2026-08-26 04:35:27
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.”
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?
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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2026-08-25 06:52:36
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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2026-08-22 02:25:19
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.
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.
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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2026-08-20 22:56:20
AI is like a genie. The way in which algorithms grant our wishes may make us regret letting them out of the bottle.
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.
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).
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.
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?
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.
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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2026-08-20 01:12:23
The authors found most of the scenarios they investigated resulted in lower emissions, including cases where the gas car was barely a year old.
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.
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2026-08-18 06:42:15
For communities in the crosshairs, every extra hour counts.
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.
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.
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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