2026-06-05 06:17:05
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.
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.
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.
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.
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.
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2026-06-03 22:00:00
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.
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.
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.”
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2026-06-02 08:59:55
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.
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.”
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.
2026-05-30 22:00:00
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.”
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.”
3D-Printable Humanoid Legs Let Robotics Experiments Run WildJeremy Hsu | Ars Technica
“A $2,500 pair of humanoid robot legs built from 3D-printed parts and off-the-shelf components is not going to win marathons just yet. But such relatively inexpensive hardware could enable researchers to more easily test and train AI-powered robotics software in a physical body during real-world experiments.”
Pancreatic Cancer Halted by Virus Injection in Three PatientsAlice Klein | New Scientist ($)
“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.”
A Reality Check on the AI Jobs HysteriaDavid Rotman | MIT Technology Review ($)
“Haven’t you heard? White-collar jobs are going away, decimated by AI. …But before you quit your job as a software developer or financial analyst—or tech journalist—and look to join the plumbers’ union, it’s worth considering today’s economic research on whether artificial intelligence has actually begun to devour white-collar work. The short answer is: No.”
The AI Superstars Who Say a ‘Vibe Slop’ Crisis Is ComingChristopher Mims | The Wall Street Journal ($)
“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.”
Mirror Life: Scientists Clash Over Threat of Lab-Engineered BacteriaJames Woodford | New Scientist ($)
“Microbes based on mirror images of molecules in the natural world would have a hard time surviving outside the laboratory, according to a modeling study. To do so, they would need a ready supply of ‘mirror food,’ or some novel way to feed themselves. But the research has drawn a backlash from other experts in the field who warn that it may underestimate the grave risks posed by so-called mirror life.”
Uber President Says AI Spending Is Getting ‘Harder to Justify’Jess Weatherbed | The Verge
“After reportedly exhausting its annual AI budget just four months into 2026, Uber is now questioning whether it’s actually seeing meaningful returns on its investments. In an interview with Rapid Response, Uber president and chief operating officer Andrew Macdonald said the company isn’t seeing a connection between rising token consumption for Claude Code and more useful features being delivered to consumers.”
Illinois Lawmakers Just Passed America’s Strongest AI Safety BillMaxwell Zeff | Wired ($)
“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.”
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.”
I’m a Professional Fact-Checker. AI Is Wrong More Than You Think.Meghan Herbst | Wired ($)
“Over the past year or so, more and more people have looked at me with great pity. Surely a fact-checker at a magazine isn’t long for this AI-upgraded world. Call me foolish, but I’m not that worried. Very little of humanity’s collective knowledge, I’ve concluded, lives on the internet. And according to my research, AI is even more wrong than people might think.”
Millions of Planets Might Form Around Supermassive Black HolesJonathan O’Callaghan | New Scientist ($)
“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.
2026-05-30 07:24:39
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.
2026-05-29 05:19:02
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.
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.
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.
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.
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.”
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.
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