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2026-08-11 22:00:00

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

Some people may have a genetic edge.

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

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

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

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

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

Mutant Protector

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

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

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

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

Go Big

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

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

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

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

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

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

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

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

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

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

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

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

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

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2026-08-11 06:36:58

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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2026-08-08 22:00:00

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2026-08-07 22:00:00

“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.

2026-08-07 04:48:17

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.

2026-08-05 04:10:46

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.”

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