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.

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.”

The post DeepMind’s Weather AI Predicts Hurricanes a Day Earlier Than Traditional Forecasting appeared first on SingularityHub.

Artificial Intelligence

These Startups Are Chasing the Next Big Thing in LLMsWill Douglas Heaven | MIT Technology Review ($)

“Transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws. A growing number of scientists and engineers are now asking what’s coming next.”

The post This Week’s Awesome Tech Stories From Around the Web (Through August 15) appeared first on SingularityHub.

In the Leiden Declaration, mathematicians issued a treatise on how AI challenges their field. Others must do the same.

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.

The post Biology Needs an AI Declaration appeared first on SingularityHub.

Sugar damage in the body was thought to be irreversible. But the new enzyme made 75-year-old tissue look chemically like a 30-year-old’s.

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.

The post Designer Enzyme Strips Decades of ‘Rust’ From Aging Human Tissue appeared first on SingularityHub.

The discovery could lead to treatments and demonstrates the power of efforts to unearth rare, beneficial genes in large populations.

“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.”

The post Million-Person Study Finds a Rare Gene Variant That Slashes the Risk of Diabetes and Heart Disease appeared first on SingularityHub.

The system combines quantum dots and an OLED display, translating infrared light into a range of visible colors.

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.

The post Monochrome No More: New Night-Vision Glasses Show Color appeared first on SingularityHub.

Future

Should AI Labs Be Treated Like the Owners of Dangerous Animals?Staff | The Economist ($)

“Gabe Weil of the Institute for Law and AI, in Massachusetts, proposes a system of strict liability. As with rules around keeping wild animals, it would assume that any harm is always the fault of the party carrying out the risky activity.”

The post This Week’s Awesome Tech Stories From Around the Web (Through August 8) appeared first on SingularityHub.

Today’s AI is neither able to improve itself recursively nor is it intelligent like us. Between prompts it remains a static mathematical object.

“We are now, like, in the singularity.”

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world.”

Days earlier, OpenAI had disclosed that two of its artificial intelligence models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is the singularity? And is Altman right that we are in it?

What Is the AI Singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI Cannot Make Itself Smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights—or “parameters”—is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous amounts of energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents—systems that run an LLM in a loop to work through complex tasks step by step—do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding, and the prompt, and nothing happens inside of it.

A Ladder That Doesn’t Exist

The second problem with the singularity story is the word “surpass.” It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep, and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimized to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging superintelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping Our Feet on the Ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education—so we start worrying about the right things.

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

The post Sam Altman Says We’re ‘in the Singularity’ With AI. Here’s Why He’s Wrong. appeared first on SingularityHub.

Researchers will hunt for antibodies in the blood of people who lived past 100, drank heavily, or smoked—but avoided cancer.

Jeanne Calment was over 122 years old when she passed away. The oldest person in history, she smoked for nearly a century, but never developed cancer.

Why does cancer grow, spread, and become deadly in some people but not others? Even twins, who share similar genes and lifestyles can differ widely in cancer risk. Many factors likely contribute, but a bold new study, called ATLAS, is investigating an unexpected player: autoantibodies.

These immune-system proteins roam our bodies, but instead of attacking pathogens, they mistakenly target healthy cells and tissues. They’re best known for their role in autoimmune diseases, but early evidence suggests they also fine-tune the immune system’s response to cancer. Some appear to weaken immune surveillance, allowing tumors to sprout and flourish. Others may boost anti-cancer immunity by tagging cancer cells for destruction.

Whether they’re friend or foe is far from clear. ATLAS researchers aim to find out by analyzing blood samples from diverse groups of people, including centenarians and people who have escaped cancer despite carrying high-risk gene variants or exposure to risk factors like smoking.

The project hopes to discover why some people are naturally resistant to cancer, which could lead to early diagnostic tests, new therapeutic targets, and more effective treatments. ATLAS may “uncover fundamental principles” of antibody immunity in cancer, wrote the team.

Immune Mayhem

Since the late 19th century, scientists have suspected the immune system helps keep cancer in check. The idea has since spawned powerful treatments. In CAR T cell therapy, for example, a patient’s own immune T cells are genetically enhanced to better recognize and destroy tumors to cure previously untreatable blood cancers. A similar strategy in macrophages, immune cells that tunnel into tumors and literally engulf them, is now entering early clinical trials.

Far less attention has been given to antibodies. These proteins normally fight pathogens, like viruses. But sometimes they go rogue, taking the form of autoantibodies that attack healthy proteins, DNA, and other molecules. Even healthy people carry a diverse collection of autoantibodies, but most bind only weakly and don’t seem to trigger biological effects.

For decades, these proteins were used mainly to diagnose autoimmune diseases such as rheumatoid arthritis, as they often appear years before symptoms emerge. But more recently, scientists have begun uncovering their broader impact on the immune system. Autoantibodies that attack cytokines, a type of immune signaling molecule, were implicated in roughly 20 percent of Covid-19 deaths, largely because they disabled antiviral defense.

Scientists have since linked them to worse outcomes in several other life-threatening viral diseases, increasing some people’s vulnerability as if they were immunocompromised. Beyond infections, they also neutralize cytokines that protect against inflammatory bowel disease.

Cytokines orchestrate many immune system activities, including inflammation, allergies, autoimmunity—and cancer. Although there’s still little direct evidence that autoantibodies themselves drive or prevent tumors, scientists have found many can recognize cancer-related proteins and are developing methods to detect them as an early sign of cancer.

If autoantibodies can reshape cytokine activity during viral infections, could they also determine who develops, or resists, cancer?

“These discoveries establish that autoantibodies can function as powerful, naturally occurring immune modifiers raising the possibility that similar antibodies may alter antitumor immunity,” wrote the ATLAS team.

Charting the Landscape

Because antibodies linger long after diseases have gone, they preserve a molecular record of a person’s immune history. Rather than focusing on a handful of candidates, ATLAS is going fishing: The study will chart the body’s entire antibody repertoire, including autoantibodies, seeking signatures linked to cancer susceptibility or resistance.

The team will first scan blood samples for autoantibodies. They’ll also catalog conventional antibodies, making note of the ones that directly recognize and attack cancers. All this data will go into a comprehensive cancer antibody atlas, giving researchers a resource to explore how different antibodies shape cancer.

To start, the team will study what they call “remarkable groups of people” whose immune systems may hold unusual clues. Among them are healthy centenarians. Although cancer risk usually skyrockets with age as DNA mutations accumulate, these individuals have somehow avoided the disease. Others have remained cancer-free despite smoking, heavy drinking, or carrying cancer-related gene variants such as the BRCA mutations for breast cancer. The team will also study pairs of identical twins where only one sibling developed cancer, allowing them to compare antibody signatures in people with nearly identical genetic blueprints.

Finally, the team plans to track people with cancer before, during, and after immunotherapy, to paint a picture of how immune responses evolve over the course of the treatment.

Ultimately, they expect to find three broad classes of antibodies: those that help or hinder cancers and those that appear largely neutral. Each could prove valuable.

Autoantibodies that blunt anti-cancer immunity could become drug targets. Scientists might make synthetic “decoy” antibodies to block them—in a way, fighting fire with fire. The findings could also inspire next-generation immunotherapies.

On the other hand, autoantibodies that help the immune system recognize cancers could become therapies themselves or complement existing therapies, such as checkpoint inhibitors, which boost the body’s immune response to cancer. These are much less toxic than chemotherapy, but only 20 percent of patients respond, perhaps because of immune differences.

Even seemingly neutral autoantibodies may be useful cancer biomarkers. Because antibody tests are already well-established, fast, and inexpensive, associated neutral antibodies could aid early detection, monitor whether treatments are working, or warn when a cancer is likely to return.

But correlation isn’t causation.

Some antibodies may merely record a person’s immune history rather than actively influencing cancer. To tease the two apart, the team plans to test promising candidates in cultured human cells and mice, to see whether they alter cancer growth or spread. Those experiments could reveal previously hidden molecular communications between the immune system and cancer and deepen our understanding of the deadly disease.

“We should be able to come up with a biomarker to predict who is likely to avoid cancer, [and] who is likely to develop cancer,” said ATLAS team member, Xin Lu at the University of Oxford. “Potentially we could come up with therapeutic, preventative agents [that are] antibody-based. And that would be fantastic.”

The post Why Do Some People Never Get Cancer? The Answer May Be in Their Blood appeared first on SingularityHub.

Sophia Space and Caltech want to fold the bulky parts of a space-based data center—solar cells and radiators—into all-in-one tiles with chips.

Every time you ask ChatGPT a question, computer chips in a massive data center whirl into action. In the blink of an eye, they ping back answers. Behind the scenes, though, AI data centers consume enormous amounts of electricity, heat, and water.

The AI boom is impacting communities. After welcoming 37 data centers, residents in Virginia’s Henrico County were hit with skyrocketing electrical bills. Schools and government buildings were asked to turn off lights, shut down computers, and avoid using space heaters to ease strain on the power grid and keep costs down.

Henrico isn’t alone. A growing backlash is prompting many states to consider legislation curbing new facilities. “No data center” signs have sprouted on lawns and alongside roads. Yet as AI demand continues to surge, so does the need for more computing power.

This has top AI companies looking skyward. Instead of routing requests to terrestrial data centers, future queries could be handled by thousands of solar-powered satellites orbiting above. The results would then be beamed back, with users none the wiser.

But there’s a major hurdle: heat.

Space’s frigid vacuum may seem like the perfect place to cool chips, but it’s not that simple. Lacking air and water to carry heat away, orbital data centers would have to use thermal radiation. Here, heat is converted into infrared energy and radiated into space, often requiring bulky hardware that adds weight, cost, and complexity.

With these challenges in mind, California Institute of Technology and Sophia Space, a California startup developing orbital computing, recently unveiled a patent for a chip cooling system designed to radiate heat into deep space. Called Sophia TILE, thousands of these chips could be linked to form large orbital data centers or organized into smaller, distributed clusters.

Powered by abundant sunlight, the chips could operate continuously without eating up Earth’s resources. The team hopes to test their vision by 2030.

“This patent reflects a different way of thinking about computer infrastructure in space,” said Leon Alkalai, founder and chief technology officer at Sophia Space, in a press release. “Instead of beaming down energy to Earth from orbit, we decided to consider putting computing in space and beam[ing] down data.”

The project joins a growing international push towards orbital computing. ADA Space, working with Zhejiang Lab, has already launched satellites for its Three-Body Computing Constellation and plans to expand into a much larger network. Meanwhile, US companies including SpaceX, Starcloud, and Blue Origin are seeking regulatory approval for constellations that could eventually grow to include up to a million AI-capable satellites.

Without doubt, the race is on.

Space Cadet

Orbital data centers would consist of high-performance computer chips housed in protective enclosures designed to withstand the harsh conditions of space. In orbit, they would collect uninterrupted solar power. In contrast, solar panels on Earth require batteries to store energy for use after sunset.

Solar power in space is hardly new. The International Space Station, satellites, and other spacecraft have long relied on solar panels. More recently, engineers have developed flexible, lightweight designs such as NASA’s Roll-Out Solar Arrays, which launch tightly rolled and unfurl in orbit.

AI, however, demands far more power. One long-standing idea for harvesting continuous solar power suggests we collect solar energy in space and beam it down to Earth. But that approach doesn’t completely appease the growing ire against data centers. They’d still consume energy on the ground and take up land and other resources. A newer idea flips the question. Rather than delivering energy to computers, why not bring computers nearer to the energy source?

The argument in favor of sending data centers skyward is growing stronger. A recent Gallup poll found roughly 70 percent of Americans oppose data centers in their backyard, while experts agree that meeting AI’s future energy demands on Earth alone will become increasingly unsustainable.

But while power is abundant in space, heat is the main problem. Without air or water to carry heat away, computers in space must rely on thermal radiation. That means adding large, heavy radiators to an already bulky, solar-powered setup. In space, weight is money, and scaling orbital data centers will take a lot of it (to put it mildly).

Hot and Cold

TILE tackles the cooling problem with a specialized material that converts heat into infrared radiation. The concept may seem alien, but everything warmer than absolute zero cools this way. Our bodies, stovetops, and car engines all shed heat as invisible infrared light.

Each TILE combines solar cells, thermal insulation, processors, memory, and optical communication hardware into a single module. Beneath the electronics sits a custom heat-spreading layer that prevents dangerous hot spots. Like placing a scorching pan onto a baking sheet, it distributes heat over a much larger surface before channeling it to the radiator.

The modules are designed to work together. Thousands of TILES could link into a giant computing mosaic, each acting as a mini computer connected to its neighbors. Like a modern power grid, the distributed architecture improves reliability—if one TILE fails, others can jump in—while simplifying power distribution and thermal management.

The modular design also solves a practical challenge: Rockets don’t have much cargo space. Similar to NASA’s Roll-Out Solar Arrays, a TILE-based data center could launch in a compact configuration before unfolding into a large, flat computing platform in orbit.

Looking further ahead, the team envisions launching multiple interconnected arrays in succession, like strings of pearls. Each could function as an independent data center that exchanges data with others, effectively extending cloud computing into orbit.

Sophia Space is targeting a demonstration mission in late 2027. By 2030, the team estimates an array of 2,000 TILEs could deliver up to a megawatt of dedicated computing power. To put that in perspective, a single ground-based data center can deliver hundreds of megawatts of computing power, and future data centers will stretch that number into the thousands.

There are challenges beyond the purely technical. Earth orbit is crowded with active spacecraft and debris, raising the risk of collisions. SpaceX’s Starlink satellites, for example, perform frequent collision-avoidance maneuvers after a close call in 2019. The breakup of a Chinese Long March rocket in 2024 threatened an estimated 1,000 satellites. Large constellations of data centers—SpaceX has plans for up to a million in low Earth orbit—would add even more traffic.

Beyond collisions, astronomers are worried that expanding satellite numbers could hinder our ability to study the universe by interfering with telescope observations and radio astronomy.

For now, orbital data centers are unlikely to replace their terrestrial counterparts. Instead, they’re more likely to complement them, processing data collected by spacecraft and beaming only the results back to Earth. Although the field is ridden with hype and controversy, there’s also promise and momentum is clearly building.

“It’s just kind of exploding,” Sergio Pellegrino, a Caltech engineer who collaborates with Sophia Space, told The New York Times. “We need to become more comfortable with space doing things for us.”

The post Heat Is an Orbital Data Center’s Greatest Foe. These Tiles Dump It at the Source. appeared first on SingularityHub.