. The accompanying paper shows the prompt given to the model, as well as a recount of the “chain of thought” conducted by the model.

This has renewed broader questions about the capabilities of AI to aid in, and perform, mathematical research.

Three Keys to Mathematical Research

Research mathematicians have been using computers for a long time, but their work is rarely driven by computation alone. Most major breakthroughs emerge from a delicate combination of three things: expertise developed over years, sustained effort to apply that expertise creatively to explore ideas (many of which turn out to be dead ends), and occasional conceptual leaps that suddenly reorganize how a problem is understood.

The first two are domains where AI models excel: as noted by Gowers, large language models such as ChatGPT have an “encyclopedic knowledge of mathematics.” Moreover, they can follow huge numbers of speculative lines of inquiry, even those unlikely to lead anywhere, without human time constraints.

The latter seems to be what provided the key to success here. In hindsight, it seems an expert given a small number of hints would be likely to be able to reach the same proof. As Gowers notes:

“Many of the ideas needed for the proof were present in the literature already, and for such ideas either no hint is needed, since the expert is aware of that piece of literature, or a highly generic ‘look it up’ hint would be enough.”

Lightbulb Moments

The harder question is how much AI can contribute to genuine conceptual leaps. These acute moments of insight, where a lightbulb moment reframes a problem in an entirely new way, are often seen as the most human part of mathematics.

These leaps are hard to formalize and even harder to predict. It remains unclear whether AI models can replicate them, even with recent advances.

What is clear is that AI models are causing a seismic shift in the way mathematics is discovered.

For centuries, progress in mathematics depended almost entirely on human creativity and persistence. Now, for the first time, researchers are working alongside systems capable of autonomously exploring enormous spaces of ideas and contributing to problems once thought accessible only to human insight.

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

The post An AI Solution to an 80‑Year‑Old Problem Has Shocked Mathematicians appeared first on SingularityHub.

The drops, tested on mice, healed eye damage using light-sensitive particles—sourced from ordinary spinach.

The unassuming vial of eye drops could easily belong on a pharmacy shelf. But swirling inside are microscopic bits of photosynthetic machinery made from plants. Within minutes of giving the drops to mice, their eyes gain an extraordinary ability beyond that of any mammal. Like a leaf, they can now harness the power of sunlight.

Photosynthetic eyes sound like they’re straight out of science fiction, but there’s a practical use researchers are after. Chemical reactions during photosynthesis generate powerful antioxidants that ward off inflammation and could potentially treat a range of health conditions.

Called LEAF, the technology is creative, effective, and simple. Its main ingredient can be found in grocery store spinach. In a paper detailing the work, researchers at the National University of Singapore and collaborators say they developed a gentle chemical cocktail to extract some of the core mechanisms used in photosynthesis.

Introduced to mammalian cells—including those that make up the cornea and immune cells—the floating photosynthetic particles made themselves at home and restarted work as usual when exposed to light. In mice with dry eye disease, LEAF continuously pumped out protective antioxidants, healed corneal scarring, and kept their eyes hydrated for days.

The animals scurried around as usual, without any inkling their eyes were now part plant.

“This is an exciting finding as we have, for the first time, demonstrated that plant photosynthetic machinery can be transplanted into mammalian tissue to generate biologically useful molecules, powered entirely by the same light that enables our vision,” study author  Kuoran Xing at the National University of Singapore said in a press release. “We, too, can have limited photosynthetic abilities.”

Planting an Idea

Dry eye disease is one the most common eye problems, affecting roughly 1.5 billion people worldwide. Symptoms are hardly trivial. Irritation and chronic pain make daily life miserable. Overtime, the disease causes scarring of the cornea, blurred vision, and sensitivity to light. The condition has been linked to depression, anxiety, and other health struggles.

Current treatments address the underlying inflammation, but they’re expensive, have limited availability, and long-term use can provoke uncomfortable side effects throughout the body.

At the heart of the disease is a vicious, runaway cycle of cellular dysfunction. When our cells generate energy, they also produce byproducts called reactive oxygen species. Like tiny bullets, these wreak havoc if left unchecked. Some tunnel through delicate protective membranes and disrupt protein function. Others damage DNA, and in severe cases, cause cell death.

Our bodies constantly mop them up with a molecule called NADPH. But during inflammation the defenses are overwhelmed. Reactive oxygen species destroy the cells’ ability to make NADPH. Left unchecked, the cell enters a death spiral: It tries to maintain its supply of energy, but this ironically, generates more bullets and these activate immune cells. Trying to boost NADPH under these conditions is a losing battle.

That’s why spinach caught the team’s attention. Plants make NADPH during photosynthesis. Powered by sunlight, they churn out energy and the antioxidant in completely different ways than our cells. Theoretically, adding plant-based machinery into our cells could bypass existing cellular mayhem and provide a new source of NADPH.

A plant-animal crossover sounds preposterous, but it already occurs in nature. The sacoglossan sea slug eats microalgae high in chloroplasts—the photosynthetic organelle in plant cells—and stores them intact in its guts. When it can’t find food, the slug can survive on photosynthesis.

In previous studies inspired by the slug, scientists have tried transplanting core bits of photosynthetic machinery called thylakoids into animal cells. They look like stacks of coins, but their interior structure is far more complex—any misalignment results in catastrophic failure.

Researchers had already tried transplanting bits of this machinery into mouse knee cells but found it required high levels of an additional chemical to keep it in working order. In another study, a team targeted rheumatoid arthritis, an inflammatory disease of the joints. But getting light into the tissues was a struggle, and the system needed lengthy exposure.

Eyes, however, are a natural window to visible light.

Eyes on the Prize

In the new study, the team’s main invention was figuring out how to keep thylakoids intact while stripping away other parts of the chloroplast that destroy NADPH.

They eventually learned how to extract thylakoid particles from spinach in such a way as to maximize NADPH production. Measuring roughly 400 nanometers across—the size of a very small bacteria—the particles produce NADPH when exposed to ambient light.

The team tested them on two types of cells responsible for dry eye disease: Large immune cells called macrophages and corneal cells. In petri dishes, both cell types readily soaked up LEAF. Once released inside the cell, the plant thylakoids steadily pumped out NADPH.

Within 30 minutes of light exposure, the amount of reactive oxygen species tanked. Angry macrophages relaxed into a state that battles inflammation. In tears collected from patients with dry eye disease, LEAF boosted NADPH levels roughly 20-fold and slashed a damaging oxidative chemical over 95 percent. Tests examining the wider metabolic landscape showed cells reverted to a healthier state after being treated with LEAF.

This photosynthesized NADPH supply can “power antioxidant metabolism,” promote cell repair, restore balance, and break the vicious cycle, wrote the team.

In a final test, they treated a mouse model of dry eye disease with the drops twice daily for five days and pitted it against an approved chemical treatment. LEAF easily entered the animal’s eyes after 30 minutes. Under ambient light, the system doubled the amount of NADPH and reversed corneal damage, outperforming the therapeutic drug.

Surprisingly, although the treatment is made of plant matter, it didn’t trigger immune attacks in the eyes or other parts of the body, such as the liver or heart. But the team didn’t specifically test to see if the drops improved the animals’ eyesight or if adding the photosynthetic machinery changed their perception.

That said, LEAF is especially well-suited for clinical use. It’s easily manufactured and stored and was consistently effective across four independent batches made in Singapore and China, with each sourced from local spinach. The nanoparticles are stable for two weeks at room temperature and last up to a year at -80 degrees Celsius.

Because LEAF “is derived from spinach, delivered as a simple eye drop, [and it] requires no external device or power source…we believe it has a strong potential for clinical translation,” said study author David Tai Leong.

Beyond dry eye disease, LEAF could be made into a cream that harnesses sunlight to treat skin inflammation disorders. The team is also looking to generate photosynthetic molecules in deeper organs and boost the health of mitochondria, the cell’s energy factories.

“It is almost surreal when thinking of a possible future reality where human cells can have some limited but beneficial form of photosynthetic ability not only in the eye but elsewhere, too,” said Leong.

The post Photosynthetic Drops Soothe Dry Eyes With Sunlight appeared first on SingularityHub.

Researchers are testing CAR T cell therapy as a way to reset the immune system in lupus, Graves’ disease, and other conditions where the body’s defenses go rogue.

This story was originally published by Knowable Magazine.

At age 49, Jan Janisch-Hanzlik’s multiple sclerosis was destroying her freedom to live the life she wanted. She gave up her active nursing job for a desk role. Frequent falls made her afraid to carry her grandchildren. She had to move to a bigger house to make room for the wheelchair she feared she might end up needing full-time.

Even the best available medication wasn’t improving Janisch-Hanzlik’s symptoms, and she worried they’d only get worse. So when she learned about a trial of CAR T cell therapy at the University of Nebraska Medical Center in Omaha, close to the city of Blair where she lives, she phoned the clinic every other month until they were ready to enroll her as the first patient.

Originally designed to target and wipe out cancer by reprogramming the patient’s immune cells, CAR T is now being offered to patients in hundreds of clinical trials for autoimmune conditions like multiple sclerosis, lupus, Graves’ disease, vasculitis, and many others. The hope is that CAR T can duplicate the success it has demonstrated in a range of blood cancers by hunting down and eliminating cells that target the self in autoimmune diseases. This would essentially reset the body’s defenses to a state like the one that existed before the disease took hold.

But along with CAR T’s promise come risks, questions and challenges. There’s uncertainty about how well it will work for autoimmunity and how long any benefits might last, as well as what long-term side effects might arise. Janisch-Hanzlik knew this when she sat down to receive the experimental treatment on June 9, 2025; she felt a mix of hope and fear knowing that she would be spending the next week being monitored for side effects including dangerous inflammation.

In addition to her clinical expertise and desire to pioneer a new treatment, Janisch-Hanzlik’s two young grandchildren helped inspire her pursuit of a treatment with known risks and uncertain benefits. Because multiple sclerosis has a genetic component, Janisch-Hanzlik knew that they have an elevated chance of going through the same struggle she has. “I would want to be able to say I did everything that I possibly could to prevent them, or anyone else, from having something like this,” she says.

From Cancer to Autoimmunity

The first CAR T cancer treatment was approved by the Food and Drug Administration in 2017 for an aggressive form of leukemia. Since then, the powerful and intensive treatment has resulted in long-term remission for many cancer patients.

The basic premise of CAR T is to activate the power of key immune cells called T cells. T cells normally recognize other cells that have been infected by a virus or bacterium, or are otherwise abnormal, and either destroy them or recruit other parts of the immune system to do so.

In CAR T for cancer, scientists engineer those T cells to specifically hunt and destroy malignant cells. The technology got its start when cancer researchers figured out how to take out a patient’s own T cells, insert DNA instructions for a “chimeric antigen receptor,” or CAR, and put them back into the person’s circulation. The CAR, which sits on the T cell’s surface and latches on to a specific molecular partner on the surface of cancerous cells, activates the T cell to attack.

Today, CAR T cells are most commonly programmed to attack B cells, another key immune player. B cells are normally responsible for making antibodies, but in certain blood cancers, they proliferate out of control. By giving T cells a CAR that recognizes one of a couple of molecules unique to the B cell surface, the cells are reprogrammed to find and eliminate those cancerous cells.

B cells also are the central problem in many autoimmune conditions: They mistakenly make antibodies against normal tissues instead of against invading pathogens. So as CAR T began to succeed against B cell cancers, it didn’t take long for doctors to reason that CAR T therapy might also be able to wipe out bad B cells in people with autoimmunity.

A German team pioneered autoimmune CAR T in a woman with lupus, reporting positive results in 2021. Since then, that team and others have worked to translate the oncology success of CAR T to tackle a broad spectrum of autoimmune diseases.

“I think it’s a game changer,” says Amanda Piquet, an autoimmune neurologist at the University of Colorado Anschutz in Aurora. Piquet is evaluating CAR T therapy for a rare and poorly understood autoimmune condition called stiff person syndrome, with symptoms including muscle stiffness and painful spasms. There is no FDA-approved treatment. When she heard about a company called Kyverna that was testing CAR T cell therapy in the syndrome, she thought it was “a perfect opportunity.”

The study she led, which reported preliminary results in December 2025, tested a single dose of CAR T in 26 people. Before the treatment, many participants struggled with a slow, mechanical gait, and 12 used assistive devices such as walkers and canes. Most patients were able to walk faster by 16 weeks post-treatment, and eight no longer needed their assistive devices for short distances. In April, the company reported that all 26 patients, as of their last follow-up appointment four to 12 months out from the therapy, were no longer using any other immunotherapies.

Risks and Uncertainties

Despite such striking results, reprogramming the immune system is no simple matter. In early treatment of cancer patients, CAR T cells produced life-threatening side effects, as outlined in a 2026 article in the Annual Review of Medicine. As CAR T cells attack their targets, the associated inflammation can cause symptoms like high fevers and low blood pressure. If that inflammation reaches the brain, it can cause additional problems such as confusion and drowsiness.

Fortunately, physicians now have a decade’s worth of experience recognizing and treating these problems. “They’re certainly reversible and don’t cause long-term damage most of the time,” says Emily Littlejohn, a rheumatologist at the Cleveland Clinic.

Physicians and patients also must contend with decreased immunity as both a side effect of the treatment and its desired outcome. In CAR T treatment, doctors typically use powerful chemotherapy drugs to temporarily reduce the body’s immune cell population to make room for the new, engineered cells, leaving patients temporarily immunosuppressed. And if the treatment works, it will decimate B cell populations. Patients can be vulnerable to infections for up to a year after treatment, says Littlejohn.

These effects are manageable with preventive antibiotics, antivirals, and vaccines. Patients also retain antibodies that their B cells made before the treatment, which provide residual protection for a few months. And for reasons that are not yet fully understood, CAR T seems to leave older B cells, which provide immune memory of past infections, intact in some cases. One study found that autoimmune patients treated with CAR T still made antibodies for diseases they’d been previously vaccinated against, like chicken pox and measles. These are signs that the treatment did not completely return the immune system to its factory settings.

When evaluating CAR T risk, it’s important to consider that many existing treatments for autoimmune disease also suppress the immune system for as long as a person takes them, experts note.

But there are other possible CAR T risks for autoimmune patients. In February, FDA officials published a paper endorsing CAR T’s potential in autoimmunity but warning of “unpredictable long-term toxicity.” CAR T treatment for cancer, the authors noted, has been linked to diverse long-term issues such as Parkinson’s disease. There have also been cases where the bioengineered cells themselves turned malignant, causing new, T cell-based cancers.

Causing a secondary cancer may be an acceptable risk when treating a life-threatening cancer, but probably not for autoimmunity, says Matt Lunning, medical director for gene and cellular therapy at Nebraska Medicine in Omaha. How to balance the risk between the impacts of an autoimmune disease, which can range widely in severity, and the difficult-to-quantify risk of future side effects or cancers remains a major open question.

Researchers are already working on second- and third-generation versions of CAR T that they expect to be safer for both cancer and autoimmunity. For example, James Howard, a neuromuscular neurologist at the University of North Carolina at Chapel Hill, is testing a technology from a company called Cartesian Therapeutics that encodes the CAR using molecules of mRNA, the short-lived genetic messenger used in Covid-19 vaccines, instead of long-lasting DNA. The CAR T cells should wipe out B cells for only as long as the mRNA persists, then lose their B cell-targeting abilities. With no chance for genetically modified T cells to hang around long-term, there should be no cancer risk.

Another plus of Cartesian’s approach: Physicians infuse these T cells in sufficient numbers that they don’t need to reproduce in the patient’s body, which Howard thinks reduces risk for inflammation. In a recent trial, 15 people with autoimmune diseases received the Cartesian CAR T treatment; two-thirds saw their symptoms improve and none suffered long-term serious side effects.

Treating CAR T Sticker Shock

Beyond side effects, the other major challenge facing CAR T therapy is its price tag, which reaches hundreds of thousands of dollars including hospital stays, cell engineering, and other expenses.

The treatment would likely be cheaper, and simpler, if scientists could eliminate the need for personalized engineering of each patient’s own cells and instead use donor cells, or if they could cut out the step of engineering and growing the cells in a laboratory. Lunning says he is eyeing up-and-coming procedures that would modify a person’s T cells within their own body ­instead of doing the genetic engineering in a lab.

Researchers are even farther along with a version of CAR T that uses healthy donors as a source of T cells. These could then be used by many patients in an “off-the-shelf” approach. It’s a method that has its own challenges, because of the immune mismatch between donor and patient cells that would lead them to attack each other. This problem can be overcome with additional genetic modifications to the donated T cells that prevent recipient and donor systems from recognizing each other as foreign, says Bing Du, an immunologist at East China Normal University in Shanghai who’s among many researchers working on this approach. Du estimates a lab could make CAR T cells for more than 1,000 patients from a single donor’s blood cells, at significant cost savings.

This kind of off-the-shelf CAR T therapy is what Janisch-Hanzlik of Nebraska received, under Lunning’s care, in 2025. The study organizers at TG Therapeutics expect to complete their research in early 2029.

Janisch-Hanzlik ended up sailing through the follow-up without side effects. A couple of months after the infusion, she was watching TV when she noticed she no longer needed special glasses to correct double vision. She started forgetting to bring her cane when moving about her house or going grocery shopping; she didn’t need it. Now, nearly a year since the treatment, she rarely falls and no longer requires a daily, three-hour nap. She recently enjoyed a trip to the Grand Canyon and looks forward to spending more time with her grandchildren.

She does still have symptoms, including weakness in her right leg, numbness and tingling in her feet, and difficulty finding the right word when speaking. She asks her doctors if they think she’s going to get better, stay the same or get worse again.

“I have been told so many times, ‘We don’t know, you’re the first. We’re just going to have to wait and see,’” she says. “I definitely am thankful for every day I have.”

This article originally appeared in Knowable Magazine, an independent journalistic endeavor from Annual Reviews. Sign up for the newsletter.

The post A Revolutionary Cancer Treatment Could Transform Autoimmune Disease appeared first on SingularityHub.

Future

These Companies Say AI Is Reviving Entry-Level Jobs, Not Killing ThemLindsay Ellis | The Wall Street Journal ($)

“In one of the biggest surveys on employers’ graduate hiring plans this year, nearly three times as many executives at companies using or exploring AI said they were increasing junior-level hiring in 2026 than cutting back. Those using AI most extensively were the most bullish, according to Strada Education Foundation, which surveyed about 1,500 employers.”

Robotics

The Internet Can’t Stop Watching Figure AI’s Humanoid Robots Handling PackagesJeremy Hsu | Ars Technica

“The promotional robot demo has become a viral sensation among tech enthusiasts, spurring YouTube commenters to name the robots and the company to rapidly roll out related robot merchandise in response. …But despite such sentiments, it’s worth bearing in mind that even the most impressive robot demos represent narrow windows for understanding real-world robot capabilities.”

Robotics

Will Robotics Have a ChatGPT Moment?Jonathan W. Hurst and Hans Peter Brondmo | IEEE Spectrum

“We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough. As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics.”

Computing

New Quantum Processing Technology Points to Life After the Transistor, MaybeTom Hawking | Gizmodo

“The paper describes how a team from the University of Tokyo took a radical approach to the problem: they did without transistors entirely. Instead, their ‘non-volatile quantum switching element’ uses the spin of an individual electron to represent the state of a given bit.”

TECH

Why SpaceX Is Worth $700 Billion, Not $1.75 TrillionMartin Peers | The Information ($)

“In other words, anyone who buys into the company at the vaunted $1.75 trillion valuation (that’s at least what bankers are hoping SpaceX will achieve) is paying $1 trillion for the promise that SpaceX will overcome major technological hurdles and launch an orbital cloud-computing service, as well as industrialize the moon. It’s admirable Musk is shooting for the stars—but investors need to know what they’re getting into.”

Biotechnology

Colossal Biosciences Is Growing Chickens in a 3D-Printed Artificial EggshellAntonio Regalado | MIT Technology Review ($)

“The biotech company today claimed it has developed a ‘fully artificial egg’ as part of its effort to resurrect extinct avian species, including birds like the dodo and the giant moa. But ‘artificial eggshell’ would probably be a better description for the invention. It’s an oval-shaped printed lattice, coated inside with a special silicone-based membrane that lets in oxygen, just as a real eggshell does.”

Energy

Soaring Solar and a Surge in Hydro Push More Coal off the US GridJohn Timmer | Ars Technica

“Compared to the same quarter the year earlier, solar was up by 24 percent. On its own, that was enough to offset 80 percent of the rising demand. Overall, the output of the major renewables (wind, solar, and hydro) grew by 11 percent compared to the same period the year prior, or about 1.8 times the growth in demand.”

Artificial Intelligence

Even If You Hate AI, You Will Use Google AI SearchSteven Levy | Wired ($)

“To answer a query on black holes, AI agents [in Google’s new AI search] might whip up an interactive graphic explaining how they work. But information has to come from somewhere. The raw material for that was the hard work of cosmologists, science writers, and visual artists, none of whom are easily credited or surfaced. These types of creators—and the web sites that hold their work—seem to be the losers in this transition.”

COMPUTING

US Government Takes $2 Billion Equity Stake in Nine Quantum Computing Firms
Joe Miller and Michael Peel, Financial Times | Ars Technica

“The US government will take equity stakes worth a total of $2 billion in a slew of quantum computing companies, including a startup backed by a firm with links to the Trump family and one taken public by a Pentagon official. The announcement by the commerce department that it had signed letters of intent with nine companies—including GlobalFoundries and IBM—sent shares in quantum specialists soaring on Thursday.”

Energy

The Quest for an Elusive Clean Fuel Is Moving UndergroundBrad Plumer | The New York Times ($)

“A start-up called Vema Hydrogen has drilled two test wells into the bedrock, each 1,000 feet deep, and is starting to inject treated water into the iron-rich rocks below. The goal is to trigger a special type of chemical reaction that could eventually produce large quantities of hydrogen, a clean-burning fuel that may one day play a vital role in tackling climate change.”

The post This Week’s Awesome Tech Stories From Around the Web (Through May 23) appeared first on SingularityHub.

Strong opposition kicks in when data center demand surpasses 5% of a country’s power supply.

As the AI boom accelerates, governments and utilities are struggling to keep pace with the industry’s huge energy demands. New figures suggest data centers now consume about 6 percent of electricity in the US, raising concerns about grid capacity and environmental impacts.

Data centers have always been energy-hungry, but the AI explosion is causing computing demand to skyrocket. The biggest data centers now consume as much electricity as small cities and are proliferating at breakneck speed.

A new report from the International Data Center Authority (IDCA) finds that the total power draw of all these facilities has now hit 67.7 gigawatts—a 36 percent jump over two years. The US alone accounts for 29.2 gigawatts of that total, roughly 43 percent of global consumption.

“Our real-time data shows that many very large AI factories are coming into operation, spiking up total US consumption,” Mehdi Paryavi, CEO and founder of IDCA, told Data Center Knowledge. “The US now devotes 6 percent of its total electricity to data centers.”

That could be a significant milestone, as the report warns that “significant community and political pushback starts to occur in nations once their data center footprints have reached the 5 percent consumption level of national grids.” The US isn’t alone—the UK is now using 5.8 percent of its electricity to power data centers, and in Germany, the figure has hit 9.5 percent.

Opposition is growing.

Hundreds of state-level bills to regulate data centers have been introduced, according to the report. In Maine, the legislature passed a bill that would have barred construction of data centers bigger than 20 megawatts until 2027. Maine’s governor, Janet Mills, vetoed the bill, and the legislature failed to override the veto. But Mills later signed an executive order forming a council to investigate the impact of data centers in the state, with recommendations due in early 2027.

Local planners are also refusing to issue new permits due to energy scarcity. For example, developers in Northern Virginia’s Data Center Alley, a region already densely packed with the facilities, will have to wait until 2032 to launch new projects.

Water usage is an equally important concern in many areas. The vast majority of data centers rely on water-cooled chillers or evaporative cooling towers that can consume millions of gallons daily. A single large facility can potentially draw as much water as 6,500 households. Modern AI facilities increasingly use more modern closed-loop liquid cooling systems that require minimal ongoing water use, but these account for a small proportion of the overall data center fleet.

The report suggests that some of this negative reaction is also self-inflicted. Developers routinely use locally registered entities with generic names that obscure who is actually behind a project, leading to a lack of trust in local communities.

“Before being swept along by the enthusiasm of tech billionaires whose profits depend on this expansion, we should pause and ask ourselves whether it’s worth the price,” Greenpeace UK’s chief scientist Doug Parr told the Guardian in response to the findings.

“We need more transparency about the amount of water and energy used by data centers, proper environmental impact assessments, and a ban on new polluting plants being built to power AI.”

It’s not only new projects putting strain on the grid though. The report found that an estimated 13 percent of US cloud consumption, totaling more than 3 gigawatts, comes from so-called “zombie” workloads—abandoned test environments and unused applications that continue to draw power without doing any useful work.

In addition, there are thousands of smaller data centers embedded in corporate buildings and regional offices drawing considerable amounts of power. These are often missed by consumption estimates that typically focus on large hyperscale campuses, but the IDCA says they account for at least 15 percent of total data center power consumption, in part because they are considerably less efficient than their larger counterparts.

The problems are only likely to get worse though, as tech companies show no signs of slowing down. Annual global data center spending is approaching $1 trillion, with up to $700 billion anticipated in the US alone in 2026, the report notes.

Whether grids will be able to absorb all that new capacity, and how hard local communities fight back against developments, may well end up being a deciding factor in whether the AI boom keeps rolling or fizzles out.

The post Data Centers Now Consume 6% of US Electricity—and the Backlash Has Begun appeared first on SingularityHub.

Researchers used two AI systems, Robin and Co-Scientist, to collapse the timeline from idea to drug candidate.

Uncovering nature’s secrets is no easy task. The daily life of a scientist is often grueling, frustrating, and—perhaps surprisingly—boring as they repeat experiments over and over.

Here’s where AI could lend a hand. This week, two studies offer a glimpse into a future where AI and scientists bounce ideas off each other and collaborate on projects to benefit humanity.

Both systems rely on large language models in end-to-end scientific discovery. They read through existing literature, generate hypotheses, suggest relevant experiments, and analyze and interpret the data for scientists to evaluate. The researchers then give the AI feedback, and the cycle begins again.

One of the systems, called Robin, was instructed to find drugs for a common eye condition. Developed by FutureHouse, a non-profit that builds AI systems to automate research in biology and other scientific fields, Robin quickly homed in on candidates. According to the team, the AI slashed research time 200-fold compared to scientists working alone.

The other system is Google DeepMind’s Co-Scientist. With human guidance, Co-Scientist found already approved drugs that could be repurposed for a type of leukemia within hours. It also surfaced promising targets for liver scarring. The system wasn’t tested in-house; it was distributed to other teams to integrate into their particular fields and workflows.

AI companies are racing to design agents that automate scientific discovery. But both teams stress their systems are collaborators, not replacements. Scientists crafted each project’s vision, checked the agent’s output, and guided its work, like a professor tutoring a bright student.

“These projects represent a significant step forwards,” wrote the editorial team at Nature, where both studies were published. “But for all the ‘wow’ factor, it is crucial to bear in mind that the AI systems were not working alone.”

Nobelist Pursuit

Scientists have a complex relationship with AI.

Nobel Prize-winning protein-prediction models have helped researchers make progress on previously undruggable targets, especially in complex diseases like cancer. Scientists are increasingly asking chatbots for help coding, writing articles, and even inspiring new ideas.

But the problem of AI slop in science is worsening: The bots are polluting scientific literature. Tens of thousands of articles in 2025 contained faulty references hallucinated by AI. Some scientists are uncomfortable with AI’s notoriously hefty energy consumption and worry over-reliance could erode cognition, judgment, and creativity. In a phenomenon called the “illusions of understanding,” AI solutions make us overestimate what we know.

Love or hate it, AI’s impact on research is growing. In the past few years, multi-agent systems, some with sophisticated reasoning abilities, are beginning to break complex problems into solvable chunks and “self-reflect” on their output.

Robin and Co-Scientist showcase this power in a cornerstone of scientific discovery: Suggesting novel, rigorous, and testable ideas when faced with real-world problems such as drug discovery.

Flurry of Ideas

Both systems use large language models to create AI agents that work semi-independently on different parts of a problem.

FutureHouse’s Robin, for example, was tasked with finding a treatment for a dry-eye disorder that’s a common cause of blindness. The agents scoured troves of scientific literature, including hundreds of thousands of open source papers, patents, and clinical trial data.

Rather than inventing a drug from scratch, the team asked Robin to repurpose existing drugs, a common strategy for speeding treatments to patients, and one particularly well suited to AI.

Robin can “consider tens of thousands of biological mechanisms…that could address the underlying cause of that disease,” study author Sam Rodriques, founder and CEO of FutureHouse, told Nature.

Armed with that knowledge, Robin took the role of research lead and recruited other AI agents to design lab experiments around potential drug candidates. In what the team called a “tournament of ideas,” the agents debated hypotheses, weighed evidence from previous studies, and selected the best for testing. The system then suggested experiments for validation.

Human scientists took over from there. They ran the suggested experiments and fed the results into another AI agent specializing in data analysis. After several iterations, Robin flagged ripasudil—a drug approved for glaucoma—as a promising candidate. The drug acts on immune cells, instead of eye cells, and hadn’t been explored for the condition. Early cell experiments were promising.

Co-Scientist works similarly but also incorporates DeepMind’s earlier experience building game-playing AI models. Faced with a scientific challenge, its agents have time to evolve hypotheses, test their reasoning, and rank ideas by plausibility and novelty.

DeepMind first released the AI in early 2025 to a small group of researchers. It’s been used by independent teams studying liver scarring, neurodegenerative diseases, and aging.

At Stanford University, for example, Gary Peltz used the system to find three promising drugs for chronic liver disease. Two worked well in the lab. One, to his surprise, was already FDA-approved for another disease. “When I saw that it was really quite striking. I kind of fell off my chair,” he said.

Beyond drug discovery, Co-Scientist has also worked on decades-old biological mysteries, like why many bacterial species share the same cluster of genes to resist antibacterial drugs. Scientists have wrestled with the problem for years; the AI system reached the same conclusion in days.

Inspiration Galore

To be clear, none of the AI-suggested drug candidates have been fully vetted. Even therapies that look promising in early cell experiments often fail once tested in the body.

Still, there’s little doubt that AI is already inspiring eureka moments.

One early Co-Scientist user, Clare Bryant who studies infectious disease at the University of Cambridge, was surprised when the system flagged a protein she’d missed. The protein intersected with biological processes she was already investigating to fight pathogens. “I spent the rest of the week itching to get back to the lab” to test the theory, she said.

Both teams took care to limit AI hallucination, where systems confidently present false or misleading information. Co-Scientist, for example, includes an internal “review board” that tests hypotheses against existing evidence to keep them grounded in reality. Meanwhile, Robin uses a built-in brake that restricts it to established knowledge and limits irrational leaps in logic.

The AI systems are already over a year old, and the field moves fast. Newer systems, such as Edison’s Kosmos, target the entire drug development pipeline. Yet even as the tools grow more sophisticated, researchers continue to stress that human oversight is essential.

“Human messiness, curiosity, and playfulness have fueled countless discoveries, and helped to inform society’s ethical frameworks,” wrote Nature’s editorial team. “AI systems might offer greater efficiency in some instances, but we don’t yet know whether greater efficiency equates to greater insight.”

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Enough hydrogen is leaking from a single mine to power hundreds of homes. Researchers say it’s far from unique.

Hydrogen gas produced by geological processes beneath Earth’s surface has been touted as a promising clean energy source. A new study provides the first solid evidence that it could be a practical and commercially viable option for decarbonizing the grid.

Hydrogen is an energy-dense fuel that produces only water when burned. But today, the vast majority of industrial hydrogen is manufactured using fossil fuels in an energy intensive process, negating its green credentials. While there’s hope renewable energy could one day power the process and provide a reliable source of green hydrogen, that technology is still a long way from commercial viability.

Recently though, there has been growing excitement about the possibility of vast natural hydrogen reserves stored deep underground. Several large deposits have been discovered and estimates suggest that trillions of tons of the gas could be sitting beneath our feet.

So far, those estimates have been almost entirely theoretical, based largely on near-surface measurements, proxy data, and extrapolation rather than direct observations. Studies have also typically brushed over the complexities of storing and distributing hydrogen gas, which needs to be kept at high pressure or extremely cold temperatures.

A new study, published in PNAS, firms up the numbers. The authors track the release of natural hydrogen over an 11-year period from a mine in Canada and conclude the site produces enough hydrogen to generate 4.7 million kilowatt-hours of energy annually. That’s enough to power a few hundred homes or an industrial facility and suggests the most promising approach to natural hydrogen could be to use it where you find it, they say.

“We present an alternative vision for the hydrogen economy that can address some of the current challenges arising from the focus to date, that has been largely based on transportation of hydrogen over long distances from source location to markets,” the authors write. “Calculations from this study site show that the amount of locally generated energy has economic value for both industries and communities located on hydrogen-producing rock.”

The new study focused on the Kidd Creek mine near Timmins, Ontario where researchers had collected 11 years of hydrogen discharge data from 35 boreholes between two and three kilometers below the surface.

The authors found that, on average, these boreholes were pumping out between 1 and 3 liters (0.04 to 0.1 cubic feet) of the gas per minute. Across all of Kidd Creek’s nearly 15,000 boreholes, the researchers estimate the site releases more than 140 tons of hydrogen per year.

The hydrogen at Kidd Creek is primarily produced through a process called serpentinization, in which water reacts with iron-containing minerals deep in the crust. More than 70 percent of the continental crust has the potential for this kind of hydrogen generation, the researchers say, suggesting the mine, and its hydrogen output, may be far from unique.

Since the gas is already being vented during routine mining, capturing it would require relatively modest investment, the researchers say.  And hydrogen isn’t the only resource on offer. Sites that produce hydrogen also tend to release methane and helium at predictable rates.

Based on the amount of hydrogen at Kidd Creek, the researchers estimate the site is probably producing 4,200 tons of methane and 140 to 280 tons of helium. The latter could be particularly valuable, given its critical role in cryogenic technologies. With recent supply crunches, further exacerbated by the Iran war, prices have been in the range of $100,000 per ton.

Capturing the gas isn’t always simple, the authors note. Underground microbes can consume it before extraction. It may also require significant investment after capture to separate the gases.

But many communities sitting on hydrogen-producing rock may have a valuable renewable energy source just beneath their feet. And the economic case for exploiting it is looking increasingly solid.

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A new study challenges the idea that consciousness is necessary to make sense of language.

Our brains keep on whirling long after we drift off to sleep.

Each night, the hippocampus, a major hub for learning, replays experiences from the previous day and etches them into memory. And even in deep sleep, neurons in sensory regions of the brain spark with activity when they receive new stimuli, like sounds.

This raises a provocative question: How much is consciousness required to make sense of the world around us?

A new study suggests the unconscious brain can handle far more than simple sensory cues. Recording electrical activity from patients under general anesthesia, a team at Baylor College of Medicine and collaborators found the hippocampus continued processing sounds, words, and speech while patients listened to alternating tones and podcast clips.

Groups of neurons shifted their activity depending on the type of word spoken—nouns or verbs, for example—and predicted the next word in sentences.

“Our findings show that the brain is far more active and capable during unconsciousness than previously thought,” study author Sameer Sheth said in a press release. “Even when patients are fully anesthetized, their brains continue to analyze the world around them.”

Scientists have long thought that language processing, a complex computation, relied on awareness. Anesthesia disrupts large-scale communication across the brain, seemingly making complex language processing impossible. But the new findings suggest that even as global brain dynamics break down, some local circuits retain the ability to process sophisticated information—and, at least for storytelling, predict what comes next.

To be clear, it doesn’t mean that participants were secretly awake. Whether the brain retains local processing power during sleep, coma, or other states of unconsciousness is also up for debate.

But “this work pushes us to rethink what it means to be conscious,” said Sheth. “The brain is doing much more behind the scenes than we fully understand.”

Lights Out

We slip into unconsciousness every night. The brain shifts gears.

Compared to when we’re awake and alert, the mind’s activity patterns change dramatically. The hippocampus reactivates neurons involved in recent learning, rapidly replaying their activity patterns to strengthen neural connections. Elsewhere, the brain generates short bursts of electrical activity called sleep spindles, which shut off communication between regions necessary for processing new information from the outside world. These unique electrical signals are crucial for sorting new experiences and integrating them into long-term memory.

The brain is clearly busy during unconsciousness, but it also seems largely sealed off from its surroundings. Over the past two decades, however, scientists have increasingly realized the sleeping brain remains surprisingly alert.

In one study, volunteers repeatedly exposed to unfamiliar sounds during sleep were able to identify them after waking up. In another, participants hearing their own names or angry voices triggered brain activity even in deep sleep, a phenomenon called “sentinel processing.”

Scientists have also recorded directly from the brains of people with epilepsy, who had electrodes implanted to pinpoint the source of seizures. The researchers confirmed that the auditory cortex—the first region involved in processing sound—lit up with activity, but it appeared disconnected with regions responsible for interpreting meaning.

Similar patterns emerged under other states of unconsciousness. After receiving propofol, a common drug used to induce general anesthesia, patients still showed activity in their auditory cortex, but information relay to higher regions involved in cognition seemed to break down.

Or did it?

“The brain has developed such amazing, sophisticated mechanisms for doing all these complex tasks all day long, that it can do some of these things even without us being aware,” Sheth told Nature. They decided to take another look.

Someone’s Home

The team focused on the hippocampus, best known as the brain’s memory center. Linking it to language processing seems like a stretch. But mounting evidence suggest the hub is responsible for far more than memory. It may also help organize information more broadly, from the mapping of physical spaces to watching other unfolding events like language.

It’s still a niche idea, said Sheth. But the hippocampus could play a much broader role in structuring the world around us—even without awareness. “How is the world organized? The hippocampus may be part of that as well,” he said.

To test the idea, the team recruited seven people undergoing epilepsy surgery. While they were under propofol anesthesia, the team inserted tiny probes into the hippocampus. Called Neuropixels, the implants are thinner than a human hair but packed with over a thousand sensors that eavesdrop on the electrical chatter of hundreds of neurons at once.

The team first played repetitive beeps to three participants, occasionally interrupted by random boops at a different pitch. In the beginning, neurons were indifferent to the oddball sounds. But within 10 minutes, their activity levels showed they were getting better at separating the unexpected tones from the normal ones.

“They learned over time to pay more attention to oddball sounds,” even while the person was fully unconscious, said Sheth.

A second test took things further. The team played 10-minute snippets from The Moth Radio Hour, a storytelling podcast featuring speakers from all walks of life, each with distinct intonations, turns of phrases, and accents.

Across the recordings, specific groups of hippocampal neurons responded to different linguistic features. Some were attuned to uncommon words like “cosmos.” Others tracked grammatical structure, responding differently to nouns, verbs, or adjectives.

The neurons also cared about semantic meaning, or the relationships between words. For example, they seemed to recognize that “cat” is conceptually closer to “dog” than an unrelated word like “pen.” The hippocampus also seemed to anticipate upcoming words based on the context of a sentence, with activity patterns similar to those seen in the awake brain.

“We are always making predictions about what we’re about to hear next,” said Sheth. Even under anesthesia, these neurons appeared to keep track of the narrative, indicating a “very sophisticated form of processing of the natural speech that they’re listening to.”

Despite intense neural activity, patients didn’t remember any of the podcast stories upon waking. Still, traces of the experience may have lingered unconsciously. In future studies, the team plans to test for this by exposing unconscious participants to different podcasts then later asking which ones feel familiar. They also want to explore whether the hippocampus processes stories told in unfamiliar languages.

The findings are preliminary, drawn from a small group of people under one type of anesthetic. The sleeping or comatose brain may work differently. But the work could help scientists decipher brain activity in people with severe traumatic brain injuries in a vegetative state. It could also guide the development of implants to rewire damaged neural circuits to other parts of the brain and reboot communication.

“Maybe the most important thing is what can we do about this,” said Sheth. For someone who’s unconscious, “can we bring them back?”

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