Computing

Why Everyone in Quantum Computing Is Buzzing About Google’s Move Into ‘Neutral Atoms’Adam Bluestein | Fast Company

“Google’s interest sends a clear signal: Neutral atom quantum computing has arrived. And while it may not replace superconducting as the leading approach, the technology is primed to be a key player in the race to quantum utility, the threshold where the value of quantum computers finally eclipses the cost of running them.”

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A new system allows agents to orchestrate complex experimental processes and extends Anthropic’s reach into the physical world.

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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As AI eats traffic, the best sites are locking it out, making reliable information harder to find.

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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Delivered by injection, the drug transforms astrocytes into neurons. In an early study, mice modeling Alzheimer’s showed marked improvement compared to untreated peers.

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.

The post This Drug Makes New Neurons in the Brain. Scientists Say It Reversed Alzheimer’s Symptoms in Mice. appeared first on SingularityHub.

Artificial Intelligence

OpenAI Is Developing a ‘Persistent’ AI AgentMaxwell Zeff | Wired ($)

“In recently aired podcasts, interviews, and private investor meetings, OpenAI CEO Sam Altman has described his desire to turn ChatGPT into a proactive, always-on AI agent. …’There’s like a single product which is: I need to ask the AI something,’ Altman said on a recent episode of David Senra’s podcast. ‘Eventually, maybe the AI should proactively offer me things.'”

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Recursive self-improvement, where AI continuously builds better versions of itself, might be harder than some hope.

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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Can AI replace lawyers—at least in some circumstances?

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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These lab-grown balls of brain tissue could help researchers study a host of disorders that emerge as the brain ages.

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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The flashy company, which recently completed a blockbuster IPO, appears to be leading the pack of humanoid robot makers.

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.

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In a new study, mice recovered their memories by regrowing brain connections lost during artificial hibernation.

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.

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