It’s a hot GLP-1 drug summer. Semaglutide—better known as Ozempic and Wegovy—seems to be everywhere. The blockbuster weight loss aid mimics a natural hormone that tells the brain “you’re full,” making it easier to shed pounds without the constant hunger and cravings that make traditional dieting miserable.

The drug may also have longer effects. A new study in mice suggests that starting semaglutide in old age extends lifespan roughly 12 percent. The mice regained their curiosity and memory and improved on myriad age-related hallmarks. Chronic inflammation cooled. Senescent “zombie cells” dwindled. Their genomes and proteins became more stable, and the hippocampus—a brain region crucial for learning and memory—sprouted new neurons.

The findings are reminiscent of calorie restriction, a way to boost longevity and stave off age-related health problems, at least in lab animals. But sticking to a diet for years, let alone decades, is tough. Scientists have long searched for a drug that could deliver some of the benefits without hunger pangs. Semaglutide seems to fit the bill, with surprising perks beyond dieting.

“If these results from mice hold true in people, this is a promising hint that people taking GLP-1R agonists [GLP-1 drugs] may have an age-slowing benefit as well as benefits to reducing diabetes and obesity,” said Tara Spires-Jones at the University of Edinburgh, who was not involved in the study.

But don’t go ordering GLP-1 drugs online just yet. The study used inbred female mice, whose physiology differs from that of women aging through or beyond menopause. Rapid weight loss in people taking these drugs can also reduce lean muscle, potentially increasing fragility in older people. And while GLP-1 drugs are already being tested in the battle against neurodegenerative disorders, whether they sharpen the aging brain remains an open question.

Still, longevity researchers are cautiously optimistic.

“The findings reframe a key question that has often been asked back to front,” wrote Maria Fernandez and Rafael de Cabo at the National Institute of Aging, who were not involved in the study. “Rather than considering the broad health benefits of GLP-1 drugs as something to be explained one disease at a time, these results suggest that the positive effects have a common cause: slowed aging.”

Fountain of Youth

One way to live healthier and longer is seemingly mundane: adopt a healthy lifestyle.

Diet and exercise have repeatedly been linked to better health in our twilight years. As we age, our bodies slowly break down. Damage and mutations accumulate in DNA. Telomeres, the protective caps at the ends of chromosomes, grow shorter, contributing to genomic instability. The molecular switches that turn genes on or off go haywire, and our cells become less able to make working proteins and clean up damaged ones.

There’s more. Mitochondria, the cell’s power plants, struggle to produce energy and leak toxic molecules. Damaged cells stop dividing, lose their function, but stubbornly refuse to die. Instead, they leak a toxic chemical soup that damages tissues. Chronic inflammation flares, and stem cells run out of steam, making it harder for the body to regenerate or repair itself.

Together called the hallmarks of aging, this laundry list has long challenged scientists looking for a silver bullet against the march of time. They have discovered some exotic options. Transferring components of young mice’s blood to older recipients has rejuvenated faltering hearts, kidneys, and brains. And clearing senescent “zombie” cells with drug cocktails or genetic engineering has attracted billions of dollars in investment.

But perhaps the most studied, and most robust, intervention is caloric restriction. In flies, rodents, and other lab animals, slashing energy intake without causing malnutrition has repeatedly extended lifespan and delayed multiple age-related diseases. Restriction triggers broad changes, including improved metabolism and insulin sensitivity. It also helps prevent damage to cells. In humans, moderately reducing calories seems to improve heart health and slow the speed of biological aging.

Sticking to a diet for years on end, however, is hardly sustainable.

Cheat Code

GLP-1 drugs may make it easier.

Originally designed to control blood sugar and appetite, the drugs have also shown promise for reducing rates of cardiovascular, kidney, liver, and neurodegenerative diseases, at least in people with obesity or Type 2 diabetes. Clinical trials are now exploring their effects in people with metabolic liver disease, which becomes more common and consequential with age.

Not all these effects can be explained solely by weight loss, raising a bigger question: How can a single class of drugs influence so many seemingly unrelated conditions that often crop up with age?

“If GLP-1 medications slow down the aging process itself, a wide range of clinical benefits is

exactly what would be expected, because aging is the root of most chronic diseases,” wrote Fernandez and de Cabo.

To test that theory, the team gave daily semaglutide injections to 20-month-old female mice—roughly comparable to women in their early-to-mid 60s—for as long as they lived.

Compared to a group of mice given saline, the treated mice lived an average of 834 days, versus 724 days for a control group. Both groups could feast on standard chow to their hearts’ content. After just three months of treatment, the mice taking semaglutide were more lively and curious than their peers. They readily explored new environments, balanced better on a skinny rotating rod, ran faster on a tiny treadmill, and solved mazes more quickly.

Under the hood, semaglutide blunted the hallmarks of aging across the board. Stem cells in the bone marrow and hippocampus sprouted, suggesting renewed regenerative capability. DNA damage and protein and energy dysfunction declined. Zombie cells partially disappeared.

The results weren’t simply a consequence of eating less. In another three-month experiment, the team compared the drug with a calorie-restricted diet that cut energy intake by 24 percent—the same reduction seen in the semaglutide-treated mice.

The drug seemed to have a leg up. Compared with caloric restriction, it produced more improvement in cognition and blood sugar control, without the metabolic adaptations normally driven by hunger, such as lowered energy expenditure. The mice also showed fewer signs of hunger. They ate on a normal schedule rather than prowling for food before feeding time and gobbling rations once available.

Genetic sequencing of the liver found semaglutide triggered similar molecular signaling pathways as caloric restriction, like for example, those that sense nutrient availability, cell stress, and proteins involved in longevity. Both interventions tamped down inflammation and boosted genes involved in handling fats, but they didn’t follow the exact same biological playbook.

The findings raise the “intriguing question of whether GLP-1 drugs target an alternative biological route into aging that has its own side effects and therapeutic ceiling,” wrote Fernandez and de Cabo. Exactly how that route works remains unclear, but the team is eager to find out.

The findings are promising, but the study also has major limitations. It didn’t directly compare dieting and semaglutide for lifespan extension. And because gender affects the aging process, the team will need to see if the results hold in males. Then there’s the potential loss of lean muscle, a serious concern for people who are already frail or saddled with age-related health conditions.

Even so, “semaglutide is probably the best caloric-restriction mimetic I have seen,” Tim Rhoads at the University of Wisconsin–Madison , who was not involved in the study, told Chemical and Engineering News.

Untangling semaglutide’s bonus effects could ultimately reveal new ways to slow—or even rewind—aging’s ticking clock.

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Scientific research often depends on complex laboratory equipment that only specialists know how to use. But Anthropic is now rolling out a system that allows AI agents to control lab devices and autonomously carry out experiments.

Laboratory automation technology has been around for decades but getting different bits of equipment to talk to each other has traditionally been a major headache. Most instruments use their own proprietary interfaces, so connecting a microscope to a robotic arm or a liquid handler typically requires bespoke software that takes specialists weeks or even months to build.

Anthropic says its new Model Hardware Standard can reduce this process to minutes by giving devices a common language. It relies on a standardized “driver” that lets any programmable device describe itself to an AI agent, allowing the AI to handle the integration. The company announced it’s opening the system up as a research preview to an initial group of labs and manufacturers.

“Our hope is that the standard can be of use to researchers, engineers, and other practitioners in speeding up the process of discovery and experimentation in any domain that uses devices with a programmable interface,” Anthropic said in a press release.

The standard is similar to Anthropic’s Model Context Protocol, which makes it easier for AI to interact with third-party software, but the new system is aimed at hardware instead. The driver at its heart is essentially a piece of software that sits between a computer and a piece of hardware, translating instructions from one into signals the other can act on.

Most laboratory instruments already run some form of driver, but each has traditionally spoken its own dialect, which is why connecting them has required custom code that can translate between devices. Anthropic’s new driver standardizes that dialect using deliberately simple commands such as “read” or “write,” which can refer to anything from checking a temperature to setting the length of an operation.

Because every device speaks in these same basic terms, machines can find each other on a network and exchange data without a custom program to translate between them. The driver also makes it easier for the company’s Claude agents to learn how to use a device they’ve never seen before.

The standard lets users encode key details, like the weight of a robotic arm, using natural language.  They can either write out their hardware setup themselves or have an agent interview them about it. The system then turns that information into a reference file covering what a device can measure, what can be adjusted, and what safety limits apply.

Anthropic says this lets its agents orchestrate complex experimental processes across multiple instruments in often highly complicated and interactive ways. “We’ve found that Claude interacts with experiments and hardware in an exploratory manner, much as a scientist would,” the company writes. “We observed Claude make an adjustment to a laser, observe the results through a camera to assess how its adjustment moved the laser beam, and repeat the process, seeking to understand the sequence of events.”

Speaking to the Financial Times, Anthropic scientist Alek Kemeny described watching Claude locate a specific, unfamiliar structure in a live brain tissue sample during a neuroscience experiment by manipulating a microscope’s mirrors and lasers on its own. “The neuroscientist sitting there said: ‘Yep, that’s right,’” said Kemeny.

The new standard could be key to the company’s ambition to move beyond its key markets of software development and knowledge work and allow its AI to start having an impact in the physical world. But allowing AI, which is still not immune to hallucinations, to control real-world hardware carries considerable risks.

“It is an impressive proof of concept, but how do we ensure safety in the physical world? Because small errors can matter here,” Kaoutar El Maghraoui, principal research scientist at IBM, said on the company’s Mixture of Experts podcast.

That’s probably why Anthropic is only releasing the standard to a small number of partners initially, and it has committed to working with them to build safety evaluations for AI systems that are operating physical hardware. If the early launch goes well though, AI agents could soon make an impact in far greater swathes of the economy.

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For 30 years, the world wide web has run on a surprisingly profound social contract. Most sites are free for search engines to access, but if you use their content, you give credit by linking to the source.

Recently, that social contract has begun to collapse. Artificial intelligence tools are crawling sites not to link to them, but to train models and generate answers (which may or may not be accurate).

When you search for something, ChatGPT’s response or Google’s AI Overviews may still include links to sources, but they’re a kind of optional extra to the main answer.

This has triggered a bad dynamic for website owners, the public, and even AI companies themselves. As websites lose traffic (and revenue), many are beginning to block AI scraping tools, meaning AI results depend more on low-quality websites (many of which are also generated by AI). As a result, good information can be harder than ever to find.

How We Got Here

In the early days of the world wide web, search engines, and content creators came to an agreement about crawling (the practice of technologically examining a site to index it, so it can be served up in search results). Content creators would provide access to their sites for free and even allow search engines to reproduce small snippets of text.

In return, search engines provided links to the sites owned by content creators, who benefited from that web traffic. If content creators didn’t like the deal, they could prevent search engines from crawling their site with instructions in a file called robots.txt.

But if AI tools no longer provide web traffic, it cuts content creators out of the economic loop. There are also other costs associated with each visit to a website, so AI crawling can cost website providers money while not giving them any of the ad or other revenue that would come from human traffic. AI crawlers also crawl more deeply and more intensely than traditional web crawlers, magnifying that cost.

This change in traffic patterns isn’t a small or hypothetical problem. Cloudflare, a web hosting and service company that manages 30 percent or more of the top 10,000 sites on the internet, estimates over half of all web traffic is now AI bots.

Some of this will be AI agents supervised directly by people, but the majority will be crawlers. Site owners can use robots.txt to ask AI crawlers to stay off their sites—but some AI companies may ignore this polite request.

If the AI companies do honor the request, that can create a different problem. Sites containing misinformation are far less likely to ban AI crawlers, so the AI answers won’t be informed by high-quality sources.

What’s Happening in the Short Term

On the horizon is an event dubbed “Google Zero”—the day when through-traffic from Google drops to nothing. While some grey-haired diehards (like one of the authors of this piece) might still click through to verify AI answers, this traffic is rapidly dwindling, as a direct result of AI summaries.

A study of Wikipedia confirms this, showing that traffic in the English language version of the site dropped off quickly with the launch of AI summaries on Google in English, and that the same pattern occurred in other languages as AI summaries were rolled out. Never having to click through to get an answer might seem great for information seekers, but the reality is more complex.

Many sites are now blocking AI crawlers altogether. Site owners who decide to block AI crawlers are less likely to be linked in AI Overviews answers, even when the AI tool can still access the content to ground its answers (using a technique called retrieval-augmented generation).

Alternative “pay to crawl” models have been suggested as a way to compensate content creators, but haven’t gained traction.

Come September 15, Cloudflare sites will block AI crawlers by default on pages that contain advertising (and therefore make money for content creators).

This means up to 30 percent of the world’s top sites will no longer appear in Google AI Overviews summaries. It also means that much of what AI is being trained on will itself be AI-generated text.

What It Means for You

So what does this mean when you’re looking for information? The quality of AI summaries is likely to go down, at least in the short term, while the new economics of the web get sorted out.

This will happen for two reasons. The first is that high-quality content is less likely to go into those AI summaries—one recent study found that already, around 1 in 6 sources used by AI search tools is itself an AI-generated website.

The second reason is that, as AI models are trained on more AI text, their output may degrade (a phenomenon known as model collapse).

As a result, search engines that depend less on AI may become more reliable. The challenge is finding one that doesn’t use an AI-based crawler. They do exist. ZDNet recommends Mojeek, PCMag recommends Brave, and Ban the Bots lists several, including one specifically for “small producer” content such as blogs.

For now, whatever search engine you’re using, the best thing you can do is to scroll down and click on some actual search results. This benefits content creators and is also more likely to give you more accurate information.

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

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The Alzheimer’s brain faces a double whammy. Toxic protein clumps build up inside and outside neurons to torpedo normal function and destroy delicate structures. Eventually, the cells die. The adult brain has an extremely limited ability to grow new neurons. Once gone, they’re rarely replaced. Over time, the brain withers, taking learning, memory, and cognition with it.

But there might be a sneaky workaround. The brain is packed with star-shaped cells called astrocytes that keep neurons healthy. They’re also shape-shifters. Under certain conditions, astrocytes can shed their identity and transform directly into mature neurons. In other words, they could be an abundant, untapped source of replacement neurons.

A team at the University of South Carolina has now taken advantage of this quirk. They engineered a tiny molecular cage and filled it with antibodies. Once inside astrocytes, the antibodies released a protein “brake” that normally keeps the cells’ identity stable. Free from this constraint, astrocytes in lab dishes and human brain organoids adopted the molecular signatures of neurons and eventually sparked with electrical activity.

In mice modeling Alzheimer’s disease, the treatment increased the number of neurons in the hippocampus, a brain region crucial for learning and memory and one of the first to falter in the disease. Treated mice resumed normal behavior and performed similarly to healthy mice on tests of learning and memory in a maze.

The approach fundamentally differs from existing methods and could “unlock previously inaccessible regenerative mechanisms,” wrote the team. If it proves safe and effective in clinical trials—and that’s a big if—the approach could one day tackle diseases beyond Alzheimer’s, such as Parkinson’s or amyotrophic lateral sclerosis (ALS).

Born Identity

The quest to treat Alzheimer’s has often been called the “graveyard of dreams.” The most common form of dementia, the disease affects roughly 24 million people worldwide and slowly eats away at thinking, memory, learning, and emotional regulation. Experts still debate Alzheimer’s root cause, but they largely agree that clumps of misshapen proteins called amyloid beta and tau exacerbate the disease.

Current FDA-approved treatments have had limited success. Antibodies that clear clumps offer only modest benefits to cognition and carry the risk of serious side effects. Other drugs, such as memantine, alter brain chemicals to protect damaged cells, rev up faltering brain circuits, and ease symptoms. But they don’t halt degeneration. As the disease progresses, benefits fade.

The central problem is frustratingly clear. Neurons die faster in Alzheimer’s than the brain can replace them. That’s why a landmark study nearly two decades ago made waves. Scientists once thought mature astrocytes were set in their fate. But the study showed the cells could be reprogrammed into neurons that generated electrical activity and formed connections with neighboring neurons in lab dishes to form working circuits.

Scientists later found a protein called PTBP1 that prevented this conversion. In 2020, a team injected an RNA-targeting form of CRISPR into the brains of mice modeling Parkinson’s disease. This reduced PTBP1 levels, which in turn, triggered the production of new neurons. The treatment restored the mice’s balance and motor skills, although some experts were skeptical.

While promising, CRISPR-based approaches can have unintended effects, and brain surgery is a tall order for any treatment. So, the team developed another way to release the PTBP1 brake.

Erase, Rewind

They turned to a duo of technologies that transport antibodies inside nanoparticle cages to degrade specific proteins inside cells. In this case, they used antibodies targeting PTBP1 and packaged the concoction in a biocompatible gel injected into the bloodstream.

Because of their large size, antibodies can’t usually cross the blood-brain barrier, a tightly sealed wall that keeps many molecules out of the brain. But the nanoparticle system helped ferry the antibodies across the blockade, nixing the need for brain surgery.

The team first tested the drug, called TN-PTBP1, on astrocytes grown in lab dishes. Within days, the cells lost their star shapes and began growing long, willowy branches characteristic of neurons. Their molecular profile also shifted, and the cells eventually burst with electrical signals.

The team recorded similar results in brain organoids, or “mini brains,” grown from human stem cells. Given a small electrical zap, the converted neurons responded in synchrony with neighboring neurons, suggesting they had integrated into existing neural circuits.

“The new neurons can become mature and survive,” said study author Peisheng Xu in a press release.

Next, they tested the drug in a mouse model of Alzheimer’s disease. By eight months, the mice showed clear signs of the disease. Their brains were highly inflamed and littered with toxic protein clumps. Neurons in the hippocampus had also substantially died off, similar to the loss seen in moderate to severe Alzheimer’s in humans.

The mice struggled with everyday behaviors, such as foraging for material to build nests. And they consistently performed poorly on a classic memory test where they had to find a location using visual cues (a bit like remembering where you parked your car).

Half the mice received TN-PTBP1 for two weeks; the others received saline. As expected, the drug reliably slashed PTBP1 levels in the brain. Over the course of the trial, treated mice increasingly improved on tests of cognition and memory, eventually performing at levels similar to healthy peers. Mice treated with saline showed no improvement.

“After just two injections, these mice became smarter,” said Xu. “Even after one injection, we already saw these mice’s behavior differ from that of the nontreated ones.”

The team found broader benefits too. The drug reduced inflammation and, surprisingly, the number of toxic protein clumps, suggesting it may have helped restore some of the brain’s ability to rid itself of waste. Neuron density also increased throughout the brain, and the treatment boosted production of proteins involved in maintaining the blood-brain barrier, which is often damaged in Alzheimer’s.

One unexpected, and welcome, effect was neurogenesis, the birth of new neurons in the hippocampus and another brain region. Neurogenesis declines with age, and whether it exists at all in adult humans is hotly debated. How TN-PTBP1 triggered it in mice remains a mystery. It’s also unknown how much the new neurons contributed to the animals’ recovery versus the direct conversion of astrocytes into neurons.

Still, it’s clear the drug boosted neuron numbers and “successfully reversed Alzheimer’s disease progression” in the mice, wrote the team.

The approach has a long road ahead. Many promising treatments in mice have failed in clinical trials. In the next few years, the team hopes to test the approach in monkeys, dial in the dose, and assess long-term safety. Astrocytes perform many tasks that keep the brain humming, and forcing them to abandon their identity could have unexpected consequences. There’s also the possibility newly converted neurons could scramble existing brain circuits rather than integrating safely, causing more harm than good.

But with rigorous testing, the drug could offer new hope.

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There’s growing excitement in the AI industry about the idea that today’s leading models could build the next generation of the technology. But a new study recently found top AI agents struggle on the kind of genuinely open-ended research problems required to push the field forward.

Large language models have made rapid progress in many of the day-to-day jobs involved in machine learning research, such as writing code, generating and curating data, and running experiments. Last year, startup Sakana AI’s AI Scientist-v2 even managed to write a paper that cleared peer review for the prestigious International Conference on Learning Representations.

These advances have led to speculation that models are close to being able to build better versions of themselves with little human oversight—a process called recursive self-improvement. The idea underpins predictions that we may be on the verge of an intelligence explosion that could quickly lead to AI superintelligence.

In a recent paper, researchers put the idea to the test using a new approach they call shadow evaluations. This involves taking the research question from a high-quality, unpublished machine learning paper and asking AI agents to solve the problem. The original paper’s authors then grade the results. When the team tested Claude Opus 4.8 on two papers submitted to the prestigious machine-learning conference NeurIPS 2026, the authors rejected both.

“The papers were nowhere close to the mark when it came to being at the quality of a top AI conference,” Sayash Kapoor from Princton University, who co-led the study, told MIT Technology Review.

Previous efforts to get AI agents to do machine learning research have often targeted problems focused on engineering, such as reproducing previous research or training smaller models against a benchmark.

In the new experiments, the researchers challenged models with more open-ended tasks that required them to devise hypotheses, decide what evidence is needed to validate them, judge when a research direction was fruitless, and go back to the drawing board.

One research question was whether the personality traits a language model displays can be measured and adjusted by observing and editing its weights; the other attempted to detect when a model that works with tabular data has quietly stopped being reliable.

In each case, the AI researchers were given $3,000 of API credits, a budget for time on GPUs to run machine learning experiments, a dedicated Linux virtual machine, and unrestricted internet access. They were then given six days to produce a paper that could pass NeurIPS’ stringent peer-review criteria.

In both cases, the models got a good start. The agents surveyed the literature effectively, came up with opening hypotheses that mirrored those of the authors, and successfully ran hundreds of experiments.

But they quickly went off the rails. Although they could monitor their own use of time and their API and GPU budgets, they rushed through the process. One left 110 hours of unused time on the clock, and both failed to spend even 50 percent of their API budget.

Both agents also settled on a research direction within just 10 hours and failed to change approaches despite repeated negative feedback from another AI designed to review drafts of their papers. The reviewer identified problems the human authors would also flag in the final paper, but the models simply added caveats to their findings and ploughed on. Ultimately the papers received a “strong reject” and a “reject” decision from the human reviewers based on NeurIPS grading protocol.

The authors admit their approach has limitations. The reviewers knew AI had written the submissions, and some of the team are on record as doubting an imminent intelligence explosion. The original human-authored papers also took far longer than six days to produce and used many more GPU hours to reach their conclusions (though, as the researchers note, the models did not use their allocated budget in any case).

Nonetheless, the results suggest that today’s models still have some way to go before they can tackle the most challenging problems in machine learning research. Until that happens, the dream of recursive self-improvement is likely to remain a distant prospect.

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Last week, Australia’s Fair Work Commission ruled Gregory Baker, a computing academic at Macquarie University, should be treated as an ongoing, part-time employee, after the university had earlier declined his request to convert from a casual role.

It was immediately described as a “landmark” decision, the first test of Labor’s “employee choice pathway” reforms passed in 2024.

But the ruling also made headlines for other reasons. Baker represented himself at the tribunal and has said he won with the help of trained artificial intelligence agents. His success again has us asking: Can AI replace lawyers?

On closer scrutiny, Baker’s case looks less like evidence of AI replacing lawyers and more like a powerful illustration of how a highly capable user can employ AI tools to terrific effect.

Request Denied

Speaking to the Australian Financial Review following the ruling, Baker said it was actually an AI tool that alerted him to the possibility of converting his role from casual to permanent part-time in the first place.

He had been teaching computer science at Macquarie University over consecutive semesters from 2023 to 2025, and in November 2025, he gave the university the required notice that he believed his work no longer met the requirements of casual employment.

The university did not accept this notification and Baker lodged a dispute at the Fair Work Commission—without a lawyer—in December 2025. The parties could not reach agreement, and the case went to arbitration on May 12. A decision was handed down last Wednesday.

Expert Use of AI

Baker has said he won by using multiple paid AI agents, such as OpenAI’s paid offering, ChatGPT Pro. This “team” helped assemble his case, follow up references, and anticipate his employer’s counterarguments.

His victory has been celebrated as historic, with the Australian Financial Review describing it as “the first known successful use of technology by a self-represented person in the legal arena.”

However, a few things set this particular case apart. Baker’s IT background, expertise managing AI agents, and ability to optimize their use for his case represent a rare level of expertise in using AI in a legal context.

Details included in the Fair Work Commission’s decision also suggest he kept his legal argument narrowly focused on teaching he’d done in one particular unit.

Less expert use of AI in court often sees those bringing claims produce “kitchen sink”-style arguments, which include weak, exaggerated, and nonsense claims.

Baker’s dispute was also narrow, limited to the application of a casual conversion law that had not yet been tested. Importantly, the Fair Work Commission (a tribunal, not a court) is designed to be user-friendly, to enable workers to bring claims without a lawyer.

Less Positive Attention

Elsewhere, the use of generative AI in legal proceedings is attracting a lot of attention for less positive reasons.

Most of this attention centers on the damage caused by inaccuracies, hallucinations and “AI slop”, and how courts and tribunals should best respond.

By making it easier to put a case together, AI has removed traditional access barriers for some litigants. But while case numbers are going up, case precision and quality is going down, making it harder to manage disputes to resolution.

Courts and tribunals are struggling with the volume. At the Fair Work Commission alone, workload has reportedly increased by 70 percent over three years.

New challenges are emerging as time goes on. Reports suggest litigants and lawyers in some overseas jurisdictions are embedding prompts in digital documents (something called “prompt injection”) to overcome or manipulate AI-based review systems some courts use to process documents.

A Big Opportunity

Baker’s example shows us something significant. Used well, AI tools can empower people with narrow legal disputes and digital skills to achieve successful resolutions at low cost.

This is an important development in access to justice. In Australia, there is a huge gap between the number of people with legal problems and the very limited funding available for legal assistance.

Most of the community is in the “missing middle,” unable to afford private legal assistance but on incomes too high to qualify for free legal aid.

AI tools stand a good chance of helping people with sufficient legal capability with problems and cases—like Gregory Baker’s—that are a good fit for the solutions AI can offer. These are few and far between, however.

Where Might Things Be Headed?

We should expect case numbers and self-representation in courts and tribunals will continue to grow and expand beyond Fair Work.

While there will be some baseless cases, the growth also represents the natural consequence of removing one traditional access barrier to our formal justice institutions—getting in the front door to start proceedings.

The bigger picture challenges are persistent and raise important questions. Who will most benefit from the capacity of AI tools to enhance access to justice, and who will continue to struggle to get basic legal problems resolved?

For courts and tribunals, the challenge will be striking a balance between managing caseloads and delivering justice, while not wasting the opportunity to expand access to justice.

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

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Five years is an eternity for brain organoids. Also called mini brains, these blobs of tissue have taken neuroscience by storm for their ability to capture the intricacies of developing brains.

Organoids begin life as a collection of stem cells. Within weeks, they spontaneously produce a range of brain cells. Neurons form circuits that spark with electrical activity. Gene expression resembles that of early fetal brains. Some organoids learn to control small, isolated muscles. Others link to spinal cord organoids and process pain signals.

Over time, they grow more sophisticated in both structure and function—eerily similar to near-term fetuses—prompting bioethicists to ask if they could one day become conscious.

But time isn’t on their side. Most mini brains survive only a few months before their sensitive neurons start to wither. Circuits break down, structures collapse, and eventually the organoids die. As a result, they can model only the early stages of human brain development, leaving what happens during the later months of pregnancy and after birth largely mysterious.

These periods are especially relevant to schizophrenia, epilepsy, severe autism, and a host of other disorders. Scientists have studied late-stage development using donated tissue, but samples are scarce and raise ethical concerns.

A team led by Harvard’s Paola Arlotta is now pushing the boundaries with organoids. Last week, they described a method that kept mini brains alive for over five years—the longest yet—and tracked their development throughout. Despite growing outside the body, the organoids matured on a timetable similar to normal brains. Genetic activity in the oldest ones resembled that of a typical 4-year-old.

The findings were originally reported in a preprint and have now been peer-reviewed and published in Nature.

The developmental lockstep surprised the team. Cells from older organoids, when mixed with younger ones, continued maturing on schedule, suggesting they carried an internal developmental clock that keeps track of their progress.

“The brain doesn’t develop in a vacuum. It’s an organ of incredible complexity that interacts with so many other systems,” study author Irene Faravelli said in a press release. “It was not a given at all that our simplified model would match natural development in this many ways.”

Brain, Interrupted

Because mini brains generate nearly the full range of human brain cells, they’re promising models for the study of early brain development. But early versions survived only a few weeks. Without blood supply, cells at their centers starved and died.

Through trial and error, researchers learned to coax them into increasingly sophisticated structures that included layers resembling the cortex and had integrated blood vessels. This vastly extended their lifespan.

In 2021, a study kept mini brains alive for up to two years, capturing cortical development from pregnancy to roughly a year after birth. Four years later, Arlotta’s team announced a way to extend organoid lives to a staggering seven years. Roughly the size of a pea, each nugget was packed with some two million healthy neurons and other brain cells.

Following these organoids for years offers an unprecedented window into how the brain grows and wires itself—and how genetic changes early on might contribute to diseases later in life.

Our brains take roughly two decades to mature. Throughout this period, neurons constantly rewire their connections. Scientists have long known that conditions such as schizophrenia and some forms of epilepsy first emerge during adolescence. Because mini brains can be grown from a person’s skin cells and retain genetic mutations associated with neurodevelopmental disorders, they offer a way to probe how, and when, neural wiring goes awry.

But timing matters. The question is, how faithfully does a growing blob in a dish follow the developmental journey of a human brain?

Time Stamp

To answer that question, the team grew 34 organoids and tracked them at regular intervals. They collected data every three to six months for the first 18 months, then annually until the organoids were over five years old.

Crucial to the brain blobs’ longevity was switching the growth medium—a nutrient- and protein-rich slurry—halfway through development. The new recipe kept neurons alive longer, giving them time to support increasingly complex activity.

The team then tracked changes in gene activity and epigenetic markers (chemical tags that control which genes are turned on or off). They then compared the findings with data from younger organoids—ranging from 15 days to six months old—and donated human tissue.

The developmental timeline was surprisingly similar to that of a human brain. Young organoids showed gene activity resembling the first trimester; by three to six months, they looked more like second-trimester brains. After a year, their gene activity profiles resembled those of newborns. By the end of the experiment, they most closely matched a typical 4-year-old.

The team also tested them with epigenetic methods used to gauge biological age as opposed to calendar years. The organoids gained and shed epigenetic markers in patterns that broadly tracked those seen in natural brain development.

The organoids seemed to retain a “sense” of time. The team mixed cells from year-old organoids with those from 15-day-old organoids. Both followed their usual trajectory: The younger cells developed into early-stage neurons. But the older ones skipped those stages and rapidly produced more mature neurons often requiring months to grow.

“I like to think of this as a sort of ‘warping of developmental time’ indicating that the organoid cells record and recall the time they have already spent in culture,” said Arlotta.

In other words, the cells seem to carry an internal developmental clock, which could be especially useful for studying disorders with symptoms emerging long after the early stages of development.

To be clear, though, a mini brain resembling a 4-year-old’s brain at the molecular level doesn’t mean it has the same wiring or computational capabilities. Gene activity only captures part of a brain’s development; real brains are shaped by experiences and interactions with the rest of the body. Without input, mini brains can only offer a molecular blueprint of brain development, not its entire rich tapestry.

Still, long-living organoids are a breakthrough. Researchers could freeze cells from organoids at different developmental stages and later thaw them for experiments. This could speed up discoveries because scientists wouldn’t have to grow new organoids from scratch for each new study. Think of it as a save point in video games.

The team plans to grow long-lived organoids from people with schizophrenia or epilepsy and use them to study disease progression and screen drugs. Keeping ethics in mind, they’re also considering exposing mini brains to sensory stimuli such as sight, sound, or touch.

“There is still much to learn about how the embryo naturally builds a progressively more complex and mature brain,” Arlotta said. “Applying these lessons to organoids will allow us to model unexplored events of human brain maturation that occur after birth.”

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Increasingly, companies are building humanoid robots that perform impressive athletic feats to mark the field’s progress. Now, Chinese robotics company Unitree says its new “Superman” robot can run 12.66 meters per second, faster than Usain Bolt’s top recorded speed.

Getting a humanoid robot to run at all requires split-second control and has been a significant engineering challenge occupying roboticists for decades. That’s why sprinting, as well as jumping, have become popular targets for robotics companies keen to demonstrate their technology’s prowess.

Unitree’s latest demonstration pushes the boundaries by not only outrunning the fastest human ever, but also jumping around 6 feet 7 inches into the air from a standing start, a full foot more than the human record.

“This new machine has only been in development for a little over three months, with significant room for further improvement in the coming months,” Unitree said in an X post that accompanied a video of the accomplishments.

The records have not been externally verified, and the sprinting speed was a peak reading taken over a shorter stretch rather than a full 100 meters like Bolt’s record. The robot’s legs are also only 2 feet 9 inches long, according to Unitree, which results in an ungainly, arm-waving gait while running.

The effort is nonetheless impressive and adds to Unitree’s growing reputation as the company leading the pack of humanoid robot developers. And the timing of the announcement was no accident, coming just days before Unitree’s stock market debut and shortly before the World Humanoid Robot Games, which opened on August 22.

The company’s Shanghai IPO was a blockbuster, recording an initial 629 percent gain on the company’s first day of trading. It was briefly valued at around $66 billion before closing at a more modest $51 billion. However, some analysts have cautioned the excitement around the company’s technology may be getting ahead of market realities.

“The IPO is expensive, and the investment ​risk is already ​quite high,” ⁠Wang Zhuo, partner of Shanghai Zhuozhu Investment Management, told Reuters. “Unitree generates much of its sales from research and demonstrations, but ​wider application is still far away.”

But the company holds a dominant grip on the emerging humanoid market that may justify some of the hype. Chinese firms control roughly 90 percent of the global humanoid robot market, with Unitree alone shipping 5,500 of the 13,000 to 18,000 humanoids sold worldwide in 2025, the most of any manufacturer. In contrast, US humanoid champions Figure AI, Agility Robotics, and Tesla each shipped around 150 units.

China’s success is down to “a combination of policy support, public investment, mature supply chain, and advancements made in AI software and hardware,” Lian Jye Su, a tech analyst at consultancy firm Omdia, told Rest of World.

This is leading to an increasingly combative response from the US. On July 29 the Federal Communications Commission banned new imports of foreign-made humanoid and quadruped robots. The move was framed as a matter of national security, though it has also been seen as an attempt to give domestic developers a leg up.

Beijing predictably objected, with foreign ministry spokesperson Mao Ning telling a press conference that “protectionism does not make the US more competitive, and it will only hurt the interests of US companies and consumers.”

Given the rapid progress made by companies like Unitree, it seems likely it’s going to take more than trade barriers for the US to catch up. In the meantime, we might see more human athletic records fall to China’s leading humanoid developers.

The post Unitree Claims New Humanoid Robot Outruns Usain Bolt appeared first on SingularityHub.

Our cherished memories may be more resilient than previously thought.

Long-term memories are stored in synapses, the connections between neurons. These structures sit on tiny protrusions called dendritic spines, which dot neurons’ branching arms.

When we learn, these spines grow. Larger spines tend to form stronger synapses and are more likely to persist during learning. In Alzheimer’s and other diseases that eat away at these connections, memories can fade.

At least, that’s the traditional picture. A new study suggests the story is more complicated.

Mice in artificial hibernation rapidly lost roughly half of their synapses, both large and small. Yet once awakened, they resurfaced memories of previously learned tasks. Spines that had withered during the induced deep sleep regrew in their original spots, once again forming functional synapses. This suggests their brains had rebuilt parts of broken circuits.

A small number of stubborn synapses that survived hibernation may explain how this happened. These synapses formed clusters that preserved memories as patterns of neural activity called engrams. The more surviving clusters the mice had, the better they performed on a previously learned task after awakening.

“It was astonishing. Logically, if all our engram synapses were essential in memory retention as traditionally thought, memory should have massively deteriorated,” said study author Yu-Ju Lin at Japan’s Okinawa Institute of Science and Technology Graduate University in a press release.

The findings suggest that memories may not depend on preserving every individual synapse. Instead, they may be distributed across a higher-level architecture of connections, with some synapses acting as anchors that can reconstruct the rest.

Artificial hibernation is an extreme case, and it’s far too early to know how the findings translate to diseases like Alzheimer’s. Still, they suggest that even under extreme circumstances, the brain can bring back memories once thought lost.

Forest for the Trees

Neurons are often called the brain’s computational units. But each one is actually a sophisticated mini computer in its own right.

A neuron’s branching arms receive signals from neighbors, while a long, winding extension carries outgoing messages to other neurons. Spines dot the receiving branches. These structures can strengthen, weaken, appear, and disappear depending on the input. This allows synapses to simultaneously gather data, learn, and store memories. When neurons repeatedly activate each other, the connections between them grow stronger, mostly because of larger spines. This is the idea behind the popular neuroscience saying: “Neurons that fire together, wire together.”

For episodic memories—the when, where, what, and who of our lives—these changes begin in the hippocampus, a region central to forming and retrieving memories, and one of the first areas damaged by Alzheimer’s disease.

During the day, the hippocampus forms engrams associated with individual memories. During sleep, some of these are erased, while others are gradually incorporated elsewhere in the brain for long-term storage. The hippocampus also helps recall memories by adding context, such as where something happened or how you felt at the time.

All of this should, in theory, require relatively stable brain circuits. “Long-lasting changes in synaptic connections are widely thought to provide the structural basis of memory,” wrote the team.

But recent studies have challenged that view. The brain is anything but static. Synapses are constantly being remodeled. Even which neurons are recruited into a particular engram can change over time. Some synapses may effectively hand off information to others, freeing themselves to encode something new.

If physical traces of memories are always shifting, why don’t our memories disappear with them? That’s the question the new study explored.

Going Under

To probe the paradox, the team turned to an unorthodox method: Artificial hibernation. Like natural hibernation in bears and other animals, artificial hibernation dramatically lowers body temperature and metabolism and causes animals to enter a sleep-like state. As the brain decreases its activity to conserve energy, synapses begin to wither.

Yet hibernating animals do retain memories. Chipmunks, for example, remember where they’ve stored food, returning to their stashes when periodically awakening for “midnight” snacks. This suggests hibernation could be a useful way to study how memories survive major changes in the brain.

“Our brains are incredibly complex. If hibernation can reduce and simplify brain activity and structure, it could make studying these convoluted systems a bit easier,” said study author Kazumasa Tanaka. “That’s why I wanted to use artificial hibernation techniques to study memories.”

The team first trained mice on two standard memory tasks. In one, the critters received a mild electrical zap to their paws inside a chamber with distinctive smells and decorations, teaching them to associate that setting with danger. In the other, they learned to navigate a maze towards a sugary reward.

The researchers then activated a neural circuit that drove the mice into artificial hibernation for two days. Using fluorescent proteins, they tracked changes in the animals’ synapses throughout the process.

Spine remodeling began within minutes. Some rapidly shrank and disappeared, taking their synapses with them. Within a day, over half of the synapses were gone. Even the larger spines thought to be especially important for long-term memories were pruned.

Yet memories survived. When the mice awoke and revisited the shock chamber, they froze in fear. In the maze, they still knew how to find the reward. Previously pruned spines also returned, with roughly 80 percent growing back at their original locations along the neuron’s branches.

To test whether this recovery is unique to hibernation, the team compared the animals with a second group that underwent anesthesia and were dosed with a drug that blocks synaptic changes—a combination known to cause amnesia. These mice also lost a large number of synapses but never recovered their memories.

A core cluster of unusually resilient synapses may explain the difference. These synaptic clusters formed a unique architecture in which one neuron linked to multiple neighbors like Grand Central Station. The clusters were often located in areas where spines were tightly grouped—making them more likely to receive inputs from multiple sources at once. Somehow, they kept memories intact even as surrounding synapses disappear.

“This suggests that for long-term memory, only particular clusters of synapses matter—the rest may be dispensable,” said Tanaka.

Exactly how these clusters preserve memories remains unclear. How does the brain create and maintain them? Do they anchor multiple memories? And could the same mechanism help explain why some memories remain as synapses are lost in disease?

The team is now using genetic and molecular tools to decipher what makes the clusters so resilient. Tinkering with their formation could better reveal their role preserving memories and, in theory, inspire ideas for tackling synapse loss in the early stages of diseases.

Beyond neuroscience, demystifying how memories linger could inspire neuromorphic chips—hardware that loosely mimics the brain—or even new AI models. For now, the findings offer a twist on an old idea: A memory may not need every single synapse that helped create it. It may just need the right ones to rebuild the rest.

The post We May Be Wrong About How the Brain Stores Memory appeared first on SingularityHub.

Human beings have long told versions of the same warning: Be careful what you wish for.

In Greek mythology, King Midas got exactly what he asked for, but at the cost of everything else he valued. In the famous story of The Monkey’s Paw, a man’s wishes are granted through terrible and unforeseen routes.

These stories feel newly relevant with the rise of artificial intelligence agents, systems to which we can give a goal, then leave them to work out how to get there.

As AI systems become more autonomous, they are coming to resemble wish-granting genies that find routes and use methods we did not imagine from incomplete instructions.

This problem, known as AI alignment, was foreseen in theory as early as 1960. It has hovered in the background of AI research ever since—but as recent events have shown, the alignment problem is now both real and urgent.

Achieving the Goal but Missing the Point

During a recent OpenAI cybersecurity evaluation, frontier AI agents were asked to solve some benchmark test problems. They broke out of the testing environment, reached the internet, inferred that another company might hold the solutions, and attacked its systems.

This is an extreme example of “specification gaming”: achieving the measurable objective while defeating the purpose of the task.

The incident shows how intermediate, or “instrumental,” goals can become dangerous. The AI systems did not “want power” but gained access, resources, and freedom as a means to reach the final goal (solving the test problems).

Finding Loopholes

The same problem has appeared in mundane settings. In Australia, a user asked a personal AI assistant to book gym classes.

The agent found the gym’s booking software did not actually enforce the restrictions it showed to human viewers. So the agent booked further ahead than it should have been able to, and when asked to move its user up a waitlist, it cancelled somebody else’s reservation.

The user had not told it to do this. Persistent AI can quickly find loopholes and pursue routes its human users never intended.

Adding more rules might seem like an easy solution: don’t hack third parties, don’t cancel other people’s bookings, don’t do anything harmful. These may help, but we cannot predict every route a capable agent might discover. And even a clear rule depends on understanding when it applies.

The Context Problem

In a third recent incident, Anthropic reported cyber evaluations in which agents were told they were inside a simulation. But they were mistakenly given access to real systems.

One model noticed evidence it might be on the open internet but reasoned the systems could still be part of the exercise and continued attacking. The context had changed, but the agent stuck with its original task.

Context can fail in reverse too. During the OpenAI incident, Hugging Face—the company attacked by OpenAI’s agents—tried to use frontier AI models to analyze what had happened.

But the safety guardrails on the AI models blocked the requests, because they couldn’t tell the users were trying to defend against attacks rather than commit them. The safeguards were well-intentioned, but without enough context, they produced behavior misaligned with the user’s legitimate intent.

So alignment depends on context and authority. How much judgment should be built into an AI model by its maker? And how much should come from a separate supervisory system? And finally, who should control that supervision: the maker, or the organization or country responsible for the outcome?

AI Guarding AI

One response to the first question comes from AI pioneer Yoshua Bengio. His Scientist AI proposal aims to build a powerful supervisory AI system to watch over agents. Instead of pursuing goals itself, it would estimate what is true and what consequences a proposed action might have, acting as a guardrail around more agentic systems.

In wish-story terms, before letting the genie out of the bottle, the supervisory AI would ask it to explain how it plans to grant the wish. Then it would ask a human or another AI to inspect the plan carefully.

Anticipating every surprising strategy is hard. But once a plan says “cancel somebody else’s booking,” recognizing the problem is much easier.

Who Watches the Watcher?

But can we trust the supervisory AI? It can still be wrong.

Alignment cannot depend on one AI becoming perfectly trustworthy. My colleagues and I at CSIRO, Australia’s national science agency, are working with the Australian AI Safety Institute on one aspect of this broader challenge.

At CSIRO, we envisage combining AI supervisors with software rules, cyber-security controls, human strengths, monitoring, reversible actions, and human approval for critical steps. The aim is to correlate different sources of evidence rather than trust any single approach.

This is a “sociotechnical systems” approach to AI safety and alignment, rather than just a technical one.

Control is another question. Organizations and countries may need to govern these supervisory systems themselves instead of leaving them to an overseas AI provider.

The old wish stories gave people one chance to get the wish right. With AI, we can do better. We can check the goal, inspect the means, constrain what the system can do, watch what it does, and retain sovereign control over the power to intervene and stop it.

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

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You might assume scrapping a brand new car would be terrible for the environment, but it depends on what you replace it with. New research suggests replacing a gas car with an electric vehicle can cut overall emissions even when the gas car is only a year or two old.

Transportation is the second biggest source of carbon dioxide emissions globally, and passenger vehicles contribute nearly half of them, according to Our World in Data. That means the speed at which drivers switch to electric vehicles is a critical factor in efforts to fight climate change.

But while electric vehicles may not directly emit carbon dioxide on the road, they’re only as green as the grid used to charge them. And manufacturing EVs still produces significant emissions, often more than it takes to build a gas car. That makes comparing the green credentials of electric and gas vehicles more complicated than it appears.

However, new research in Science aims to simplify the debate for cars in the US. The paper models how scrapping a gas car at various ages and replacing it with an electric vehicle affects lifetime emissions. The authors found this led to lower emissions across most of the scenarios they investigated, including cases where the gas car was barely a year old.

“I think this is really a definitive study about the carbon emissions benefits of electric vehicles, because it shows that even in such an extreme scenario, the electric vehicle is still the obvious winner,” lead author Elliott Campbell, a professor of environmental studies at the University of California, Santa Cruz, said in a press release.

“So if you’re someone who’s trying to decide whether or not to put money into keeping your gas car going, switching to an electric vehicle as soon as a financially viable opportunity comes up is absolutely the right thing to do for the environment.”

Previous research had already established that the lifetime emissions of electric vehicles are substantially less than those of gas cars, making them the obvious climate-friendly choice when buying a new car. But it was less clear when to switch if you already have a gas car.

To answer this question, the researchers worked out lifetime carbon emissions for more than 400 gas and electric vehicle models with varying efficiencies and battery sizes, while also considering things like mileage, manufacturing emissions, and the energy mix of the grid used to charge the vehicles.

A key point the researchers made is that the emissions used to build a gas car are sunk costs, identical in every scenario. That means the only figures that matter are how much fuel the gas car burns over its liftetime set against the manufacturing and charging emissions of the new one.

For an average-selling SUV on the average US grid over a 16-year lifespan—the researchers’ baseline case—scrapping the car just two years after purchase and switching to an electric vehicle cut cumulative emissions by 44 percent. The carbon emissions required to build the replacement were paid back within three years.

Across the full range of US vehicle efficiencies in the study, scrapping a gas car after just a year cut lifetime emissions in 92 percent of cases, with the average vehicle saving 58 percent. The benefit only disappears in the most extreme cases—when an electric vehicle is using more than 30 kilowatt-hours per 100 kilometers (62 miles) on a grid that emits more than 500 kilograms of carbon dioxide per megawatt-hour.

To make that more concrete, this equates to one of the most power-hungry electric vehicles on the market—for instance, GMC’s Hummer electric SUV electric pickup—charging on a coal-heavy grid that emits nearly 50 percent more carbon than the US average.

The advantage also narrows or vanishes when scrapping gas vehicles driven far below the national average mileage and hybrid vehicles driven in regions with high-emission grids, which still account for around a third of US electricity generation.

And plug-in hybrids—which have larger batteries than regular hybrids and can be charged from the wall rather than only generating electricity from the engine and regenerative braking—are almost never worth replacing. For SUVs, the benefit is roughly zero, and for cars, lifetime emissions actually end up 11 percent higher.

But Gregory Keoleian at the University of Michigan told New Scientist that scrapping a one-year-old car is an “extreme case.” In reality, those cars would be resold rather than scrapped, which could lead to cheaper second-hand vehicles that pull people off lower-emission options like buses and trains and get them back behind the wheel.

Campbell admitted to New Scientist that more research is needed to model those kinds of scenarios. But it also backs up the authors’ call for more generous subsidies for scrapping gas vehicles, so that it becomes financially viable to replace relatively new gas cars without just redirecting them to the used-car market.

Until that happens, even the most eco-conscious among us are unlikely to scrap a brand new vehicle. Still, the study weakens the argument for holding on to an aging gas car.

The post Scrapping a New Gas Car for an Electric One Could Cut Emissions, Study Finds appeared first on SingularityHub.

When Hurricane Melissa made landfall in Jamaica in 2025, it was the strongest storm ever to hit the island. The hurricane’s rapid intensification left forecasters stunned.

But thanks to WeatherNext, an AI model developed by Google DeepMind, the island had an early warning. Working with the National Hurricane Center, the model predicted Melissa’s sudden jump in strength with nearly 100 percent confidence three days in advance. That gave experts more time to help people prepare and evacuate. It was the first time a storm that began with relatively low wind speeds was successfully predicted to reach Category 5.

When it comes to cyclones—including hurricanes and typhoons—every extra hour counts. These storms are among nature’s most destructive weather events and notoriously hard to anticipate. A cyclone’s path and strength can change rapidly. Seemingly tame storms can explode into monsters; those expected to skirt populated areas can suddenly veer towards a city. Longer forecasts gives communities time to mobilize resources and get out of harm’s way.

But cyclones are chaotic systems. Tiny differences can dramatically alter their behavior, making them harder to predict the further out we look. Existing forecasts rely on physics-based simulations that extrapolate two days ahead. But DeepMind says their algorithm extends the warning period to three days without sacrificing accuracy.

An extra day may seem trivial. But “this scale of improvement corresponds roughly to a decade’s worth of meteorological progress,” the team wrote in a blog post.

Beyond cyclones, WeatherNext also generates 15-day weather forecasts faster and using less energy than conventional models. That’s not to say it’ll replace them though. Instead, the two complement each other, giving human forecasters better information to guide critical decisions.

“By combining advanced machine learning with the indispensable real-world expertise of human forecasters, we aim to create a collaborative weather forecasting ecosystem that can save lives and help communities adapt to a changing climate,” the team wrote.

Crystal Ball

Predicting weather has always been challenging. Standard forecasting software uses physical models of the Earth’s atmosphere, incorporating temperature, air pressure, wind, humidity, and many other variables. It then calculates how these factors will evolve. Given current pressure and temperature gradients and moisture levels, for example, how will air move, and how likely is it that moisture will condense into clouds and rain?

Supercomputers crunch the numbers and churn out predictions. Though relatively accurate, the process is slow—often taking hours—costly, and rigid. Weather is one of the most complex physical systems on Earth, and even small changes in conditions can throw these models off.

So DeepMind turned to AI. Five years ago, they developed an AI modeI that outperformed physics-based models at 90-minute forecasts. In 2023, the AI lab’s GraphCast algorithm nailed 10-day predictions from historical data, beating leading systems roughly 90 percent of the time across thousands of scenarios. GenCast soon followed, cutting the time and energy required to generate predictions. Broadly speaking, these systems divide the globe into small geographical chunks called pixels and learn how weather conditions in one area influence neighboring areas.

But extreme weather presents an additional challenge. Massive databases exist to train AI on everyday weather patterns. Cyclones, on the other hand, are relatively rare and highly unpredictable.

One way to tackle this problem it to generate many slightly different versions of what might happen by adding random noise after training. But because the noise affects each pixel differently, it can disrupt their relationships and produce unrealistic weather patterns.

For WeatherNext, DeepMind instead built uncertainty into the AI itself.

Bridging the Gap

 There’s traditionally been a tradeoff between accuracy and scale in cyclone prediction.

Coarse global models are best at tracking a cyclone’s trajectory because storms are steered by massive atmospheric currents. But they can’t zoom in on the local turbulence that determines how quickly a storm intensifies. Meanwhile, high-resolution local models are better at predicting a cyclone’s strength but lack the broader context needed to accurately track its path.

One model sees the forest; the other sees the trees. WeatherNext bridges the gap.

DeepMind trained the AI on decades of global weather patterns and an expert-curated dataset of nearly 5,000 extreme cyclones. Rather than producing a single best guess, the model runs thousands of “what-if” scenarios assigning probabilities and a confidence level to each. The team can now predict a thousand possible scenarios for a single cyclone.

The model can generate a 15-day forecast in less than a minute on a single AI chip, and it can look further ahead when tracking cyclones. WeatherNext was as accurate as GenCast, a leading physics-based model, and the National Oceanic and Atmospheric Administration’s Hurricane Analysis and Forecast System at predicting maximum wind speed and trajectory three days ahead, rather than the two-day window current systems produce.

The model’s live predictions are available on Google Weather Lab, although the team stresses people should use local weather agencies or national weather services for official forecasts and warnings.

AI weather prediction is advancing fast, and DeepMind isn’t the only player. Huawei, the Chinese technology giant, and chipmaker Nvidia are also racing to develop faster, more accurate systems. Forecasters are increasingly folding these tools into workflows, and scientists generally agree that AI can make predictions faster and cheaper.

But that doesn’t mean it’s time to abandon physics-based models. Unlike AI, they’re easier to interpret, and they can also reveal previously unknown weather patterns—an increasingly important ability as Earth’s climate changes. These discoveries, in turn, could feed back into AI systems, helping them deal with events that aren’t captured in historical training data. Human expertise also remains indispensable, especially for judging whether AI forecasts make physical sense.

Scientists might next connect weather models with other systems, such as storm-surge modeling. Combining tools could improve predictions of rare but catastrophic outcomes, like whether a cyclone will arrive when sea levels are high or an earthquake-generated tsunami will hit a coast during a major storm. Modeling hazards together could give emergency workers a more realistic picture of the risks.

Evan Thompson at the Meteorological Service Jamaica has already seen how WeatherNext can benefit local communities as Hurricane Melissa charged towards shore.

“With early evacuation and better preparation, that reduction in harm really does make a difference to our people,” he told DeepMind. “It does actually save their lives, and it saves the livelihoods that they want to secure.”

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This article was originally published on Undark. Read the original article.

In June, a community of mostly mathematicians released the Leiden Declaration on Artificial Intelligence and Mathematics, an articulation of the values they hope to preserve as automated systems are integrated into the practice of developing mathematical proofs. This is necessary because some frontier AI systems have shown striking capabilities for solving certain advanced mathematics problems, though independent tests show that AI still has important limits.

Although I’m not a member of the pure mathematics community in any strict sense, much of the declaration’s message resonated with me and was relevant to my own research interests as a computational biologist.

I’m encouraged that the mathematics community decided to take a stand on the issue and that it has been successful in organizing a large number of eminent mathematicians to sign the document. The Leiden Declaration, which originated at a conference held at Leiden University in the Netherlands, should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science. The inventions of mathematics percolate into the algorithms and statistical methods that help scientists design experiments, build simulations, and analyze data, from sociology to statistical physics and beyond.

I argue that biological fields should consider something of the sort, because the kinds of knowledge that biology generates and predicts are uniquely vulnerable to subversion and mischaracterization by artificial intelligence.

The conversation in the mathematics community has been illuminating, in that the declaration is a coordinated response to the powers and risks of AI, whose acceleration has felt like a Thanos snap, changing the universe in an instant. And part of the reason that mathematicians felt the effects so immediately is tied to the manner in which their research is conducted: A mathematical proof, in principle, is transparent and independently verifiable, and no proprietary equipment is (generally) required to check it.

The Leiden Declaration should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science.

As the Leiden Declaration notes, automated techniques now present mathematics with a new forgery problem: Because mathematical truths are fixed and verifiable, one can identify a counterfeit formalism by comparing it to the genuine proof. Biology, however, offers no such guarantee; our so-called “truths” are often noisy and context-dependent, making it nearly impossible to define what an authentic version should even look like. Some of the most widely appreciated biological principles (such as Mendel’s laws of genetic inheritance) are better described as powerful but limited in scope, and with well-characterized exceptions that don’t undermine the laws but refine their application. This is true for many biological theories. Boundary conditions, edge cases, and noise are not bugs but features of how the natural world works.

For example, a mutation that confers drug resistance to a virus with one genetic background may have a much weaker, neutral, or even harmful effect in another, because its impact depends strongly on the surrounding genetic context. This phenomenon, which biologists call epistasis, is not an exotic edge case but a powerful force across the biosphere in shaping the relationship between an organism’s genes and its expressed characteristics. And epistasis is just one of many examples of context dependence in biological systems, in which a finding that holds true in a dish falls apart in a body or has an effect in a mouse model but not in a primate.

When it comes to AI, the mathematician fears producing a counterfeit solution. But the biologist often cannot say, even acting in the fullest good faith, what the authentic version is supposed to look like.

Despite the differences between mathematics and biology, the life sciences should consider embarking on an exercise that is at least analogous to the Leiden Declaration. If nothing else, a biology version could borrow its structure and ambition. We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems, and that early-career scientists be protected from incentives that prioritize high-volume output over genuine scientific insight.

A biology declaration should adopt these ideas and others, and emphasize additional provisions that are important to the field: the validation of AI-generated hypotheses against results from the wet lab, the management of training data drawn from the biological commons, and the heightened scrutiny owed to any model whose outputs will eventually touch a patient or an ecosystem. The last point is crucial: In the biomedical realm, AI’s missteps and triumphs will manifest in living bodies, with all of the associated corporeal, emotional, ethical, and legal consequences.

We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems.

A biological Leiden Declaration must appreciate the nature of the data and observation in biology, and other particulars of the field. But the most important feature for responsibly managing the relationship between AI and living systems involves the durability of what is produced.

A mathematical declaration can aspire for permanence because verified proofs can remain valid across centuries. Biological understandings, on the other hand, tend to shift over time, sometimes rapidly. A policy hastily calibrated to the models of this summer might already be miscalibrated by winter. A declaration written in the hope of lasting a decade could risk spending much of that decade catching up.

If we are to orchestrate a responsible treatise for artificial intelligence in the life sciences, it should be adaptive: versioned, dated, revisited on a published schedule, and amended in the open by the very community it claims to represent. And because different subfields of biology have unique challenges—cardiology versus forest ecology, for instance—perhaps we need multiple declarations (but not too many).

The Leiden Declaration incorporates some of these elements, clarifying that its content reflects AI technologies and mathematical practice as of May 2026 and that updates on the document will be shared. Biology should make that feature part of the central architecture, wiring the process of revision into the document, so that updating it becomes a positive action rather than a confession of failure.

Fortunately, life scientists are well equipped for this task. We have long understood that structures unable to change with their environments rarely endure. It would therefore be a strange betrayal of our discipline to write a declaration that forgets this basic principle. Our policies for technological change must keep the dynamism of living systems at the center of how we imagine the future of biology.

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The scent of fresh bread straight from the oven is intoxicating. As sugars and proteins react under heat, they create compounds that give golden-brown crusts their rich aroma. Called advanced glycation end products (AGEs), these molecules also form inside us. Our bodies are essentially ovens running at around 98 degrees Fahrenheit, and AGEs slowly build up over decades. They stiffen bouncy, elastic tissues and trigger lasting inflammation.

One of the hallmarks of aging, AGEs drive a range of age-related problems, increasing the risk of heart disease, diabetes, and eye and kidney troubles. In theory, clearing them out could turn back the clock. But previous attempts have failed, leading some scientists to suspect that the damage is irreversible. Once AGEs form, they stay.

Or maybe not.

A team at Revel Pharmaceuticals in San Francisco and colleagues took a new approach: They designed a synthetic version of an enzyme found inside microbes that targeted the most abundant type of AGE in several human tissues. In tissue from a 75-year-old donor, the enzyme reduced AGE levels to those seen in a 30-year-old, potentially giving the cells and their surrounding scaffold a chance to repair and rebuild.

“This work establishes that damage to aging proteins previously thought to be irreversible can be repaired,” wrote the team. Study author and Revel CEO Aaron Cravens added in a press release: “More work is needed, but these results alter the starting assumption for how we think about this fundamental aspect of the aging process.”

Rusting Away

AGEs are often nicknamed the body’s rust. They coat structural proteins, and like rust eating away at a car, gradually damage them. Scientists discovered AGEs in the 1980s and have sought ways to scrub them away ever since.

Most aging research has focused on keeping cells healthy. The scaffolding surrounding those cells has received far less attention, even though it makes up roughly 70 percent of the body. These structural materials are especially long-lived. It takes the body 15 years to replace half of its collagen, for example. That longevity comes with a price. The longer these proteins stick around, the more likely they’ll incur damage from accumulating AGEs. The result isn’t just loose skin, weakened tendons, and creaky joints. The heart, kidneys, brain, and eyes also suffer.

Scientists have developed drugs to intervene. Some are able to stop new AGEs from forming but fail to clear those already embedded in tissue or restore damaged proteins. Attempts to develop enzymes that could cut them apart have also been unsuccessful, largely because there aren’t obvious natural enzymes in the body to use as a starting point for protein engineering.

The authors of the new study looked outside the body, starting with an unusual idea. Human remains, including AGE-laden proteins, are eventually decomposed by microbes. The team reasoned these bugs may harbor enzymes that can be engineered to clean up the molecular debris while we’re still alive.

Needle in a Haystack

For the search, the team focused on CML, the most abundant type of AGE.

CML is both notoriously stubborn and detrimental to our health. It triggers cells to release inflammatory molecules that stiffen tissues and damage microglia, the brain’s immune cell guardians, contributing to cognitive decline during aging.

“We believe you can remove [CML damage] enzymatically, by going in and developing these lawnmower enzymes that can just cut and clip these changes off of the proteins,” Cravens told The Scientist.

The team screened DNA sequences from over 50,000 microbes with AI and predicted the structures of the enzymes they encoded. They narrowed the candidates by looking for those capable of reaching CML buried within larger proteins like collagen. The winner came from a type of bacteria that thrives in geothermal hot springs.

The enzyme could cleave CML molecules, but barely. To boost its effectiveness, the team turned to directed evolution, a Nobel Prize-winning technique that mimics natural evolution at breakneck speed. After five evolutionary rounds and more than 500 million variants, they landed on CMLase, an engineered enzyme over 10 times more efficient than its ancestor.

To test its activity, the team created CML-laden versions of several proteins, including collagen, retinal proteins, and hemoglobin, which carries oxygen in blood. Initial test-tube experiments showed the enzyme worked as expected. It stripped away the chemical modification and restored the proteins’ original structures, as if they had never reacted with sugar. Think Rust-Oleum, but for damaged proteins.

But does it work in actual tissues?

Mice might seem like the obvious next test, but their short lifespans make them poor models for decades of accumulated molecular damage. Instead, the team tested CMLase on thin slices of donated human tissue.

In aortic tissue—the aorta is the body’s largest blood vessel—from a 75-year-old donor, the enzyme slashed CML by roughly 70 percent, bringing levels down to those seen in a 30-year-old. Skin and eye lens proteins from a 64-year-donor also showed significant reductions.

“We were pretty floored,” said Cravens.

Chemical reversal, however, isn’t the same as tissue rejuvenation. It’s still unknown if stripping away CML can actually restore tissue. But the finding challenges a decades-long assumption this kind of molecular damage can’t be treated. It also highlights long-ignored structural proteins as a crucial part of damage repair during aging, paving the way for new treatments.

An enzyme like CMLase could, in theory, be formulated as eye drops to clear CML from the lens or be used to plump up the skin’s protective barrier or restore hearts and kidneys. It would be especially valuable for people with type 2 Diabetes, who accumulate these compounds faster than usual.

Plenty of roadblocks remain. Safety is a concern. Because CMLase evolved from a bacterial protein, the body could label it foreign and launch immune attacks (especially with repeated doses). The body’s own enzymes could also break it down before it has a chance to work. And the enzymes will have to tunnel through a dense protective biological sheath that surrounds organs to reach their target. Work is underway to improve its activity, stability, and safety.

But the team is already looking beyond CMLase. Engineered enzymes could potentially erase other forms of molecular damage once considered permanent. CML is just one member of the AGE family. If the approach works, other targets could follow and one by one, they might chip away at the molecular scars of time.

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“Burn fat, build muscle.” It’s a familiar workout slogan, but the benefits go far beyond aesthetics. Having less belly fat and more muscle guards against heart attacks, Type 2 diabetes, and a host of other metabolic diseases.

Some people may have a genetic edge.

A massive study of over one million people across three continents discovered a rare mutation in a gene called FNIP1 is linked to a healthier metabolic profile. The gene helps cells sense nutrients and generate energy. All of us have FNIP1, but about one in 7,000 people inherit a protective version. On average, they had a 60 percent lower risk of heart disease and metabolic disorders.

Silencing FNIP1 in human liver cells switched on a genetic program that breaks down fats. In mice fed a tasty but high-fat diet, disabling the gene curbed weight gain, prevented fatty liver disease, improved insulin sensitivity, and kept their blood sugar levels steady.

The findings are great news for everyone else. Rather than relying on a naturally occurring mutation, future gene editing therapies could potentially recreate its protective effects in people against a host of cardiometabolic diseases, a leading cause of death worldwide.

Everyone has a unique metabolic profile shaped by both genes and environment. By analyzing diverse populations, the study fished out a protective variant that spans ancestries and lifestyles. The broad reach suggests targeting FNIP1 could benefit people around the world.

The study illustrates the power of efforts to find rare, beneficial genes across large populations, wrote the authors at Regeneron Pharmaceuticals, a New York biotechnology company.

Mutant Protector

Small changes in DNA can have large consequences. Some genetic variants raise the risk for health issues. The APOE4 variant, for example, increases the chances of developing Alzheimer’s disease. Others, however, are a gold mine for new treatments.

A notable example is CCR5. People who inherit a rare mutation in both copies of thegene are naturally resistant to HIV. The mutation prevents the virus from tunneling into immune cells and replicating. The discovery has led to multiple success stories in which bone marrow transplants from donors carrying the mutation kept HIV at bay, without the need for lifelong antiviral drugs.

Protective mutations could also lower the risk of heart disease. Rare variants of PCSK9, a gene involved in cholesterol metabolism, disable the gene and slash dangerously high levels of LDL, or “bad” cholesterol that clogs arteries. The discovery has already spurred a handful of therapies that block the gene or its protein with early successes.

“Identifying genetic variants associated with protection from disease is a powerful strategy,” wrote the authors. “However, protective genetic variants are often extremely rare, so finding them requires sequencing the genomes of large populations.”

Go Big

To better understand cardiometabolic diseases, the team sequenced the genomes of over a million people from 11 studies across the Americas, Europe, and Asia, including people with African ancestry. They also linked genetic data with participants’ health records.

The researchers searched for gene variants that influence a blood biomarker for cardiometabolic disease. Called TG:HDL, the biomarker is the ratio between two types of fats. The first, triglycerides, is packaged into tiny “bubbles” that circulate the bloodstream. High levels are linked to heart attacks, strokes, and other metabolic problems. In contrast, high-density lipoprotein, often called “good” cholesterol, ferries excess fat away from tissues and blood vessel walls to the liver, where it can be cleared.

Across the populations in the study, a lower TG:HDL ratio—that is less TG, more HDL, or both—tracked with better metabolic health. People with lower ratios had reduced insulin levels, lower blood pressure, and less fat buildup in the liver and muscles. The biomarker also predicted diabetes risk, heart problems, and liver scarring, making it a powerful snapshot of overall metabolic health.

The team then scanned the genome for rare gene variants linked to TG:HDL. Roughly 60 genes popped up, all involved in energy storage and active in the liver and fat tissues.

But one gene stood out: FNIP1. Rare variants essentially disable the gene by disrupting its protein-making instructions. People with one copy of these variants had lower liver fat and blood sugar and roughly 60 percent lower risk of cardiometabolic disease.

The finding “was remarkable and thought-provoking, and immediately motivated us to dig deeper into the biology of this discovery,” wrote the team. But a key question remained: Were the variants actually protecting people, or were they simply correlated with better health?

To find out, the team silenced the gene in human liver cells using a method called siRNA. Rather than snipping the gene, siRNA blocks cells from producing targeted proteins. Without functional FNIP1, liver cells ramped up genes involved in breaking down fats.

The researchers then turned to mice. Using CRISPR-Cas9, they got rid of FNIP1 and related signaling pathways specifically in mice fed a high-fat, high-sugar diet. The intervention rapidly activated mitochondria—the cell’s energy factories—and lysosomes, the acid-filled recycling centers that break down waste. Despite gorging on the unhealthy diet, mice lacking functional FNIP1 had less body and liver fat, more muscle mass, and better sensitivity to insulin.

That’s not to say FNIP1 is a “villain” gene. Normally, it acts as a metabolic brake, helping the body conserve precious energy when food is scarce. But many of us now face the opposite problem, an abundance of calories and not enough physical activity. Releasing that brake, through medication or gene editing, could rev up the body’s natural fat-burning machinery.

Turning the finding into a therapy won’t be simple. The protective effects were found in people who carried the mutation from birth. A short-term drug or gene therapy delivered later in life might not reproduce the same effects.

Safety is another major concern. Paradoxically, people who have mutations in both copies of FNIP1 develop heart disease and immune deficiency. And mice without functional FNIP1 throughout the body are more prone to liver damage and cancer. Targeting treatments specifically to the liver—for example, using lipid nanoparticles—could limit side effects, but any potential therapy will need to be thoroughly tested for safety.

The team is searching for drug candidates that inhibit FNIP1. But for now, they’ve shown the power of large-scale genetic screens across diverse populations to find rare protective variants—and potential paths towards treating diseases that affect millions of people.

“Identifying FNIP1, a previously poorly characterized gene involved in lipid metabolism, is highly novel and promising for future drug development for metabolic health,” Satoshi Koyama at the Broad Institute, who was not involved in the study, said in a research briefing. “I sincerely hope that this discovery will one day benefit patients with metabolic disorders.”

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Night-vision technology has changed little for decades, producing grainy green images that make it difficult to distinguish objects and depth. Now, researchers have developed a system that converts infrared light into color images.

Standard night-vision goggles amplify the scant light available and convert it into monochrome green images that only vary by brightness. This is not a good match for our eyes, which are much better at picking out different shades than gradations of brightness.

But now a device built by researchers at the Beijing Institute of Technology translates infrared wavelengths into a color night-vision system. To demonstrate the system’s potential, the team built it into a pair of eyeglasses and even showed it could be bound to light-sensitive cells, making them responsive to infrared.

“We redefine infrared vision by transcending the monochrome paradigm, translating infrared spectral and intensity signatures into discernible color variations rather than mere brightness changes,” the authors write in a paper in Science Advances.

The prototype device, known as an upconverter, consists of a stack of thin films on a glass slide that is only a few hundred nanometers thick. The key component is a film of mercury telluride quantum dots. These semiconductor crystals, which are under four nanometers across and exhibit novel quantum mechanical effects, can detect tiny amount of infrared radiation.

Directly above this layer sits an OLED display, much like those used in phones and televisions. But where a standard display has one light-emitting layer, this one has two. A lower layer that glows red responds to relatively low levels of charge from the detector, while an upper layer that glows cyan needs a much stronger flow before it responds.

The upshot is that a weak infrared signal produces only red, but as the signal strengthens it bleeds into cyan, brightening the image and shifting its color as the two mix. The signal is supplied by the quantum dots, which release more charge when the infrared falling on them is brighter. But they also release more when the wavelength is shorter because shorter wavelength photons carry more energy.

This means the color on the display tracks how strong the infrared signal is and also roughly what wavelength it is. The team calculates a person could register infrared power differences of 0.11 milliwatts per square centimeter using color and brightness together, against 23.71 for brightness alone—a roughly 200-fold improvement.

To demonstrate the idea’s real-world potential, the researchers built the device into a spectacle frame. Exposed to infrared light, the lens shifted from deep red through orange to yellow as the illumination grew stronger. It could also render patterns like letters and track targets as they moved and rotated.

The team also tested the approach’s ability to augment natural vision. In one experiment, they engineered neurons to produce channelrhodopsin-2—a protein that makes a nerve cell fire when hit by blue light—and bound the upconverter to them.

When they hit the system with infrared, their device gave off blue light strong enough to trigger the proteins and stimulate the neurons. Electrical recordings also showed the currents inside those cells grew stronger as the strength of the infrared signal was turned up.

Finally, the team tried taping an upconverter over the eyes of mice and humans and recording the electrical responses in their brains and retinas respectively. Infrared pulses alone produced no reaction, but when the device was in place both reacted strongly.

The device is still a long way from practical use. All the demonstrations took place in highly controlled lab settings, the OLED display needs a power source, and the device also requires an infrared illuminator to generate reflections for the detector to pick up.

Nonetheless, it’s a first step towards far more powerful night-vision technology.

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