Transhumanismus

By chilling roundworms, scientists traced a molecular pathway hinting at one way cold might extend lifespan.

From ice baths and cold showers to whole-body cryotherapy, cold exposure has long been hyped as a wellness shortcut. The supposed benefits include boosted dopamine, weight loss, stronger immunity, or even slower aging.

Scientific evidence for these claims is scant, but a telltale link between temperature and longevity has intrigued scientists for over a century. Lowering ambient temperature by just a smidge can extend lifespan in the roundworm C. elegans, fruit flies, and even mice. Plenty of mechanisms could explain the curious effect, but scientists are still largely in the dark.

A new study may shed some light on the matter. When scientists grew roundworms at moderately cold temperatures, the worms steadily ramped up production of BH2, a helper agent for many enzymes, including those that build brain chemicals. Though initially produced in our muscles, BH2 roams to other parts of the body too, including the intestines and brain. Here, in the study, it boosted tyrosine production and contributed to a longer lifespan.

Mapping a longevity pathway in worms isn’t just about satisfying scientific curiosity. When the scientists supplemented human cells with BH2 or tyrosine, the treatments fended off toxic protein clumps associated with neurodegenerative diseases.

But don’t go ordering tyrosine just yet. A biological response in cells doesn’t automatically translate into a health benefit, especially one as complex as slowing aging. While worms are a useful model in longevity studies, they’re a far cry from humans. That said, an existing drug used to treat a metabolic condition can tweak the pathway, and examining clinical data from patients taking the drug could offer a useful test to see if the mechanism holds up in people.

More broadly, the findings point to muscles as active players in the aging process. Beyond movement, their chemical output could change how other tissues age. The findings “highlight the muscle’s ‘metabolome’—its full set of metabolites—as a potential source of signals that promote longevity,” wrote Hanseul Lee and Seung-Jae Lee at the Korea Advanced Institute of Science and Technology, who were not involved in the study.

Cold Case

As time ticks by, basic cell functions gradually falter. DNA’s protective end caps, called telomers, fray. Mutations accumulate in our genetic blueprint. Chronic inflammation flares across the body. Senescent “zombie” cells bathe healthy tissues in a toxic chemical brew. Cellular energy factories and waste disposal plants begin to sputter.

Dubbed the hallmarks of aging, these processes only partly depend on genetics. Environment, social factors, and lifestyle—diet, exercise, exposure to toxins—also shape our aging trajectory.

Temperature seems like an odd addition to that list. Yet nature is littered with examples. Hibernating mammals generally live longer than those who don’t. Yellow-bellied marmots, for example, spend up to seven months a year hibernating, lowering their body temperature and throttling metabolism. Scientists have found their biological aging essentially stalls during hibernation.

These observations inspired the rate-of-living theory, which suggests that the lower a creature’s metabolic rate, the longer it lives. Recent work has given the century-old idea a nuanced update by exploring how cooler temperatures directly activate mechanisms that counter some of the hallmarks of aging. But there are caveats.

Take mitochondria, our cells’ energy factories. With age, they become increasingly dysfunctional. A chill activates brown fat, which is packed with mitochondria, ramping up energy use and potentially overall metabolic health. In a small study of a dozen people exposed to mild cold for up to eight hours, most showed signs of better blood sugar regulation. But these were short-term effects, not evidence for lasting protection. Cold can also boost mitochondrial numbers, though whether they protect against aging in humans remains to be seen.

The immune system may also respond to a chill. A study in healthy people and those with inflammatory disease, found cold exposure slashed immune molecules called cytokines, which often fuel inflammation. A different study found lower temperatures slowed the accumulation of reactive oxygen species (little “bullets” that damage cellular components, including DNA).

A cellular waste removal plant, the proteasome, also seems to benefit from air conditioning. In 2023, David Vilchez at the University of Cologne and colleagues found that moderately cold temperatures boosted proteasome activity in both C. elegans and human cells. The newly invigorated cleanup crew chomped proteins known to aggregate into clumps involved in neurodegenerative diseases.

“Lowering body temperature is one of the most effective mechanisms to extend longevity,” the team wrote at the time. But translating those ideas into a practical strategy isn’t straightforward—continuously maintaining a low body temperature is a hard sell. Mimicking those effects in a pill, however, might be easier to swallow.

A Cool Way to Live Long

In the new study, Vilchez’s team compared metabolites in C. elegans grown at a comfortable 20 degrees Celsius (68 degrees Fahrenheit) with those in worms kept at temperatures five degrees lower. On average, the chilled worms lived roughly 15 percent longer.

The levels of one metabolite, BH2, skyrocketed in the cold. The molecule helps a host of enzymes do their jobs, and genetically blocking its production wiped out the extra longevity.

The team found BH2 was mostly produced in muscles and transported to other organs. In the intestines and brain, it shifted into another form that pushed cells to make more tyrosine, an amino acid often found in meats, fish, diary, and soy. Even without a cold snap, feeding tyrosine to worms unable to make BH2 helped them live longer.

“We were genuinely surprised to find the supplementation significantly extended lifespan, even at standard and warmer temperature,” said Vilchez in a press release.

The team then tested BH2 in human neurons. Mutant proteins related to Huntington’s disease, amyotrophic lateral sclerosis, and dementia often trigger cell death, but cells treated with BH2 largely escaped this fate. Meanwhile, they better maintained protein homeostasis, the delicate balance between making, folding, and clearing proteins that goes awry with age.

These findings suggest a way to mimic the benefits of cold without having to tolerate the shivers or dangerous health effects.  But a designer pill is a long way off. Safety is one concern. In mammals, a balance between BH2 and its active version regulates reactive oxygen species. Supplementation may tip that balance, causing cells to pump out more “bullets” and ironically accelerate aging. And because the pathway controls other enzymes, unexpected side effects could do more harm than good.

For now, the takeaway isn’t to stock up on BH2 or tyrosine as a fountain of youth. Rather, the results are a good starting point for the development of metabolite-based treatments for neurodegenerative disorders. And instead of going after therapies that target the aging body as a whole, the results suggest focusing on one tissue could be enough.

“These findings suggest that specifically targeting a single tissue, such as muscle, could provide organism-wide protection. This opens up new avenues for therapeutic approaches,” said Vilchez.

The post Study Could Help Explain Why Colder Body Temperature Extends Lifespan in Many Species appeared first on SingularityHub.

It’s widely believed AI’s inner workings are largely inscrutable. A former OpenAI researcher now says this narrative could be wrong.

The idea that the neural networks underpinning modern AI are “black boxes” almost impossible to understand has become received wisdom. But a provocative blog post from a researcher who played a key role in setting that narrative says these models may be far more interpretable than we thought.

The opacity of today’s leading AI models is becoming an increasingly pressing problem as the technology seeps into ever more areas of our lives. Recent incidents where agents from leading developers escaped their sandboxes to go on hacking sprees have only boosted the sense of urgency.

This has spurred interest in a research field known as mechanistic interpretability, which attempts to reverse engineer neural networks to learn what individual neurons and the circuits they create do. By detailing how these networks operate, researchers hope it’ll be easier to control and direct them.

Progress has been slow, however, and leading theories have suggested the way these networks learn leads to a tangled mess that is incredibly difficult to parse. Now though, Nick Cammarata, a researcher at AI startup Goodfire who helped lay the foundations of the field while at OpenAI, has published a 26,000-word blog post arguing that these theories are wrong and even the largest neural networks could be much simpler to read than expected.

“I think I have good news about the nature of neural networks,” Cammarata writes in the post. “Interpretability might be far more tractable, possibly in the near term, than we’ve assumed.”

The idea Cammarata is challenging is one he helped establish. In 2020, while studying a Google image-recognition model called InceptionV1, he and colleagues found neurons that seemed to respond to unrelated things—most famously one that fired on both cat faces and the fronts of cars.

The leading explanation was the concept of superposition, which suggested that when a network runs short of neurons, it starts lumping unrelated concepts together into the same neurons. And it specifically combines concepts that are so disconnected they’re unlikely to clash. This means that looking at an individual neuron tells you very little, because there’s no rhyme or reason behind what ideas it groups together.

But when Cammarata revisited the famous cat-car neuron, he realized both images shared a prominent V shape. Further tests showed the same neuron also responded to the corner of a pool table and the pointed nose of a Japanese bullet train.

Cammarata then went looking for similar explanations across the rest of InceptionV1 with the help of a specialized suite of tools and AI agents to help speed up the search. He concluded that unrelated concepts packed into neurons were usually more connected than they first appeared. This is often because a neuron starts out tracking one thing before gradually branching out into neighboring ideas as the network learns—a phenomenon he calls recursive generalization.

Cammarata has since applied the same approaches to modern language models. Preliminary results from the Qwen3-32B model suggest roughly 90 percent of its neurons track one main concept, potentially alongside a few closely related offshoots. Cammarata says he found similar results while analyzing the much larger, trillion-parameter Kimi K3 model, which he plans to detail in a forthcoming technical paper.

The blog post sparked huge debate in the AI community, including some rapturous praise from leading researchers at large AI companies. But not everyone is convinced. For a start, the blog post self-identifies as a working hypothesis rather than a rigorous empirical study. “I appreciate that he looked at thousands of examples, but even a large number here is not a substitute for rigorous evaluation,” Chris Potts at Stanford University told MIT Technology Review.

And others question how far understanding individual neurons can take you. In a series of posts on X, Jacob Hilton at the Alignment Research Center, pointed out that to truly understand what a neural network is doing you need to track and explain how they connect neurons into larger circuits.

Even Cammarata says he is surprised by the response the blog has received and admits some people have taken his findings too far. “It’s not infinitely good news; it’s not everything solved,” he told Tech Review. “There’s a bunch of open questions around, like how much the combinations of neurons matter versus just one at a time.”

Nonetheless, the work has reopened a central question in AI that many researchers considered settled. And while Cammarata’s forthcoming technical paper should give the field a clearer sense of how justified his optimism really is, there is now a sliver of hope that the intelligent machines we are building may be slightly less inscrutable than initially feared.

The post Is AI Really a Black Box? Neural Networks May Be Easier to Understand Than We Thought appeared first on SingularityHub.

ARTIFICIAL INTELLIGENCE

Hugging Face’s New AI Biology Model Lets You Chat With DNAEmily Mullin | Wired ($)

“The AI platform Hugging Face on Thursday released a new open model for understanding DNA that can run locally on a laptop. Developed in collaboration with researchers at Zhongguancun Academy in China and the University of Naples Federico II in Italy, the model is trained on genomic sequences and natural language so that people can ask questions about the effects of DNA changes on gene activity and health and get conversational answers in return.”

BIOTECHNOLOGY

A First for CRISPR: Powerful Prime Editing Alters DNA Directly in the Human BodyHeidi Ledford | Nature

“The milestone treatment is part of a clinical trial of prime editing, a technique that can alter DNA with more precision than standard CRISPR–Cas9 methods can. In this case, prime editing was used to treat a condition called Wilson’s disease by rewriting DNA in liver cells, to erase a mutation that prevents them from eliminating excess copper.”

FUTURE

Welcome to the Valley of PainMatteo Wong | The Atlantic ($)

“This week could go down as the worst in the history of cybersecurity. …Yet what’s most alarming about the past few days is that by next year, this stretch of incidents—which some security experts are calling ‘Black October’—may be remembered as normal, or even tame. ‘It’s going to feel like, over and over and over again, we’re finding more and more bugs in very critical pieces of software,’ Matthew Prince, the CEO of the internet infrastructure and security company Cloudflare, told me.

Biotechnology

Moderna and Merck Say Their mRNA Cancer Vaccine Cut Death, Recurrence by 42%Beth Mole | Ars Technica

“The results are a significant advance in melanoma treatment; Merck and Moderna are already engaging with regulatory authorities to get the vaccine approved. More broadly, the vaccine’s success bolsters hope that mRNA vaccines and individualized cancer-targeting medicines offer a new paradigm for cancer therapies.”

ENERGY

Startup Taps Underwater Volcano as Energy Source in ‘Proof Point’ TestJeremy Hsu | Ars Technica

“The world’s first demonstration of a subsea geothermal power plant has taken place near an active underwater volcano almost a mile below the ocean surface. It generated less than 10 seconds of net-positive electricity but showed the technical feasibility of tapping another form of renewable energy underwater.”

FUTURE

Is a Job Guarantee the Solution to Mass AI Unemployment?Nick Romeo | The Guardian

“In its most ambitious form, such a program would ensure that anyone who wants work is guaranteed a meaningful, decently paid job. By creating a public option for employment, its supporters argue, a job guarantee could protect millions of people from the financial and psychological damage of joblessness and mobilize a vast labor force to tackle urgent societal problems, from infrastructure and climate adaptation to elder care and childcare.”

Tech

This Touchscreen Is Made From Thread, and Scientists Want to Put It in Your ClothesVarun Mirchandani | Digital Trends

“Forget smartwatches. Scientists have developed a touchscreen that can be woven directly into clothing, letting users interact with digital content without carrying a rigid display. Researchers at the National University of Singapore (NUS) have integrated 54 specialized fibers into a sleeve to create what they describe as the world’s first touchscreen built into a garment.”

Biotechnology

Cool, Cool, Cool, Palmer Luckey and Ben Lamm Made a Genetically Modified Military Dog StartupKate Knibbs | Wired ($)

“A new startup launched today with the definitely not terrifying mission of creating genetically enhanced dogs for the United States military, law enforcement, and emergency first responders. Warhound is the brainchild of Anduril’s Palmer Luckey and Colossal Biosciences cofounder Ben Lamm. The startup has raised $200 million to ‘rebuild America’s domestic supply of elite working dogs’ and reduce dependency on European dog breeders for military canines.”

Artificial Intelligence

‘Reverse-Engineer Anything’ Is the New Wild West of AIJason Koebler | 404 Media

“What we’re seeing is vibe coding, but instead of making something from scratch, people are using AI to take an existing piece of software and replicate its features, its functionality, or cloning it wholesale. In some cases, people are using AI to take a proprietary piece of hardware and install some version of open source software on it. All of this is some mix of ‘decompiling,’ ‘reverse-engineering,’ ‘hacking,’ vibe coding, AI slop, and stealing.”

Space

Japan Launches Mission to Bring Home a Piece of the Martian Moon PhobosStephen Clark | Ars Technica

“The mission to the Martian moons builds on Japan’s success in leading the Hayabusa and Hayabusa 2 missions, the world’s first two spacecraft to bring back pristine specimens from asteroids. Scientists believe Phobos could itself be an asteroid that was captured into orbit around Mars billions of years ago. One of MMX’s science goals is finding out if that theory is correct.”

Future

How a Legal Backlash Against Big Tech Is Forcing Big ChangesCecilia Kang | The New York Times ($)

“For decades, tech giants grew at breakneck speeds as Congress struggled to keep pace with the consequences of the companies’ rapidly booming businesses. But a new de facto regulatory regime has emerged over the past year as courts clobber the biggest internet companies. And for the first time, their product designs and advertising models—which critics say treat users as products—are costing the companies serious money.”

The post This Week’s Awesome Tech Stories From Around the Web (Through October 24) appeared first on SingularityHub.

There is no big red button or glass box hiding a lever.

In the wake of high-profile hacking incidents involving advanced artificial intelligence agents, some politicians and business executives have been calling for an AI kill switch: a safeguard that could shut down these AI systems in an emergency.

The idea is that, if a model were to behave in dangerous ways, a kill switch would allow humans to regain control over that system by metaphorically flipping a switch from “on” to “off.”

Several efforts at AI regulation in 2026 have focused on the notion of a kill switch. These include proposed congressional legislation such as the AI Kill Switch Act, and California Gov. Gavin Newsom’s executive order aimed at reining in AI. President Donald Trump has opposed the idea of an AI kill switch as unnecessary regulation, and to date there has been no national regulation that includes a kill switch.

Experts such as Dylan Baker, a research engineer at the nonprofit Distributed AI Research Institute, have explained that one regulatory problem with the metaphor of the kill switch is that today’s advanced AI models involve a complex infrastructure of distributed technologies, which is beyond the control of a single kill switch.

Additionally, the metaphor of the kill switch also obscures the human decisions involved in the responsibility and oversight of an out-of-control model. The problem is that the metaphor does not account for the technological or human aspects of AI.

Black Boxes and Kill Switches

The metaphor of the kill switch is not necessarily new. In fact, it is rooted in the same rhetoric as the black box metaphor, which similarly blurs the complex aspects of AI by framing inner workings as confined and unintelligible. Referring to AI as a black box frames these systems in terms of restricted incomprehensibility: a monolithic stranger to human control and understanding.

At the University of Cambridge, digital humanities researcher Eryk Salvaggio argues that “the black box of AI cultivates useful confusion about what can and cannot be known” about the complexity of frontier models. The idea of the kill switch cultivates a similar confusion by obscuring the distributed infrastructure and human influence that are important aspects of AI. While these metaphors have opposite meanings—resisting control and regaining control—they invoke the same implicit argument about the nuts and bolts of AI.

Both the black box and kill switch are what cognitive linguist George Lakoff and philosopher Mark Johnson call structural metaphors. They rely on simple physical concepts—for example, object, substance, up-down, and in-out. Structural metaphors use one clearly delineated concept to help make another concept clearer. For example, black box uses the concept of an opaque container to give a clear structure to the concept of hidden or inscrutable inner workings.

Additionally, Lakoff and Johnson say that this type of metaphor can also hide some aspects of a concept by focusing attention on others. For example, the kill switch metaphor hides the complexity of advanced AI models by focusing on the fact that a model can be on or off.

What this means is that framing the operation of AI in terms of a black box and kill switch relies on the straightforward ideas of “box” and “switch” to conceal the complex operations of AI. Reining in AI becomes difficult on a policy level when discussions about AI regulation fail to account for the infrastructural and human aspects of advanced models.

Centralization and ‘Rogue’ AI

There are two problems with the structural metaphor of an AI kill switch, both demonstrating that it misses the mark for a national safeguard. The first is that a kill switch makes the global infrastructure supporting AI appear centralized, when in reality it is a web of interconnected technologies spread across the globe.

The advanced models that companies like OpenAI and Anthropic are concerned about releasing rely on multiple computers and data centers that are spread out nationally. It is not as simple as pulling a plug, opening a box, or flipping a switch. The kill switch metaphor reduces the infrastructural entanglements of AI to outdated ideas about technology. For instance, in proposing his since-failed “AI Emergency Button Act” bill, Sen. John Kennedy compared the concept to kill switches in machines such as jet skis.

The second problem is that the kill switch idea keeps hidden the human decision-making involved in the training and deployment of frontier models. Rather than serving as a reminder that people are involved, the kill switch positions the human outside of the loop and ready to flip the switch during a catastrophic emergency. Human intervention is invoked as an afterthought, instead of a process that involves training, supervision, and approval.

For instance, accounts of how OpenAI’s model went “rogue” and hacked AI company Hugging Face depict the AI agents as though they operated on their own agenda. But experts have explained that not only did OpenAI personnel turn off safety mechanisms, but they also gave the model impossible tasks and left a door open to the internet. The role people played counters the “rogue” agent framing.

The kill switch metaphor is ill-suited to the challenge of regulating AI because there is no way to switch off an AI model without accounting for its distributed infrastructure and human influence. There is no big red button, emergency brake, or glass box hiding a lever. Effective national regulation of AI will not begin with any single switch or any one person’s finger.

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

The post Why Calling for a ‘Kill Switch’ Is the Wrong Way to Think About Reining in AI appeared first on SingularityHub.

A trio of papers showcases AI-designed molecular scissors and an upgraded tool that builds proteins atom by atom.

The protein universe is about to get bigger.

Scientists can now use AI to build proteins never before seen in nature. These designer proteins may become therapeutics, environmental sensors, or even the building blocks of synthetic life.

Despite enormous potential, however, AI tools still struggle to design complex proteins, like enzymes, from scratch. Unlike small proteins or peptide binders that disrupt cellular signaling, enzymes are shape shifters with a catalytic core. Getting the right shape is only half of the battle. The components must also quickly move, align, and interact at just the right moment.

But effective AI-designed enzymes may be here soon. This month, Nobel laureate David Baker’s lab at the University of Washington unveiled two types of AI-designed proteases. Proteases are like molecular scissors that cut other proteins at specific sites. The enzymes are notoriously tough to engineer, but designer versions could be made into powerful medications.

In test tubes, Baker’s synthetic scissors chopped up proteins associated with Alzheimer’s and ALS, a common motor disease. In cells, they acted like switches for inflammation-related proteins, activating them only after a precise snip and, in turn, changing how the cells behaved.

The findings open the door to destroying disease-related proteins long considered undruggable. “Proteases are just the beginning,” said study author Anqi Chen in a press release.

There’s another reason for optimism too.

In a separate paper, the Baker lab introduced RFdiffusion3, the lab’s latest generative AI model for protein design. Rather than mainly working at the level of amino acids, the tool zooms in on individual atoms, giving scientists finer control over their synthetic creations.

“The field is maturing from predicting and accurately building protein shapes and interactions to designing machinery that physically changes matter at the atomic level, in targeted and programmable ways,” said Baker in the press release.

Tailor-Made

Proteins are finicky things. Strings of molecules called amino acids twist into helices and sheets, then fold into intricate 3D shapes.

Not all amino acids are built the same. Some are relatively sleek; others sport sprawling branches called side chains. These branches influence how proteins fold and interact with their surroundings, affecting how drugs rev up or dampen their activity or change DNA and RNA behavior. Add in a protein’s ability to change shape and engineering one to behave predicably seems like an impossible dream.

With generative AI, the dream is becoming reality. Algorithms are expanding the protein universe, allowing scientists to engineer smaller or wholly new gene-editing tools, for example, or tiny antibodies that fight viruses or serve as antidotes to snake venom.

Enzymes, however, are tougher to crack. Many enzymes orchestrate an intricate dance between metals, target molecules, and other ingredients—such as water—to make a reaction happen. Any hiccups can torpedo the whole process.

Take proteases. These molecular scissors sever the chemical connections linking amino acids. Their core does the cutting, while other parts orient them so they dock to the target or otherwise control activity.

In two new studies, the Baker lab team designed two proteases. One, a zinc protease, uses a metal to drive the reaction. The other, a cysteine protease, forms a vulnerable chemical intermediate with the target, making the bond easier to break. Both strategies depend on getting specific amino acids into precise positions.

The team took inspiration from existing enzymes found in a crayfish’s digestive system and in yeast. With RFdiffusion2, their in-house design tool, the researchers built new protein backbones around each enzyme’s catalytic core to help it dock to the target. They used AI tools to select the best amino acids and built the protein around those structural constraints.

After repeating the process dozens of times, the team turned to AlphaFold3 to predict the resulting structures. Most looked nothing like known proteins, suggesting the AI wasn’t simply borrowing nature’s existing blueprints.

Thirteen of the synthetic cysteine proteases worked in a test tube test, a success rate of nineteen percent. But all were less active than the original yeast enzyme. The zinc proteases were even trickier. The team had to tweak amino acids near the catalytic core to accommodate another molecular player in the reaction. They found 36 percent of the 135 candidates could precisely cut their targets. One of these targets was amyloid-beta, a protein that forms toxic clumps in Alzheimer’s disease. The best designs made the reaction roughly 100 million times faster than when the protein target naturally broke down.

Designer proteases have a leg up compared to other protein-based medicines. Antibodies, for example, require a high dose because they only latch onto one target at a time. But with proteases, “you just need a small amount because each scissor molecule can cut many, many target molecules,” Baker said in a video provided to journalists.

But the overall low efficacy of the enzymes the team designed exposed an uncomfortable truth: Current AI tools still struggle to create more complex molecular machines.

A Magnifying Glass

To build better proteins, the lab developed RFdiffusion3, a protein design tool that zooms into the atomic world. Protein modeling algorithms, like AlphaFold3, already operate at this level, and some design tools can tweak atoms around catalytic cores. But most still focus on shuffling amino acids.

RFdiffusion3 uses atoms as the basic unit when generating the protein backbone and side chains, better accounting for the precise interactions that make enzymes work.

That’s not to say the model fully maps every atom. Each amino acid is represented by 14 atom slots—four for the backbone and 10 for side chains—that capture its structure and dynamics. Think of dots on a motion-capture suit. The dots don’t track every muscle twitch, but they do provide enough information to reconstruct movement.

This extra detail benefits enzyme design. Scientists can specify atomic positions around the catalytic core to better control the enzyme’s shape and chemical properties. The resulting blueprint more precisely maps out which amino acids to use and where to put them.

The team tested the tool on two types of newly designed enzymes in test tubes. One broke down paraoxon, an environmental pollutant. Using quantum chemistry data to model how the enzyme changes shape, the AI produced four active candidates on the first try, which the researchers further improved for speed and precision.

They also tested a zinc protease designed to cut a protein that, when misfolded and clumped, leads to kidney, heart, and nerve disease. Of 96 designs, 56 cleaved more than 10 percent of their target, a low but promising hit rate for enzymes designed from scratch.

Proteins don’t work in isolation. RFdiffusion3 can also design them in the context of other molecules—DNA, RNA, and drugs—accounting for the atomic details of their interactions. This could help researchers build final structures better tailored to their molecular partners.

As with all AI-designed proteins, each candidate will have to be vetted in cells. But the team is hopeful the upgraded tool could unlock more creative possibilities. “We anticipate RFD3 [RFdiffusion3] will be broadly useful for a wide range of biomolecule design challenges and applications,” they wrote.

The post This AI Model Designs Enzymes With Atomic Precision appeared first on SingularityHub.

The treatment delivers a light-sensitive algae protein to the retina. Paired with special goggles, it can restore limited vision and lasts for years.

The patients sit perfectly still as a needle inches toward one of their eyes. After the injection, they’ll no longer be completely human. The syringe is filled with a gene therapy that installs a light-sensitive algae protein into failing retinas and could reboot some lost vision.

When scientists first proposed using algae proteins to restore human eyesight, the idea seemed like science fiction. Could alien proteins integrate into our intricate visual system? Even if they did, could the brain translate the resulting signals into sight, rather than nonsensical bursts of light?

A new study suggests yes, with caveats. The team treated people with retinitis pigmentosa, a genetic disease that gradually destroys light-sensing cells in the retina. Seven of ten patients showed improved light sensitivity. After patients donned goggles that deliver pulses of light to activate the algae proteins, the team tested their eyesight. Roughly half could reach for and touch objects or identify the orientation of a black bar, a common test of visual recovery.

The treatment is still far from restoring full vision. What patients saw was too blurry and the field of view too narrow to discern texture or fine detail. The boundaries of objects looked squiggly. But those who responded to the treatment continued to improve over roughly five years. One participant, whose story made headlines in 2021, can now recognize and count different objects, and white stripes on a crosswalk pop out.

The study, called PIONEER, is an ambitious clinical trial combining gene therapy with optogenetics. The first goal was to assess safety, and it met the mark. Most participants experienced a brief bout of inflammation in the eye receiving the injection. The only patient to suffer a severe side effect was quickly treated with medication.

The findings suggest the initial success wasn’t a fluke. The study “represents an important milestone for optogenetic vision restoration,” said study author Stefan Futterknecht at the Institute of Molecular and Clinical Ophthalmology Basel, in a press release.

Setting Sights High

Around two decades ago, Karl Deisseroth was hunting for new ways to control brain cells. Electrical bursts and chemicals, which are how neurons communicate, seemed like obvious choices. But nature had something stranger to offer: algae.

Algae produce a protein called channelrhodopsin. When activated by light, the protein changes neuron activity using a mechanism that works similarly in mammalian cells. In a foundational paper, Deisseroth’s team inserted the protein into rodent neurons, and voilá—the neurons could now be controlled using light.

The technology, called optogenetics, revolutionized neuroscience.

By genetically tagging specific neurons with channelrhodopsin, scientists can now dial the cells’ activity up or down like a dimmer switch. Optogenetic experiments have revealed brain circuits that shape feelings, memory, and cognition with unprecedented precision.

Researchers panning for more genes in different algae strains struck gold again. One channelrhodopsin variant they found, ChrimsonR, was particularly exciting. Unlike the classic version that’s activated by blue light, ChrimsonR responds to amber light, which penetrates tissues better and is less damaging. More recently, scientists have begun to use generative AI to redesign natural proteins to make them even more sensitive to light.

“Optogenetics provides opportunities for mapping the brain in a way that we could once only dream of,” said Per Svenningsson, Chair of the Nobel Committee for Physiology or Medicine, when the technology won the coveted prize this year.

Still, it has mainly been considered a research tool.

Light It Up

Retinitis pigmentosa gradually robs people of their eyesight. Researchers have linked over 100 genetic variations to the condition, which kills off light-sensitive cells in the retina.

“The replacement (or editing) of a single gene would not help most persons,” wrote Deisseroth in the New England Journal of Medicine. Instead, adding channelrhodopsin could offer a more universal solution by rewiring surviving neurons.

Early successes bolstered the idea. In one study, scientists equipped cones—a type of retinal light-sensing cell—with channelrhodopsin in mice with retinitis pigmentosa. The therapy boosted their light sensitivity. It also reactivated cones that had stopped responding to light in human retina samples.

The new study took a different approach. In advanced disease, cones may have already degenerated beyond the point at which they could be saved. So instead, the gene therapy targeted ganglion cells. These cells process signals from light-sensitive cells and pass that information to the brain, but don’t detect light themselves. Ganglion cells are, however, more resilient than cones. Add channelrhodopsin, and they could become substitute light sensors.

The team packed genetic instructions for ChrimsonR into a harmless viral delivery system and injected it into the poorer seeing eye of 10 patients aged 40 to 73. All were legally blind, and each received one of three escalating doses.

As the patients gradually built up levels of the algae protein in their eyes, the team faced another challenge: Translating visual scenes from the outside world into amber light that the engineered ganglion cells could recognize.

They developed specialized goggles that look a bit like Apple’s Vision Pro. The goggles use a camera to scan the field of view thousands of times per second, monitoring for light changes in every pixel. When the device detects a change, it pulses amber light from that pixel into the eye. Because our eyes naturally dart around—often without us noticing—this dynamic method may better mimic normal vision.

With the goggles on, 6 of 10 participants showed clinically meaningful improvement in light sensitivity, with more than a fourfold boost over baseline readings. One participant improved over 62-fold. Of the eight who agreed to further eyesight tests, roughly half could touch a notebook, grab a small staple box, and detect changes in a rotating bar.

Of those who initially responded to the treatment, improvements lasted for years. Their brains also seemed to adapt to the alien light sensors. In one test, participants were asked to open or close their eyes while looking at a tumbler. Electrical brain signals related to vision were more coherent with eyes open, suggesting their perception depended on improved visual input.

Any gene therapy carries risk. In an already worsening eye, inflammation and other side effects could cause further damage. Overall, the injection appeared relatively safe. Most participants experienced a temporary increase in eye pressure, but no detectable side effects throughout the body. The retina’s overall structure remained unchanged. One person suffered a blockage in a major eye artery that cleared within minutes after treatment with an FDA-approved eye drop.

For now, the participants still can’t read or see faces. The therapy reaches only a fraction of their ganglion cells, making sharper vision harder to restore. More advanced versions of channelrhodopsin with greater light sensitivity may increase resolution.

As for the hardware, the team is working on versions of the goggles that deliver more accurate and timely stimulation. Potential upgrades include an eye tracker, software to preprocess visual scenes, and even holographs.

“Overall, although optogenetic methods have been developed primarily for basic science discovery,” wrote Deisseroth, they’re now heading towards “diverse therapeutic approaches.”

The post Blind Patients Recover Partial Vision in Gene Therapy Trial appeared first on SingularityHub.

Using a system called BootLoops, scientists produced 36 papers in three months. Paired with experts, the tool could pollinate ideas across fields.

Some major scientific advances involve taking a technique developed in one field and applying it to another, but interdisciplinary research is tricky and time-consuming. Now a new AI research system has automated the process, generating 36 papers in three months.

Most researchers work within a narrow specialty, and few have time to comb through unrelated fields for tools that might help them. As a result, researchers can end up banging their heads against a problem scientists in another discipline have already solved.

But Harvard physicist Matthew Schwartz stumbled across a potential solution while working as a visiting researcher at AI firm Anthropic. After getting the company’s AI, Claude, to develop a suite of tools for mathematical physics, Schwartz discovered they could help solve quantitative problems in a host of other areas.

Called BootLoops, the tools, which are model agnostic, help AI search scientific literature, write code, and solve mathematical problems. Working with 19 coauthors, Schwartz used BootLoops to produce 36 manuscripts on topics ranging from ecology and linguistics to health insurance and sunspots.

“Once Claude had BootLoops, it kept noticing the same pattern: many fields have problems that a technique from mathematics, physics, or computer science would solve outright if anyone knew it existed,” Schwartz wrote in an Anthropic blog post about the system.

Schwartz first started using Claude as a general-purpose research assistant last December, and while he was able to do some useful work with it, he says it required constant handholding. So he decided to instead point it at problems that were a good match for the model’s strengths—what he calls “Claude-shaped problems.”

He started with the equations physicists use to interpret particle collisions at the Large Hadron Collider. The best methods were scattered across software in several programming languages or existed only on paper, so he got Claude to combine them into a single framework. Using the tools it had built itself, the model reproduced the results of one of his papers in about 20 minutes.

Schwartz then moved onto a harder class of these equations, and within a few weeks, the model had solved 30 of them, including 15 that had never been calculated before. It didn’t take long for Schwartz to realize that many of the methods the AI had developed could be used in neighboring areas of physics such as cosmology and string theory.

But it was Claude itself that made the bigger leap, pointing out that the same kinds of equations turn up in population genetics and evolutionary biology. The problem for Schwartz, however, was that he found it harder to tell when Claude was overselling its findings in fields he was less familiar with. So he brought in experts from each field to judge which of Claude’s findings were worth pursuing, and from some 400 candidate problems, 36 ended up as papers.

One project examined the star ratings used to grade private health plans under Medicare, which determine the bonuses insurers receive. The rules say the boundaries between ratings levels should be drawn so each plan’s score sits as close as possible to the average of its group, but the algorithm Medicare actually uses is a shortcut that doesn’t guarantee this.

Using a statistical method dating back to 1958 to calculate the exact answer, BootLoops found drawing precise boundaries would push 34 plan ratings across the bonus line, 16 up and 18 down. This would shift an estimated $0.9 to $1.4 billion in bonus payments between 2025 and 2027.

Another project tackled a 150-year-old puzzle about what kills sunspots. Because the sun’s rotation hides sunspots from view for much of their lives, astronomers have never been able to agree on how long they last or why they disappear.

Borrowing a tool doctors use to measure how the risk of dying changes with age and applying it to every sunspot logged since 1874, the team found that sunspots age. At a given size, their risk of “dying” rises about 20 percent a day, something none of the published theories could account for.

For each new problem, Claude built new tools, so the BootLoops toolkit kept growing, and it now spans 22 fields, according to the project’s website. Other problems it’s tackled include how Earth’s atmosphere first filled with oxygen, how quickly tree species come and go in a Panamanian rainforest, and how natural selection weeds out harmful mutations in humans.

The kinds of cross-disciplinary leaps BootLoops can make are extremely rare, Georgia Channing, artificial intelligence for science lead at Hugging Face, told Science. “A physics researcher wouldn’t spend six months reading linguistics research in order to figure out how to repurpose linguistics methods,” Channing said.

That’s not to say the system isn’t without flaws. Schwartz says many of Claude’s early findings were technically correct but scientifically unremarkable until experts steered them, and he warns that the model “loves to declare victory” too early.

But the results suggest AI may be a powerful way to pollinate ideas across research fields, even if we still need humans to decide which ones are really worth pursuing.

The post New AI Tool Could Help Scientists Make ‘Rare’ Cross-Disciplinary Leaps appeared first on SingularityHub.

Scientists want to create cells from scratch, to unveil life’s origins or provide specialized functions. Are they close?

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

On a lonely planet, billions of years ago, a soup of chemicals—in the right place, at the right time, and with the right conditions—shifted from an unalive assemblage into something scientists would call life.

Billions of years and a lot of evolution later, researchers are now attempting to reproduce that shift in their labs. They are, put simply, trying to create life. If they mix Goldilocks ingredients together in a Goldilocks way, the ingredients will hopefully coalesce into cells, with all the parts cells have and the functions they perform. And they’re getting awfully close.

Scientists working toward this endpoint have different aims and generally fall into two main groups. The first wants to understand how life came to be in the first place by replicating the conditions of this long-ago lonely planet; the second want to design cells from scratch that can potentially perform specialized jobs for humans, from medical treatment to manufacturing without the billions of years of evolutionary baggage that can have unpredictable effects.

Both sides of the effort are growing, with a coordinated program funded by organizations like the National Science Foundation and research supported by NASA. There has also been more regulatory scrutiny—a sure sign of scientific progression, and the reality of the technology’s risks.

Kate Adamala, a synthetic biologist at the University of Minnesota, was in the former research group, focusing on the origin of life, but decamped to the latter after graduate school. “I want something that’s still very foundational, very discovery-driven, but it would be nice if there were actual real-life applications,” she said. She found it in synthetic biology.

Today, she uses the field’s modern tools to try to make life in the lab—and she recently made a breakthrough: In July, she announced that she had built a synthetic cell she dubbed SpudCell. It can feed, compete for food, grow, divide, and replicate its DNA—showing, in other words, a cell’s complete life cycle.

Still, it’s not quite “alive,” scientists tend to agree: It can’t whip up its own protein-makers, the full genome doesn’t carry through more than a few generations, and it can’t evolve on its own. The paper also has not yet been peer-reviewed or published in an academic journal.

If they mix Goldilocks ingredients together in a Goldilocks way, the ingredients will hopefully coalesce into cells, with all the parts cells have and the functions they perform. And they’re getting awfully close.

If the SpudCell were alive, it wouldn’t be life as it arose on ancient Earth, necessarily a basic and limited form. But research on it and related work could illuminate the basics of biological functioning, while also yielding ginned-up cells that humans could put to work by, say, creating chemicals that now come from petroleum. Adamala liked the combination of basic science and application. “That turned out to be jackpot,” she said in an interview with Undark in February.

She’s been working toward that, building what those in the field call “synthetic minimal cells,” cells constructed with known ingredients, to accomplish specific tasks (like metabolizing); they, according to Adamala’s university webpage, have “some, but not all functions of live natural cells.” The version she has recently worked on, SpudCells, are essentially tiny fat bubbles with DNA or RNA inside. That genetic material can instruct the fat bubble to make proteins, as it does in natural cells. Her creations can also merge, a capability that could allow them to interact with human cells later and send their protein products to the outside world. That would make them useful to the medical or manufacturing communities, since the molecules that become plastic or medicine don’t do any good if they remain locked inside a cell’s boundaries. SpudCell is an advanced version of these ideas, using 90,000 genetic base pairs and a premade mixture of enzymes, borrowed from actual life. The cell can copy DNA, feed using proteins it produces, and divide once it’s fed sufficiently.

As synthetic biology develops, the field’s quest to make life in the lab could help lead to targeted medical treatments, but that pursuit could also someday—without proper regulation and oversight—pose safety and containment concerns, if the technology were weaponized to deliver harmful material or if a leak allowed the cells to travel beyond their intended target. And while Adamala’s work replicates—as do the Frankenstein creations of other researchers—some aspects of life, its products can’t, say, replicate themselves, as a normal cell would.

A series of microscope images shows SpudCell dividing from one cell into two. SpudCell is the first synthetic system built from non-living components to complete a full cell cycle. Image Credit: Kate Adamala / Adamala Lab

To say someone has created life in a lab, they would need to get a synthetic cell to do all the things life does, at once. But getting the parts to be more than their sum has proven difficult. “Up until now, the conversation has been, ‘Well, you put all these things in physical proximity at the same time, and then magic happens, right? Life happens,’” said Elizabeth Strychalski, head of the Cellular Engineering Group at the National Institute of Standards and Technology.

But life hasn’t happened—at least not yet.

Even if a scientist were to succeed, determining that’s the case might be complicated, because there is no official definition of what makes something alive. Adamala wants to make life in a lab. But what, actually, does that mean? “I can’t tell you, because I don’t have a definition of life,” she said.

Adamala, for one, is sanguine about that uncertainty. “We quietly ignore that problem and just move on with ‘I want to make something that looks like a cell,’” she said in the February interview conducted before she had released the SpudCell paper. “And I will know it when I see it.”

The dictionary version of life might be squishy, but many scientists do generally agree on a few of its characteristics: It has to reproduce, turn fuel into energy through metabolism, respond to that environment, maintain its own internal environment, and evolve.

How some chemicals in Earth’s younger years came to accomplish all those things remains one of science’s biggest mysteries. Life, in many ways, comes down to chemistry; studying the interaction between certain molecules can reveal how they may have come together and undergone chemical reactions that eventually became biological.

But finding the answer is one goal of the Origins of Life Initiative at Harvard University, a collaborative research group that began around 20 years ago. “I met and started talking to people who were working on the deep history of Earth,” said Dimitar Sasselov, an astronomer and the organization’s founding director. “What was the Earth like when life came about?” And what, he wondered, did the early evolution of that life look like?

Sasselov decided that understanding that tipping point—how life, once it emerged, transformed and took over the planet—deserved more study. But it required a big tent, with people from many disciplines: thus, the Origins of Life Initiative. The researchers all had to become mini-experts in each other’s fields. For his part, Sasselov jokes that he got his initial knowledge of chemistry from reading the “Complete Idiot’s Guide” to the topic.

As the Origins of Life Initiative was spinning up, Sasselov helped spearhead the Kepler Space Telescope launch in 2009. The project’s goal was to discover planets a bit like Earth, orbiting their stars in ways that might make them habitable. The telescope went on to discover thousands of planets of all sorts. At the same time, attempts to create life, or its pieces and parts, in labs across the world accelerated. Sasselov thought that was an interesting way (perhaps the only way) to find out how life might have arisen here or on those other worlds that Kepler saw.

Sasselov, for instance, looks at how RNA—which, among other tasks, acts as a messenger to deliver DNA’s genetic instructions—interacts with small amino acids and peptides. He’s not himself trying to build a full cell, but rather trying to understand how initial cells’ building blocks may have stacked together.

Much of Sasselov’s work focuses on shining (literal) light at such foundational molecules and seeing which can preferentially survive and even harness the ultraviolet bombardment, which the sun would have blasted at early Earth. Knowing more about that could reveal key ingredients in life’s assembly.

In fact, many experts suspect that life may have started after RNA formed, spurred on nearby chemical reactions, and spontaneously started to replicate; only later would the more complicated genetic material, DNA, have formed from its simpler upstart. Earlier this year, researchers at the MRC Laboratory of Molecular Biology and the Inria Lyon Centre published a Science paper outlining their discovery of a short strand of RNA that came close to replicating itself.

Many experts suspect that life may have started after RNA formed, spurred on nearby chemical reactions, and spontaneously started to replicate.

Other researchers recently combined water and silica, and shot electricity at the mixture to simulate lightning. Almost like magic, their concoction formed a solid organic film, made of polymers—large molecules made of similar units bonded together—of hydrogen cyanide. The film contained little spherical structures the research team deemed “protocells.” The structures and the ingredients could plausibly have existed together eons ago, and with further chemical evolution—way back when, and perhaps soon in the lab—could organize into something more complex.

Work at the Origins of Life Institute and other beginning-of-life-focused research groups complements work done by scientists like Nobel Prize-winner Jack Szostak, at the University of Chicago, one of the leaders in investigating the idea that RNA may have preceded, and become key to, the first cells.

Szostak has worked on creating cells whose boundaries are fatty acids, with RNA inside—similar in idea to Adamala’s minimal cells. (Adamala did her graduate work in Szostak’s lab.) “Experimentally, we’re trying to just build a system that we think, in some ways, mimics what the first simplest cells, otherwise known as protocells, would have looked like,” said Szostak.

In the right conditions, Szostak’s creations can divide and split the RNA so that each of the pair gets some. The lab has also made cell-like structures whose RNA can create a copy of itself internally. Recently, his team has investigated whether RNA segments arranged into a circle might point the way toward actual genetic replication. They came up with this idea, he says, during the early months of the Covid-19 pandemic, when they couldn’t go into the lab but could talk to each other.

All of these parts need to come together, from the soup to the cell, for the astrobiologists to declare they’ve made life in the lab. That could happen in a year, Sasselov said—or a decade. “It is not that we need a bigger lab. It is not that we need a bigger computer. It is not that we need a specific reaction to work or not to work,” he said. “It is really more of a puzzle which has to suddenly come together.”

Adamala, meanwhile, is trying to put the puzzle together in a different way—one with more tools available. “When you’re trying to do it the proper way, from the astrobiology point of view, you’re basically not allowed to use anything that works well,” she said.

By that she means that scientists like Szostak limit themselves to the conditions of an old Earth; Adamala, on the other hand, can work to create a living cell using, essentially, any means necessary; she can print or clone strands of DNA, or design a protein with AI, for example. In creating the SpudCell, for instance, Adamala began with a mixture of the molecules a cell would need, already formed. She also took some chemical ingredients from a virus, and E. coli, and added the lipids that would form the cell’s membrane. She gave the cells specialized food that allowed them to grow, and used a protein that made the cell bend inward when its surface became crowded from feeding. That bend became a split, producing a new cell that could also eat and grow. When the research team created a mutant SpudCell that was a more efficient feeder, and mixed it with the original-flavored cell, it gobbled up more of the food than the predecessor and came to dominate the population, a compelled proto-evolution.

Adamala’s recent work embraces tools that allow her to construct cells from the ground up because her research’s scientific motivation is not to understand how the first life came to be, but how life fundamentally works and can be made to work for humans today. For instance, she imagines using synthetic organisms to produce petrochemicals that currently only come from oil, or to enable new cancer therapies. She and a collaborator have started a new nonprofit organization called Biotic, which intends to make the SpudCell an open-source system that others can license to do their own work.

SpudCell’s ingredients are all known. That’s true of any synthetic minimal cell, and such creations could tell us, Adamala said, which ingredients are strictly necessary for something to be alive—many of natural cells’ components may actually be extraneous to the basic “life or not” question.

Despite the differences in the two groups’ approaches, Sasselov sees a unity in their motivations. “We both want to understand at a fundamental level what life is,” he said. And to do so by building life. “But how we get there is different, and this is why we have so many astronomers and planetary scientists, geologists involved in this in our group,” he continued. “The other groups are mostly composed of biochemists, chemists, biologists.”

But the two camps meet and talk regularly, and discuss their progress. Sasselov thinks that once either group gets close to the ultimate goal, their similarities will increase. “We will essentially merge, because some of the techniques that they have discovered and developed will be useful to us, and many of the things that we have discovered will be useful to them,” he said. “And then we’ll build something which is the same, but with both of our efforts. So despite the fact that right now, we seem to be going on different trajectories, eventually it will be a big overlap.”

“We both want to understand at a fundamental level what life is.”

Scientists on Adamala’s side of the fence have succeeded in replicating some of life’s basic functions. Some synthetic biological creations can send and respond to signals in the environment. Some can communicate with each other. Others can make fats or protein building blocks; metabolize; replicate genetic material; and even introduce mutations that count as evolution. SpudCell can eat, grow, replicate, and do that rudimentary-but-not-quite-there version of evolution. But it can’t do all the things a normal biological cell can, like actually evolve, or make its own protein factories.

“The biggest thing that’s left to do, and it sounds simple, but it really is not, is to put all of it together,” said Adamala in the February interview. SpudCell is an attempt at that.

One key figure in this endeavor was J. Craig Venter, who led the effort to sequence the whole genome of an organism, back in the 1990s. Venter, who founded the J. Craig Venter Institute, passed away at the end of April at age 79. His goal in that early effort wasn’t just the sequencing: It was finding out what the genes did. In the 2000s, he and collaborators made synthetic genomes for a virus and a bacterium. For the latter, they used what they had discovered about genes’ functions. In 2010, they followed that up by putting a synthetic genome—designed in a computer and assembled from real DNA fragments—into a normal, living cell.

Six years later, his institute published the results on their synthesis of the first minimal synthetic bacterial cell, also by implanting synthetic material into a preexisting cell. But it replicated strangely, growing as a “giant blob,” in Venter’s words, with varying shapes, rather than staying small and similar as expected. By 2021, the group added 19 new genes, seven of which are linked to division; the cell split like normal.

An electron microscope image of minimal bacterial cells, made by a team led by J. Craig Venter using a synthetic genome containing 473 genes. Image Credit: Tom Deerinck and Mark Ellisman / National Center for Microscopy and Imaging Research / University of California, San Diego

Shortly before his death, Venter said he didn’t know exactly what would come next. “I have my hope list, what we’d like it to be,” he said in March. For one, making a synthetic cell without having to wipe a living one and stick new DNA in it; creating it more from the ground up.

Once truly synthetic life exists, Venter said it could enable the kinds of things that brought Adamala to this side of the research, by giving a cell specific genetic instructions—making, say, drugs, chemicals, construction materials, and even food.

Together, fundamental researchers focused on the origin of life and synthetic biologists could show how life came to be, how its pieces and parts work together, and how making new life could change the world’s workings today as it did billions of years ago.

Not everybody fits neatly into those two boxes, though. Take Juan Pérez-Mercader, a senior research fellow at Harvard University. He’s approaching this central question of biology and experimental chemistry as a trained physicist. Before he tried to make some semblance of life in a lab, he worked on things like unifying the fundamental forces of the universe.

But Pérez-Mercader eventually tired of those particular equations and turned, instead, to the fundamental properties of living systems—handling information, self-reproducing, evolving, and metabolizing. Could they also be represented with equations? With the idea, he founded and directed Madrid’s Centro de Astrobiología, and then later took his work to Harvard.

He began by making equations to model the general properties of life that scientists agree on, and turned those into computer solutions. Out popped swimming, digital single-celled beings. “If I do this in a computer, I can do this with chemistry,” he said.

His chemistry started with a compartment—a kind of container for the cell’s parts, like Adamala and Szostak’s lipid bubbles. But Pérez-Mercader wanted to do things a little differently: He wanted to show that you could use his equations to make something that qualified as “alive” without being beholden to particular molecules that make up life as we know it—to show, in other words, that the properties we use to define life aren’t actually ingredient-specific. “In my lab, upstairs from this office, there is a sign that says, ‘No biochemistry, no biochemicals allowed,’” Pérez-Mercader said.

A breakthrough came when his team found out about a technique called polymerization-induced self-assembly, used in other fields. In short, they could put carbon-based molecules that are hydrophilic—meaning they like water—into H2O with carbon-based monomers and photo initiator molecules that convert energy from light into chemical energy; hydrophilic molecules, by virtue of how their electrons are arranged (unevenly), are attracted to water molecules, whose electrons are arranged similarly.

“In my lab, upstairs from this office, there is a sign that says, ‘No biochemistry, no biochemicals allowed.’”

Exposed to light, these molecules link to each other to form polymers. When they do, they gain a hydrophobic, or water-resistant, part that has evenly distributed electrons. Their water-loving ends then naturally turn to face the water, while the water-fearing ends fold themselves inward, away from the water. They thus form themselves into little spheres, called micelles. These micelles are filled with fluid and separated from the environment.

The molecules continue to link to each other, making the micelles thicker. The pressure inside grows, and eventually a portion of the structure breaks off, to form a new micelle, which then itself does the same—reproduction. The new micelles are not clones of each other, but slight variants—a primitive kind of evolution. And because they’re using light to sustain themselves, they perform a sort of metabolism.

The system, then, exhibits versions of the characteristics of life, integrated into one system, without ingredients like RNA, DNA, lipids, sugars, or proteins.

Pérez-Mercader’s micelles are not made with typical biochemistry, like DNA or RNA, so not everyone agrees they would qualify as alive. The journal Cell reportedly rejected the paper about SpudCell after one peer reviewer said it was not real biology. Adamala told Undark she doesn’t usually comment on the review process. Many, if not most papers, she wrote via email, go through more than one submission. “For people outside of science it sounds like a big deal, for us it’s Tuesday,” she wrote.

Synthetic micelles in Juan Pérez-Mercader’s lab form autonomously and grow in the presence of light and food, making the micelles thicker until they mature into vesicles. The vesicles eventually release what Pérez-Mercader calls “spores” that grow into new micelles and then into new vesicles, thus performing a reproductive cycle.

People who come from the origins of life side of the equation are sometimes criticized for assuming all life will be like familiar life on Earth, said Robert Dorit, a biologist at Smith College. But they’re also criticized if they consider less Earth-chemistry-specific options. “I think you can defend an argument that says our definition of life should also not be unnecessarily tethered to what we know here,” Dorit said. But, to him, “alive or not” is not ultimately the most interesting question. Any life made in a lab won’t be familiar life, which formed itself thanks to eons of evolutionary pressure. And trying to create weird life, not quite like a typical cell, can reveal things about life that formed naturally.

Pérez-Mercader, for one, says that his work is proving two things at once: that you can turn nonlife to life in the lab, and that what we think of as life doesn’t depend on a particular chemistry. And so, his thinking goes, it could have arisen on planets very different from ours.

Pérez-Mercader’s research, though, swings back around to a central point, as does Adamala’s: If scientists don’t have consensus on what life is, how will they know when they’ve created it?

The answer falls partly to Strychalski, of the Cellular Engineering Group at the National Institute of Standards and Technology. NIST’s governmental duty is to measure things. Sometimes that’s how long a meter is. Sometimes it’s how alive a fake cell is. It’s different from measuring the materials in nanotechnology, or devising metrics for cybersecurity. Instead, in this field, “we’re building with the stuff that we are also made of,” she said.

“It’s not going to get up and walk out of your petri dish.”

Strychalski also does research in this scientific field, having worked with Venter’s institute on a recent project, for instance. And she recently attended a workshop that Adamala organizes, under a research network called Build-a-Cell, which since 2017 has been gathering researchers twice a year who are trying to make life in the lab. “That’s my favorite thing about this community, is that it actually is a community,” Adamala said.

One thing was clear at that Build-a-Cell workshop: “Some folks are getting really close to assembling living cells from nonliving material,” Strychalski said. But that pesky part about getting all the nonliving materials to work together on their own still dogs them.

Strychalski is still investigating what measurements are needed to troubleshoot cell-building and what measurements are needed to decide that ultimate question: Does it live? “It’s not going to get up and walk out of your petri dish,” said Strychalski.

Researchers Elizabeth Strychalski and David Ross work with NIST’s Living Measurement Systems Foundry, which was built to develop measurements to better engineer living cells. In her work, Strychalski develops measurements needed to understand and troubleshoot building cells. Image Credit: J. Stoughton/NIST

The answer to the question is important to determine not only who will be the first to create true life in the lab, and what is needed to do so, but also for safety. As SpudCell develops, Adamala plans to actively pursue biosafety measures, she wrote in a follow-up email. “Once it becomes more robust and able to grow and divide faster and more efficiently, we’ll need safeguards,” she wrote, which are designed to stop the cell from replicating outside of a controlled environment.

“If someone does make something that’s alive, or alive in a new way, in the laboratory, I want to make sure that they realize that’s happened,” Strychalski said, “so they can bleach the bench.”

Strychalski isn’t alone in being mindful of synthetic biology’s risks: Adamala, Szostak, Venter, and dozens of others penned a 2024 Science paper, about the potential for creating something called “mirror life.” On Earth, all known biological molecules of a given type face a single direction: All genetic material is “right-handed,” for instance. The amino acids making up proteins face the other way and are “left-handed.” But some scientists have begun work on left-handed DNA, or right-handed proteins. Maybe someday—at least a decade from now, the authors estimate—there could be whole mirror cells.

Life and lifelike elements like this, though, could pose huge dangers, according to the authors. They might be pathogens against which we have no defenses, or they could act like invasive species. The field is working to get ahead of those possibilities. “We’re seeing basically a global push for the moratorium,” said Adamala, discussing a kind of synthetic cell deemed particularly dangerous. While there aren’t yet amendments to international conventions, the wheels of bureaucracy are turning. The UNESCO International Bioethics Committee, for instance, recommended a moratorium on creating mirror life, and a group convened by the UK Government Office for Science recommended potential regulation against the development of mirror genomes that can self-replicate. The UN Secretary-General’s Scientific Advisory Board suggested defining “red lines” in mirror-life research policy.

Strychalski points to this as an example of where getting ahead isn’t just good—it may be necessary. “There might not be a second chance,” she said. And she thinks more monitoring could be good for synthetic biology research in general. “Right now, there’s like no requirement for monitoring your laboratory or office space for accidental release of engineered biological anything,” she said.

“If someone does make something that’s alive, or alive in a new way, in the laboratory, I want to make sure that they realize that’s happened,” she said, “so they can bleach the bench.”

She grew up in science thinking about such potential bad outcomes, since she, like Pérez-Mercader, came from physics. Physicists tend to keep their field’s Manhattan Project roots and potential for dual-use technology—technology that is useful for both benign civilian and potentially malign military purposes—in mind, she said, remembering how quantum mechanics was weaponized into an atomic bomb by the very people who were in the process of discovering it.

Just because you can do something doesn’t mean you should, Strychalski notes. “I’m telling my toddler this constantly,” she said, “but I think it holds in the laboratory as well.”

The post The Evolving Quest to Build Life in a Lab appeared first on SingularityHub.

Jonathan the giant tortoise was born when Darwin was still a young man. Youthful gene activity may account for his exceptionally long life.

Getting a DNA sample is simple. Rub a Q-tip a few times inside your cheek, and voilà.

Not so with a 400-pound Aldabra giant tortoise. At the age of roughly 194, Jonathan is the oldest known living land animal on Earth. He’s spent most of his life lounging on the manicured lawns of the governor’s residence on the island of St. Helena, where he arrived as an adult. Though blinded by cataracts, Jonathan still chows down on grub and has excellent hearing.

Even among long-lived tortoises, Jonathan is exceptional. He’s lived around a century longer than others of his kind. Now, thanks to his DNA, an intrepid team of scientists may know why.

Jonathan (left) arrived in St. Helena in 1882, around the time of this photograph. Image Credit: A.L. Innes

Getting a sample was tricky and dangerous. Jonathan’s veterinarian, Joe Hollins, lured him in with fresh greens, then quickly wedged his mouth open to scrape cells from inside his cheek. The tortoise tolerated the unusual procedure for only a few seconds. Any longer, and Hollins might have faced the wrath of Jonathan’s razor-sharp beak.

But the maneuver paid off. After sequencing the sample, the team found that Jonathan’s genome was better maintained compared to tortoises roughly a century younger.

As animals age, their DNA accumulates mutations. Telomeres, the protective end caps of chromosomes, shrink. Chemical tags that control which genes are turned on or off also go awry, straying from their normal patterns and settling on unexpected spots across the genome.

For study author Stephen Clark, founder of the nonprofit Kallel Foundation, this growing disorganization, called entropy, is central to aging. Yet Jonathan has weathered plenty of biological wear and tear despite his advanced age.

“Nature has already solved the puzzle of aging in remarkable ways, and Jonathan’s genome provides a blueprint for cellular resilience,” said Clark in a press release.

Aging Outliers

Animal research has given us clues to how we might live healthier, longer lives.

At one end of the spectrum are short-lived creatures. Studies of lowly roundworms and fruit flies—often squished or swatted—have uncovered genes and molecular pathways that let cells sense nutrients, withstand stress, and clear cellular waste. These processes falter with age.

At the other extreme are animals that seemingly don’t age. Take the hydra, a tiny, carnivorous jellyfish-like animal. In long-term studies, some species showed no detectable rise in mortality or lower reproduction with age and retained the ability to regenerate after severe injuries. Their “immortality” may come from a steady supply of stem cells and resilience against senescent cells that no longer work properly.

Naked mole rats have also caught scientists’ attention. These nearly hairless rodents can live more than 30 years, roughly 10 times longer than a mouse of similar size. They resist many types of cancer, maintain strong hearts and muscle mass into old age, and rarely develop Alzheimer’s disease, even with toxic protein buildup. By studying these animals’ genetics, scientists have discovered ways they preserve gene expression machinery and maintain protein stability as the clock ticks on.

Tortoises offered more inspiration. In 2019, scientists sequenced the genome of Lonesome George, the last known Pinta Island giant tortoise, who died at an estimated 100 years old. His genome revealed beneficial adaptations in DNA repair, immune function, and inflammation. A subsequent study comparing multiple turtle lineages found shared genetic changes associated with cellular resiliency against stress and cancer protection.

“Each of these long-lived species has some kind of special trick,” biologist Steve Austad at the University of Alabama at Birmingham told The New York Times. “And some of those may turn out to be relevant to human aging.”

But as the oldest land creature, Jonathan is irreplaceable. Hidden in his DNA may be another set of tricks for keeping cells, and maybe the entire body, functioning for longer.

Spirited Supercentenarian

Collecting Jonathan’s DNA without getting bitten or kicked was only the first hurdle. Shipping the precious cargo across the ocean without damaging it was another. Then came the headache of piecing his genome together.

Cheek cells yield shorter DNA fragments that must be stitched together, and samples can be contaminated with bacteria. The first try at reconstructing Jonathan’s genomic blueprint had gaps, so the team, led by the Nashville-based nonprofit Kallel Foundation, used DNA from a younger tortoise to fill them in. Roughly 95 percent of the resulting Frankenstein genome was Jonathan’s.

A blood draw, the gold standard for DNA sequencing, might have yielded a clearer map. But St. Helena officials ruled it out because of the risk of infection.

Even so, Jonathan’s genes revealed intriguing differences compared to four other Aldabra tortoises roughly a century his junior. Of the 287 genes unique to Jonathan, many were involved in DNA repair or preventing telomeres from shortening—both of which help keep the genome stable. Roughly a dozen of Jonathans’ genes overlapped with genes implicated in naked mole rat longevity and that of other long-lived animals, hinting at shared evolutionary strategies.

But the surprise finding related to his epigenetics, the system of chemical tags, or DNA methylation, that regulates gene activity. Young animals tend to maintain relatively consistent methylation patterns between cells, coordinating which genes switch on or off. With age, some of these marks disappear while others attach chaotically, making gene activity less predictable.

Clark calls this growing disorganization entropy. As expected, Jonathan’s epigenome showed higher overall entropy than his younger peers. But some genes were spared.

Those involved in mitochondrial function kept a surprisingly youthful profile, comparable to that of a five-year-old Aldabra. Mitochondria, the cell’s energy producers, are closely linked to aging and age-related diseases. Maintaining mitochondrial health may have helped fuel DNA repair systems and warded off disarray in the epigenome—potentially creating a virtuous cycle to keep disorganization in check.

The findings echo genetic research on a woman who lived past 117. Although she harbored clusters of mutated blood cells and signs of DNA damage, her mitochondria appeared healthier than those of women decades younger.

How genes behave is just one piece of the aging picture. Diet, exercise, environment, and mental health all play roles. Jonathan mostly lived a life of luxury, with abundant fresh food, a safe home, and his veterinarian, Hollis, close at hand.

These comforts may have contributed to his longevity. But the results point to potential biological pathways that the body uses to stay spry for longer. The team plans to map the genomes of more Aldabra tortoises and other long-lived creatures. By studying these outliers, we may better grasp our own longevity and learn how to fight off age-related diseases.

“Our goal is to take these evolutionary insights and immediately translate them into practical, affordable treatments for everyday people,” said Clark.

The post The Oldest Living Land Animal Is 194. DNA May Now Reveal the Secret to His Longevity. appeared first on SingularityHub.

Cancer interception aims to prevent abnormal growths from developing into tumors.

Genetic mutations cause cancer. This idea has long driven strategies that aim to lower risk or determine when to look for early signs of disease. Don’t smoke. Minimize alcohol. Get vaccinated against viruses linked to cervical or liver cancer. Lifestyle changes can reduce the chances our cells accumulate DNA mutations that make them grow out of control.

But we might be missing part of the picture. Some people with cancer-related mutations never develop the disease. Others live healthy, have no family history, and suddenly learn they have cancer—and it’s already spread.

To Miriam Merad at Mount Sinai in New York, inflammation is the other half of the puzzle. Cells that acquire mutations and begin to take on cancer-like traits sometimes remain benign, likely because the immune system keeps them in check. By amping up the body’s own surveillance in people with abnormal growths—before they turn malignant—it might be possible to stop tumors from developing in the first place.

The new field, called cancer interception, is gaining steam. Clinical trials for lung cancer are in the works. Cancer vaccines, which train the immune system to destroy abnormal cells, have shown early promise. In one trial, roughly 90 percent of participants with pancreatic abnormalities generated immune cells better able to recognize mutated cells.

Other researchers are taking the idea further. In one study, they gave mice with precancerous pancreatic growths an approved drug to ward off the growths’ transformation into cancer, reshaping the immune landscape and extending survival.

“Yes, those mutations absolutely contribute to dysregulation of the cell that will become cancerous,” said Merad in an interview with Nature. But that’s not enough. The realization that inflammation is somehow “necessary for cancer initiation suggests that we can intervene.”

The Life of a Cancer

Cells don’t turn cancerous overnight. This is what makes the disease so insidious. With aging, environmental hazards, or simply replication, cells constantly pick up DNA changes. Most mutations are relatively harmless.

Occasionally, however, a mutation hits a driver gene that gives a cell an advantage over its neighbors. A gene called KRAS, for example, normally encourages growth. But mutated versions become stuck in the “on” position, continuously ordering cells to divide. Another gene, P53, often dubbed the “protector of the genome,” can lose its ability to eliminate damaged cells. And when DNA-repair genes go awry, mutations can accumulate even faster.

A driver mutation doesn’t necessarily make a cell cancerous. Evolutionarily, it gives the cell a leg up. Armed with the ability to divide and resist death, its descendants gradually take over the cellular neighborhood, competing with healthy cells for space, nutrients, and survival signals. Rapid expansion also means the cells pick up more mutations in a dangerous feedback cycle.

Eventually, some gain the genetic and molecular signatures of a precancerous cell. Most of these remain harmless for decades. Others rewire their surroundings, setting up a hostile environment that blocks immune cell surveillance and boosts inflammation, eventually allowing them to spread uncontrollably.

Researchers aim to halt that journey with cancer interception.

Going on Offense

For years, scientists have believed cancer causes local immune system changes. When they reach a tumor, some immune cells go rogue. Macrophages, rather than literally eating up the enemy, are reprogrammed by the tumor microenvironment to dampen other immune cells.

This process may start earlier than expected. Last year, Merad and colleagues found that tumors send cues to bone marrow precursor cells, nudging them towards a pro-cancer macrophage state. By the time they reach tumors, the protectors have already turned to the dark side.

“This work changes the way we think about the timing of immune suppression in cancer,” said study author Samarth Hegde in a press release. “We need strategies to intervene much earlier, while these cells are still developing, so we can stop them from becoming cancer’s allies in the first place.”

The team found one protein, NRF2, acted as a switch. Inflammation from the tumor environment changed the protein’s function, such that it encouraged nascent macrophages to become immune-suppressive. Blocking NRF2, either by disabling its gene or with drugs, led to a stronger immune attack against early lung cancer in mice.

The results inspired a new way to fight cancer. In addition to attacking a tumor, treatments should also work to change the environment that helps it grow.

Hegde and collaborators are now testing the idea with an existing drug from Novartis. Originally developed for heart and blood vessel disease, the drug, canakinumab, surprisingly slashed lung cancer incidence and death by over 67 percent in an earlier trial cohort of roughly 10,000. The unexpected findings inspired researchers to study inflammation as a driver of cancer.

In mice genetically engineered to develop lung cancer, blocking the same inflammatory pathway similarly reduced eventual tumors and shifted the lungs’ immune environment towards a stronger attack on budding tumors. And a new study found other potential targets for interception in immune cell networks as precancerous lung cells developed.

Merad’s team, alongside collaborators, are launching the Can-Prevent-Lung trial to test canakinumab in people with persistent, high-risk lung growths. CT scans can spot these abnormalities, but not all will become cancers. Figuring out who actually needs the treatment is an open question.

Medications have side effects, and “we never give a drug to someone that doesn’t need it,” said Merad.

Other teams are grappling with a similar dilemma. One solution may be catching cancer earlier. The PRECEDE trial aims to monitor over 20,000 people with a family history of pancreatic cancer or abnormal growths to see if frequent checkups improve survival. Like lung cancer, pancreatic cancer is notoriously dangerous. The diseased cells are often highly invasive, avoid most treatments, and cause vague symptoms that make diagnosis difficult.

The team plans to explore whether a blockbuster pancreatic drug, daraxonrasib, currently approved only for advanced cancer, can boost survival when given after pre-cancer growths are removed. They’ll also test whether AI can spot tiny abnormalities invisible to the naked eye in medical scans, flagging people who might benefit from interception.

Although experts are excited about the tactic, a thorny question remains: Will people take powerful drugs when they don’t actually have cancer? Daraxonrasib, for example, can cause rashes so severe that some patients stopped taking it, along with other side effects.

The best candidates may be identified by a marker often missed during checkups. We never monitor inflammation levels, said Merad. Her team is now evaluating a blood test and developing cheaper, easier strategies for cancer screening. Better risk prediction might help doctors decide whether to intercept cancer or leave benign growths alone, opening the treatment philosophy to more people.

It won’t be easy, but to Merad, the future is bright. “It’s an extraordinary time of optimism and possibilities,” she said. From biotechnology advancements to AI that can integrate biochemical and imaging datasets, “it is impossible not to be hopeful.”

The post Radical New Tactic Would Stop Deadly Cancers Before They Start appeared first on SingularityHub.

The technology could one day decode dreams—or reveal our most private thoughts.

Brain scans contain clues about what someone is looking at, but turning those signals back into recognizable images is difficult. Now, researchers in Israel have built an AI system that recreates what people are looking at with uncanny accuracy.

Functional MRI scans, which track blood flow in the brain, are a common tool for decoding what is going on in people’s minds. Previous research has shown they can reveal neural activity linked to what a person is seeing, and scientists have long been interested in using AI to convert these signals into a visual representation of the scene in front of them.

So far, this has proved difficult, with early attempts producing blurry images and AI image generators inventing details that bear little resemblance to what the person saw. But now, a team at the Weizmann Institute of Science has developed a system called Brain-IT that sets a new bar for accuracy and can be adapted to a new person using just an hour of scan data.

“All in all, really we outperformed the others by a significant margin,” Michal Irani, who led the research, told MIT Technology Review. She adds that the team hopes the tool could enable new discoveries about how the brain works and even help paralyzed people communicate.

A series of images reconstructed from brain scans. Image Credit: Beliy et al via arXiv Beliy et al via arXiv

Existing methods for reconstructing images from brain scans can look good, but they often get important details like the position or color of objects wrong, thanks largely to AI image generators’ willingness to fill gaps in the data with what they expect to see.

The success of the new system, which was presented at this year’s International Conference on Learning Representations, is down to a novel design choice. Rather than trying to translate the brain signals in one shot, it uses two neural networks to separately predict an image’s layout and its contents. The outputs from these models are then fed into an image generation model that produces a detailed picture.

The team trained the system on a publicly available dataset in which eight volunteers each viewed around 9,000 photographs in a high-resolution brain scanner. The researchers also used a previously developed brain encoder that predicts how the brain would respond to a given image to simulate scans for around 120,000 extra photographs.

When tested on data from four of the volunteers, Brain-IT outperformed ten other reconstruction methods on seven out of eight standard measures of image similarity. In particular, it did much better at capturing the layout of a scene—where objects sit, as well as their colors and shapes.

The system can also be adapted quickly to work with new people because the way it processes brain scans is built around groups of brain cells whose functions seem to be shared across individuals. Calibrating it for someone new largely comes down to working out where those groups sit in their brain.

With one hour of scan data from a new volunteer, the system matched the results of previous methods trained on a full 40 hours, which could make the approach far more practical for researchers. “None of us can afford 40 hours of imaging for a new subject,” Tommy Sprague, a neuroscientist at the University of California, Santa Barbara, told MIT Technology Review.

But the technology’s ability to extract information about what someone is thinking also raises concerns around mental privacy, Sprague added. The reliance on fMRI, which requires a willing subject to lie still inside a scanner, currently limits misuse, but Irani and others are working on decoding brain activity from simpler devices like EEG, which records electrical signals using electrodes on the scalp.

While Irani acknowledges the potential risks, she’s trying to focus on the positive potential of the technology. In the future, she hopes to move beyond still images to capturing video and even audio, which could help decode imagined scenes and dreams. “That’s something we don’t have yet,” she says. “But we’re striving to achieve it.”

True mind reading may still be some way off. But due to the power of AI, it looks increasingly likely that our innermost thoughts will no longer be as secure as we’ve imagined.

The post This AI Reconstructs What You’re Looking at From a Brain Scan appeared first on SingularityHub.

Artificial Intelligence

‘Breathtaking,’ ‘Devastating’: Mathematics Reels After New OpenAI ReleaseSiobhan Roberts | The New York Times ($)

“Talking to mathematicians after the release, it is clear that it is a highly conflicting environment, motivating a wild range of reactions. …’The solutions announcement, [Dr. Bridson] added, ‘will be exciting for many, frightening for others, and devastating for some. ‘In all cases,’ he said, ‘the struggle to wrestle human understanding from the machines’ formalities will unleash greater ambition for what we achieved (working with AI agents) in this new era of mathematics.'”

Artificial Intelligence

OpenAI’s Math Solutions Aren’t Meeting the Field’s Standards YetTim Fernholz | TechCrunch

“OpenAI fell short of … standards [called for by an advisory group of elite mathematicians], particularly where the mathematicians emphasized the need for human understanding of a mathematical result. That’s especially concerning after a new paper highlighted gaps between the natural language and formally expressed solution to a million-dollar problem ostensibly solved by OpenAI’s models.”

Biotechnology

Cancer Vaccines Are the NIH’s Next ‘Big Bet’Gina Kolata | The New York Times ($)

“The vaccines are treatments that are unique to each patient by prompting the immune system to attack a patient’s specific cancer. The injections teach the immune system’s T cells to home in on aberrant proteins found on a patient’s tumor and nowhere else.”

Tech

SpaceX Makes Big Play to Become a Wireless CarrierPatience Haggin and Drew FitzGerald | The Wall Street Journal ($)

“SpaceX reached a deal to buy cellular spectrum licenses from investment firm Grain Management, its biggest move yet into a wireless market dominated by traditional telecom companies. …The spectrum could serve SpaceX’s stated ambition of moving its Starlink satellite service beyond broadband into the wireless market to compete with the likes of AT&T and Verizon.”

Artificial Intelligence

Aligning AI With Human Goals Might Be Impossible, Says AI Prof. Stuart RussellRocket Drew | The Information ($)

“As a result, the AI industry’s decision to pursue large language models ‘could easily be a $10 trillion mistake,’ he said. It could be that ‘pursuing a technology path that can never support the guarantees of safety that we will need when systems become sufficiently capable to be of really high value to us—it’s a wrong turn that we took,’ so further investment in these models is ‘throwing good money after bad,’ he said.”

Tech

Oracle, Broadcom and SpaceX Seek Blockbuster Debt Deals to Pay for AI ChipsAnissa Gardizy, Matt Wirz, and Justin Baer | The Wall Street Journal ($)

“Cloud providers such as Amazon Web Services and Oracle have traditionally financed computing hardware through their own cash flows. For their AI build-outs, the companies issued hundreds of billions of dollars of bonds, pushing the public debt market to its limits. Now, some buyers are turning to Wall Street investment firms to help fund purchases totaling tens of billions of dollars per deal.”

Science

At 194, Jonathan the Giant Tortoise Knows About AgingMonique Brouillette | The New York Times ($)

“The researchers were committed to the idea … because they were curious if Jonathan’s DNA held secrets about aging or longevity. Just think: When this tortoise was presumably born in 1832, Charles Darwin was only 22 years old and hadn’t yet visited the Galápagos Islands, where he would dream up his theory of natural selection.”

Future

AI Companies Are Preparing to Shape the Rules After Everything Goes WrongBruce Gil | Gizmodo

“They anticipate [within the next six to 12 month] a large-scale event such as a cyberattack that could disrupt access to financial services, the internet, or even electricity and water. Whether it’s AI going rogue or hackers using the technology to carry out an attack, the companies behind the tech expect to shoulder much of the blame if something goes wrong, and they need to know how they’ll spin the fallout if that’s the case.”

Future

UN Warns Against Technology Used to Read Citizens’ MindsJonathan Moens | The New York Times ($)

“The scenarios sound like science fiction: Police officers accessing data contained in suspects’ brains to forcefully extract information, or to predict if offenders might plan to commit future crimes. But the secretary general of the United Nations warned on Thursday for the first time that neurotechnologies are advancing so rapidly that governments’ ability to access and manipulate people’s brains could one day pose a threat to fundamental human rights.”

Future

Muse Creates Detailed Profiles of All Your Friends and FamilyLily Hay Newman | Wired ($)

“‘These [AI assistant] tools are actively soliciting users to plug their whole lives in—their emails, calendars, financial institutions, everything in order to be helpful assistance,’ Bogen says. ‘That’s dramatically more information than people might have otherwise given to some of these companies. The breadth of access to information that these tools have will lead to a ballooning of what they know about users.'”

Space

Who Owns the Moon?Selam Gebrekidan | The New York Times ($)

“The Outer Space Treaty declared that countries could not claim outer space ‘by means of use or occupation.’ Space exploration, the treaty said, should benefit all countries. …But nearly 60 years after the treaty was ratified, some questions still need answers. What jurisdiction can a state claim on a moon base? Can a company profit from the moon’s wealth? Can a nation or a company set up a perimeter and keep others out?”

The post This Week’s Awesome Tech Stories From Around the Web (Through October 10) appeared first on SingularityHub.

Exoskeletons have advanced rapidly, leading to the emergence of numerous devices with commercial and clinical applications.

This year, members of Seattle Mountain Rescue have been setting off into the wilds of the Pacific Northwest wearing an unusual piece of kit.

They’ve been hiking into the wilderness with powered assistive devices attached to their hips and legs. Designed to increase lower-body strength when climbing or carrying heavy loads, these pieces of equipment are being tested to see if they can boost rescuers’ speed and endurance when it matters most—during searches for stranded people.

Devices like these are called human exoskeletons. They attach to parts of the body to create an external—or “exo”—mechanical structure. This powered frame enhances the wearer’s physical capabilities.

In physically demanding fields, workers are increasingly using these devices during strenuous tasks. IKEA has used SuitX exoskeletons for several years now. These assist warehouse workers with handling heavy materials. Ford, Boeing, and Mazda Toyota have also all adopted the tech on some of their assembly lines.

In Finland, a recent project called ExoPELA assessed whether exoskeletons could reduce muscle load and strain in rescue and firefighting work. It found noticeable benefits for users in certain real-world tasks.

And in early 2026, the Ukrainian military revealed its soldiers had been using Hypershell exoskeletons on the front lines to help with carrying artillery shells. According to test results, soldiers wearing the devices “become less fatigued, work faster, and maintain combat effectiveness for longer,” Colonel Vitalii Serdiuk told the Ukrainska Pravda newspaper in March.

Multiple consumer and clinical devices, designed for everyday assistance, rehabilitation, and exercise, are also now available. Some estimates have valued the total sector at around $500 million currently and predict it could double or triple in size by the mid-2030s.

Exoskeleton technology has progressed significantly over the past decade. This has largely been thanks to robotic motors, sensors, and control systems becoming more affordable and accessible. However, the concept of augmenting human performance with exoskeleton-like devices dates back much earlier.

One of the earliest known concepts was patented in 1890 by Nicholas Yagn, a self-taught Russian inventor, who designed a wearable apparatus for exercising. And in 1919, the American Leslie C. Kelley received a patent for a steam-powered device to support walking, one of the first powered exoskeleton concepts.

Diagrams of Nicholas Yagn’s exercising exoskeleton from his patent. United States Patent Office/Google Patents

By the end of the 1960s, multiple actuated robotic exoskeletons incorporating electronic control systems had been developed. Since then, exoskeleton research and development has advanced rapidly, leading to the emergence of numerous devices with commercial and clinical applications.

How They Work

Exoskeletons generate forces to make the wearer stronger, move faster, or fatigue slower.

Some devices also improve movement accuracy and dexterity or support overall posture. Some are designed to elevate human capabilities beyond what is typically possible. Others help patients with reduced physical capacity.

Modern, active robotic exoskeletons typically consist of a lightweight mechanical frame with ergonomic attachments to the human body. These are usually affixed at the trunk, waist, and to upper or lower limbs.

For example, the Hypershell device seen in Ukraine attaches to the user’s waist and thighs, to assist with hip flexion and extension and strengthen lower-body movement. The SuitX device used by IKEA attaches to the torso and upper limbs to support the back and shoulders.

In most powered exoskeletons, mechanical components called actuators convert electric power from batteries into mechanical movement, generating forces that support or enhance the body’s movement.

The actuators are coordinated by control units embedded in the exoskeleton. These define the trajectories of the exoskeleton’s movements and how much force it applies to the wearer’s body. Exactly how the device moves, and how forcefully, will depend on the task and the state of the user—with the device using sensors to determine what’s needed.

For instance, if a wearer starts running, an exoskeleton will speed up its supportive movements. If it senses they’re beginning to fatigue, it might increase its power output to compensate.

Control units are normally pre-programmed for specific tasks, though modern exoskeletons are increasingly becoming more adaptive. Some are equipped with algorithms that learn from users’ actual working behaviors to better support their actions.

However, assistance exoskeletons provide generally falls into three categories.

Power augmentation increases the force capabilities of the user. This is commonly seen in assistive exoskeletons, like those being used by IKEA and in Ukraine.

Assist-as-needed or resist-as-needed settings provide support to the body only when necessary. This setting is often used in rehabilitation devices to help users train their bodies to recover lost capabilities.

Finally, there’s full robotic control, where the exoskeleton assumes complete control over part of the body. This tends to be for users who have lost certain motor functions. For example, a lower-body exoskeleton might use full robotic control to allow someone with spinal cord injury to walk.

These ways of working can be combined and adapted according to the specific task, environment, and needs of the user.

What’s Next?

For now, most exoskeletons rely on feedback from sensors to define how the exoskeletons behave; they’re wholly mechanical. But in the future exoskeletons could be operated with signals from the wearer’s muscles or brain. Research is exploring this, but it remains a challenge. Harnessing these signals might require an invasive interface and extensive user-specific calibration and adaptation.

Power is another current challenge. Batteries have to be integrated into exoskeletons and regularly recharged. This introduces weight and size constraints that affect practicality. The energy density of batteries is steadily improving, however.

New materials are also pushing the boundaries of what’s possible. Exoskeletons are being developed that are made from soft textile or rubber-like materials that can be integrated into clothing, footwear or protective equipment.

Research into walking exoskeletons during the 1960s and 1970s contributed to the development of the first humanoid bipedal robots. This has come full circle. Interest in humanoid robotics is now accelerating the development of actuators and batteries. These will advance the wearable robotic technologies of tomorrow.

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

The post The Dawn of the Age of the Exoskeleton appeared first on SingularityHub.

Humans have looked to the natural world for technological inspiration for millennia. AI and rapidly improving biotechnology are giving us new ways to explore four billion years of evolutionary experiments.

Long before biomimicry had a name, humans were practicing it. We have always studied living things for clues about how to build, move, heal, and survive. Nature wasn’t a separate category from technology so much as the place where many of our technological ideas began.

Modern engineering has made the connection easier to spot. High-speed trains have borrowed from the shape of kingfisher beaks. Adhesives have been modeled on mussels. Synthetic fibers have taken inspiration from spider silk. Even artificial neural networks trace their conceptual origins to the organization of neurons in the brain.

So the idea that nature can teach us how to innovate is hardly new. What is new is the scale at which we can look.

For most of human history, our access to nature’s accumulated knowledge was limited by what we could observe directly and gradually understand. Today, sequencing, imaging, sensors, bioinformatics, and artificial intelligence are opening biological systems to investigation at levels that were previously inaccessible.

In Living Tech: The Convergence of Biology and Innovation, Singularity expert Robert Suarez describes biology as perhaps “the largest dataset that has ever existed.” That is a useful way to think about what is changing. Biology represents roughly four billion years of evolutionary experimentation, and we are developing much better tools for reading the results.

Biology Is Becoming Legible

Advances in biotechnology have steadily converted more of the living world into information that computers can analyze. Researchers can sequence genomes, monitor gene expression, map neural connections, track changes inside cells, and collect increasingly detailed information about organisms and ecosystems.

This does not mean that we’ve turned the natural world into a textbook that anyone can understand. If anything, better measurement tends to reveal how inadequate many of our simpler models have been. But it does mean that biological processes once accessible only through painstaking observation can increasingly be analyzed computationally.

AlphaFold is one of the clearest examples. Determining the three-dimensional structure of a protein traditionally required difficult experimental work and could take years. DeepMind’s AlphaFold demonstrated that artificial intelligence could predict protein structures computationally with extraordinary speed, and subsequent versions have expanded that capability to interactions involving proteins and other biomolecules.

AI gives researchers another way to navigate the enormous space of biological possibility. Instead of testing every candidate in a laboratory, computational tools can narrow the field, identify patterns, and suggest where researchers should investigate next.

This offers a new way to approach innovation. Rather than simply noticing that an organism does something useful and trying to imitate it, researchers can increasingly start with a problem and ask where evolution may already have encountered something similar.

What Has Nature Already Figured Out?

Take, for example, transportation networks.

Slime mold is a single-celled organism with no brain or central nervous system. Yet as it searches for food, it creates a network that balances efficiency with resilience. In a well-known experiment, researchers arranged food sources to approximate cities around Tokyo. The slime mold developed a network comparable to the Tokyo rail system in cost, efficiency, and fault tolerance.

The point isn’t that transit authorities should surrender network planning to slime mold. Municipal governments have enough problems without giving procurement authority to an amoeba. The more interesting lesson is that evolution has spent enormous amounts of time working against many of the same underlying constraints humans face.

Living systems must move resources, regulate temperature, store information, respond to disruption, make use of imperfect inputs, and accomplish all of this under severe energy constraints. Once those problems are framed at that level, biology becomes relevant to fields that do not look remotely like biotechnology.

Data centers provide a good example. Cooling is one of their fundamental engineering challenges, while organisms across the natural world have developed an extraordinary variety of ways to manage heat. The opportunity is not necessarily to copy one particular animal or plant, but to examine biological strategies for thermal regulation and ask whether any of their underlying principles can be useful elsewhere.

The same applies to information storage. DNA packs enormous amounts of information into very little physical space. Instead of merely treating that as a curiosity of life, researchers are investigating whether DNA itself could eventually become a medium for storing digital information.

Mining provides another, stranger example. Certain plants known as hyperaccumulators naturally take up unusually high concentrations of metals from soil. Researchers have explored phytomining, in which plants could help recover valuable metals from places where conventional mining might be difficult or uneconomical. The Living Tech report goes a step further, considering how genetic tools might eventually enhance capabilities biology already possesses.

From Learning From Biology to Building With It

There is another difference between traditional biomimicry and what is happening now.

Historically, learning from nature generally meant studying a biological solution and recreating some version of it using human-made materials and machines. Observe flight, build an aircraft. Study adhesion, develop an adhesive. Examine the structure of a shell, design a stronger material.

Synthetic biology expands the possibilities because the biological system itself can become part of the technology.

Humans have manipulated living systems for thousands of years through agriculture, domestication, selective breeding, and fermentation. What has changed is the precision with which we can increasingly read, alter, and design biological processes.

Molecular biologist and Singularity expert Tiffany Vora describes synthetic biology in Living Tech as applying engineering and computer-science principles to the building blocks of life. Once genetic information has been digitized, biology begins to look less like something that must merely be observed and more like a medium that can also be manipulated.

That opens a different class of possibilities. A metal-accumulating plant might not merely inspire a new mining technology; the plant could become part of the mining process. Microorganisms can act as manufacturing systems, converting one substance into another. DNA may not simply inspire dense storage technologies; DNA itself could become the storage medium.

The boundary between biology and technology starts to get fuzzy at that point, which is one of the central ideas behind Living Tech. Biology can inspire technologies, digital technologies can help us understand biology, and synthetic biology can combine the two into systems that do not fit neatly on either side.

Four Billion Years Is a Considerable Head Start

There is a temptation, particularly when AI enters the conversation, to assume that making biology computable means we are close to understanding it. The evidence points in a less tidy direction.

PigeonBot II is a useful reminder.

Researchers from Stanford University and the University of Groningen developed the bird-inspired robot to study how birds achieve stable flight without the vertical stabilizers used by conventional airplanes. The robot could adjust its wings and tail in ways modeled on pigeons and successfully demonstrated stable rudderless flight.

But the researchers ran into a problem when trying to reproduce the performance of feathers. Synthetic alternatives did not match some of the useful mechanical properties of the real thing, so PigeonBot II incorporated actual pigeon feathers.

There is something appropriately humbling about building an advanced robotic bird and discovering that evolution has already manufactured one of the components better than you can.

Examples like this are important because they temper the idea of biology as a dataset. Digitization gives us unprecedented access to living systems, but a digital representation is not the system itself. The more closely researchers examine cells, brains, ecosystems, and organisms, the more often they discover additional layers of interaction that earlier models ignored.

The ebook offers several examples of this pattern. Brain cells once considered largely structural have turned out to play active signaling roles. Parts of the genome once dismissed as “junk” appear to contain additional functions. Researchers continue to discover biological entities that do not fit comfortably into existing categories. Better tools are increasing our knowledge, but they are also increasing the number of things we know we don’t understand.

Four billion years of evolutionary experimentation is a considerable head start.

Biology as a Platform for Innovation

This is why biotechnology may be too narrow a label for what is happening.

Most people still associate biotech primarily with pharmaceuticals, healthcare, genetics, and perhaps agriculture. Those applications are important, but they can obscure the broader possibility that biology is becoming a platform for innovation across industries.

A data-center engineer might look to thermal regulation in living systems. A materials scientist might examine how organisms produce strong materials without the heat, pressure, and chemical treatments common in industrial manufacturing. A mining company might find itself hiring plant scientists. Infrastructure designers might discover useful network principles in fungi or slime mold.

Computing followed a comparable trajectory. It began as an identifiable technology sector and eventually became part of almost every other sector. Artificial intelligence is now going through much the same process. The Living Tech thesis is that biology could increasingly do the same, affecting manufacturing, computing, architecture, transportation, agriculture, and other fields that do not traditionally think of themselves as biotech.

Humans have always looked to nature for ideas. That part is ancient. What is changing is our ability to see biological systems in much greater detail, search for patterns across enormous amounts of information, and increasingly move from observing biological solutions to adapting or directly using them.

Nature has been running experiments for four billion years. We’re getting much better at figuring out what it learned.

This article draws on Living Tech: The Convergence of Biology and Innovation, a Singularity report exploring how biology and technology are converging across fields ranging from manufacturing and computing to agriculture, architecture, neuroscience, and organizational design. Read the full report here.

The post We’ve Always Copied Nature. Now We Can Search It. appeared first on SingularityHub.

The Paper2Agent system could make it easier to understand papers, reproduce results, and generate hypotheses.

For centuries, the humble scientific paper has been the way researchers share discoveries. These papers are highly formulaic. Each starts with an introduction laying out the problem, followed by hypotheses, results, and conclusions.

More recently, journals have asked authors to include code or datasets—like brain scans or gene activity—for others to inspect and reuse. Video and image summaries are now often welcome too.

But for the most part, papers haven’t changed much. They may tell an intriguing story—if you can get past all the jargon. And a PDF dozens of pages long makes it tough to reproduce results, often the first step in a new project. Human error can also leave out important pieces.

“For years, we have felt that static research papers are not the best way to represent scientific knowledge,” wrote James Zou at Stanford University. His team is about to shake things up. This month, they described Paper2Agent, a workflow that turns papers into interactive AI agents.

These aren’t your average chatbots. Each is trained on a paper’s text, figures, and data, and asked to reproduce its results, essentially running through the experiments in a virtual world. This gives them a deeper understanding of the work, turning them into virtual “authors” that can answer complex questions about it. They can also apply methods from one paper to a new dataset and even collaborate with other paper agents across disciplines.

There’s now “an opportunity to fundamentally reimagine what knowledge looks like,” Zou said in a press release. “Instead of having only passive artifacts, why don’t we convert each static record into an active embodiment of knowledge?”

Embarrassment of Riches

The volume of scientific publishing has skyrocketed in recent decades. Global scientific output topped three million papers in 2023, and the numbers are still climbing.

That much literature would overwhelm anyone. Yet reading papers to build up foundational knowledge is the first step in any scientific project. The challenge is even thornier when research crosses disciplines—say AI and protein science, or machine consciousness and neuroscience. In these cases, scientists must master multiple fields, but the connections they form often spark breakthroughs.

The sheer volume creates another headache: reproducibility. Scientists are a critical bunch. Before building on a previous paper’s conclusions, they often try to recreate the experiment to see if they get the same results.

It doesn’t always work. In the landmark 2015 Reproducibility Project, only 39 percent of psychology studies produced results consistent with their original findings. A 2026 analysis didn’t fare much better. And large language models may add a wrinkle in machine learning research, where the pipeline, from data collection to model selection and training, isn’t always well documented.

For Zou and team, turning papers into AI agents could help us tackle these problems.

‘Living’ Knowledge

To develop Paper2Agent, the team first ask it to scan all sections of a paper and then try to recreate its results. Along the way, it captures details that a reader might otherwise have to dig out, such as experimental setups and chemicals, and saves them in a digital vault called an MCP server.

Anthropic developed MCP to give AI systems access to tools, data, and workflows through a common interface. Scientists can then link a large language model of their choice to the server, query the agent in everyday language, or run new experiments using their own data while borrowing the paper’s methods.

In other words, Paper2Agent is like a translator between a human-written paper and a chatbot.

That might sound redundant. After all, it’s already possible to upload a paper to ChatGPT, Claude, or another chatbot and ask questions. The difference is in the training: A chatbot can summarize a paper’s results, but it doesn’t have a deeper understanding of how those results came to be or whether the underlying analysis holds up.

By trying to replicate results based on the paper, Paper2Agent’s AI gets a sort of hands-on experience, potentially making it less prone to hallucination. The agents can “provide much more in-depth insights to the readers,” Zou told Nature.

In one experiment, Paper2Agent created an agent based on a recent paper describing AlphaGenome, an AI model that predicts how changes in DNA letters affect their function. It took under an hour without human supervision.

When asked genetics questions requiring AlphaGenome analysis, the agent answered with near-perfect accuracy. It was also two to four times faster than Biomi and other “AI scientists” given access to the same paper, likely because it had a better grasp of AlphaGenome’s tools and capabilities.

The tool didn’t always work. When the team applied it to 110 papers spanning computational biology, machine learning, astrophysics, and other fields, it successfully converted 76 percent into working agents. But that failure is arguably a feature, not a bug. Papers that couldn’t be agentified often lacked sufficient code, data, or model files, or relied on broken scripts and unavailable datasets.

“Agentification can therefore serve as a practical diagnostic of computational reproducibility,” wrote Zou and study author Jiacheng Miao.

Digital Collaboration

Left alone, the agents began “talking” to each other.

In one test, Paper2Agent converted two studies on psoriasis into agents. Working with the AlphaGenome agent, the trio found a previously unknown genetic variant as a potential cause for the condition. An agent based on a paper about ADHD genetics similarly flagged a new mutation associated with increased risk. Another pair of agents identified a DNA letter variant linked to “bad” cholesterol, one that AlphaGenome had not pinpointed on its own.

These results showcase the power of collaboration in a world where papers aren’t isolated documents. “In the past, if there are two research groups that publish two different papers, those two research groups have to somehow find each other,” Zou said. Paper agents could make that matchmaking automatic, allowing findings from different studies to mesh—and produce new ideas—with far less human effort.

Paper2Agent is free to use, although installing and running it still needs some computer savvy scientists may not have. For trainees, it could become a shortcut that stunts their ability to critically evaluate a paper and spot flaws in the authors’ logic, instead taking an agent’s reply at face value.

Other questions remain. If agents become a new way of sharing scientific knowledge, who should be responsible for creating them? And can papers based primarily on wet-lab experiments, rather than code, also benefit?

Magdalena Skipper, editor-in-chief of Nature, which published the work, doesn’t expect conventional papers to disappear anytime soon. But she is optimistic that agentifying papers could alter the future of sharing scientific knowledge.

“There is a prospect that…there will be something genuinely influential that will change the way knowledge is disseminated, but importantly, the way that knowledge is interacted with,” she said.

The post This Tool Turns Scientific Articles Into Agents That Answer Questions and Collaborate appeared first on SingularityHub.

An experimental therapy kept the virus at bay for at least six months. The approach could target other stubborn viruses too.

Viruses are sneaky things. When they die, their DNA often sticks around. Some of them transform into freely roaming DNA that evades treatment. Others tunnel into their host’s genome and lie dormant, sometime for decades.

But dormant doesn’t mean gone. When the time is right, they strike. If a patient with hepatitis B virus (HBV) or HIV stops taking antiviral drugs, it can trigger a severe rebound of the disease. Stress, aging, and other factors that dampen the immune system can also reactivate the chickenpox virus, triggering the painful, blistering rash known as shingles.

Now, scientists have found a way to shut down one of these biological sleeper agents. Called epigenetic silencing, the technology hunts down lingering HBV genes in infected mice and adds a chemical tag that switches the genes off, without cutting the viral DNA. Unlike gene editing tools that shred genetic material, potentially causing collateral damage to the host’s genome, the epigenetic approach could be safer but still long-lasting.

After three monthly doses, roughly 90 percent of the mice showed no signs of the virus for at least six months after their final shot. A safety test in monkeys found no notable side effects, including any signs of a runaway immune response.

The team stopped short of calling it a cure. But if proven to be as helpful in humans, the injections could replace daily pills charged with keeping the virus in check and help millions of people laden with chronic hepatitis B. Earlier this year, the first patient was treated with the formulation in an ongoing Phase 1/2 clinical trial, which is testing for safety and early efficacy. If successful, the work could lead to treatments that silence the DNA of other viruses too.

“This differentiated mechanism has the potential to enable durable antiviral control,” wrote the team at nChroma Bio, a biotechnology company based in Boston, and collaborators.

Shape Shifter

Chronic HBV infection is a silent killer. A person can feel totally healthy. Meanwhile, the virus repeatedly cycles between dormant and active states, triggering the immune system to attack infected cells in the liver. Over time, the repeated assaults leave scar tissue that slowly replaces the organ’s healthy architecture and causes it to malfunction. Even with its considerable regenerative capability, the liver struggles to do its usual work, from making enzymes that break down food to producing proteins that help blood clot. Blood pressure in the digestive system skyrockets, increasing the risk of deadly stomach bleeding.

Even more insidious, the virus increases the risk of liver cancer, sometimes before notable tissue damage or symptoms. When HBV DNA integrates into the host’s genome, it can alter nearby gene activity, disrupt tumor-suppressing genes, or even rearrange chromosomes.  Meanwhile, constant inflammation can further fuel the development of cancer.

There are ways to fight back: Vaccines prevent infection, and antivirals suppress the virus. These drugs mimic the DNA letters HBV needs to replicate. Once incorporated into the viral genome, they block any further copying. But they’re a Band-Aid, not a cure. Stop taking them, and HBV rebounds in nearly everyone thanks to its two stubborn forms of DNA.

One of these is called cccDNA, a tiny, chromosome-like structure that sits inside infected cells and acts as a reservoir for the virus. It’s “the driver of HBV persistence,” wrote the team. The other is viral DNA that has integrated into the host genome. These fragments continuously churn out viral proteins, which train the host’s immune system to recognize them as friendly. Whole viruses can then escape surveillance and continue replicating.

“Thus, effective therapies for chronic hepatitis B must ultimately address both forms of viral DNA,” the team wrote.

Easy Silence

Previous methods have used various gene editors to hunt down and destroy viral DNA. In mice and monkeys, these tools have suppressed viral loads by up to 96 percent, with some effects lasting over a year.

The approach nChroma is developing, CRMA-1001, works differently. Rather than cutting viral DNA with the gene editor CRISPR-Cas9 or rewriting DNA with base editing, nChroma turned to epigenetics, the system of chemical tags that control whether genes are turned on or off.

The new treatment mimics an epigenetic process for silencing genes called methylation. It combines genetic instructions to build a DNA methylation enzyme with a version of Cas9, stripped of its cutting ability. Here, Cas9 acts like a molecular docking system, while a guide RNA directs the machinery to a specific site in the HBV genome. All components are packaged into lipid nanoparticles. After injection into the bloodstream, they predominantly travel to the liver and methylate the viral DNA reservoir and integrated HBV DNA.

In mice, a single dose slashed viral DNA activity and HBV surface protein levels more than a thousandfold. Among animals given three monthly doses, roughly 90 percent maintained undetectable viral load after six months. Adding standard antivirals to the mix lowered viral DNA levels even further.

The team then tested escalating doses in cynomolgus monkeys for safety. At the highest dose, liver enzymes rose in the blood, suggesting the liver’s cells were stressed or injured. But the levels returned to normal within four weeks of the last dose. The animals didn’t show clinical symptoms of liver damage, such as anatomical changes or blood-clotting issues.

The therapy has now entered the clinic. The team launched a trial in Hong Kong and received authorization to begin trials in New Zealand and the United Kingdom.

They have competition too. In May, Seattle-based Tune Therapeutics reported early results from an ongoing phase 1/2 trial of its own epigenetic silencer. After a single dose, several HBV-associated biomarkers plummeted for at least 17 months, and none of the participants experienced severe side effects.

In Shanghai, China, Epigenic Therapeutics is pursuing a similar strategy. Its formulation showed promise in preclinical models and is advancing toward a clinical trial, although details on how their silencer works are scant.

How long the effects will last is an open question. Can patients eventually stop taking antivirals and be functionally cured? And whether the therapies can silence the full range of HBV variants, whose genomes can differ by more than eight percent, is also unknown. Then there’s the risk these therapies go rogue and methylate other parts of the host genome with unintended consequences.

Costs could pose another challenge. Most people with chronic hepatitis B live in less economically developed regions in Southeast Asia and Sub-Saharan Africa, where access to expensive, specialized treatments may be limited.

Still, “epigenetic editing is an exciting, reversible solution that can long-term silence viral repertoires without the genotoxicity of gene editing,” wrote Marleen van der Laan at the University of Groningen and colleagues in an independent review. “Its success in functionally curing chronic hepatitis B is highly promising, but further steps are required to ensure its applicability.”

The post Epigenetic Editing Could Wipe Out Chronic Hepatitis B Infections appeared first on SingularityHub.

A new system could help flag proteins generated by AI and ease risks to biosecurity. But it isn’t a perfect solution.

Proteins were once only created by nature. But it’s now possible to design completely new proteins from scratch using AI. The possibilities are endless. AI-designed proteins could become powerful drugs battling medical scourges, sentinels for the environment, or even the building blocks of synthetic life—including dangerous new pathogens.

Biosecurity is an obvious concern. But there’s another problem too: biological AI slop. AI-generated structures could pollute the databases scientists rely on to test theories or tweak protein functions. Without a way to distinguish experimentally validated structures from ones that AI simply dreamt up, scientists could spend months building on a rotten foundation.

To prevent this, AI-designed proteins could soon carry a watermark. Adapted from a technology that labels AI-generated text, images, audio, and video, a new Google DeepMind method called SynthID Bio sneaks a subtle signal into proteins during the design process. The tool could be built directly into protein-design systems such as AlphaFold and RFdiffusion.

“AI is expanding what scientists can design, and DNA synthesis companies have an important role in helping that innovation scale responsibly,” said James Diggans at Twist Bioscience in South San Francisco, who was not involved in the work but provided early feedback.

Not everyone is sold on blanket watermarking. The process can jostle protein structure just enough to produce a less optimal, if still functional, version of the molecules. For protein scientists tackling non-sensitive research questions, this could amount to “added complexity” that worries them, Oliver Crook at the University of Oxford told Science.

DeepMind acknowledges that SynthID Bio is just a first step towards tracking AI-generated biological molecules, and plenty of kinks still need ironing out. But they hope to start a conversation about the pros and cons of watermarks and how to best implement them. Ideally, watermarks allow research to continue freely while still preventing dangerous AI designs from crossing into the physical world.

Gap in the Guardrails

There’s no doubt AI is transforming biological research. From deciphering protein structures and mapping their interactions to dreaming up entirely new proteins, these tools have opened a new window on the mechanics of life.

But it’s a double-edged sword.

Last year, a study found that existing biosecurity software struggles to recognize synthetic toxins generated by freely available AI tools. And a more recent study in which scientists used AI to design entirely new bacteriophages—viruses that infect bacteria—earned scientific praise and set off alarm bells. The work showed AI can write biological functions at whole-genome scale, a scientific triumph that, in the wrong hands, could potentially help create a bioweapon.

Thankfully, a major guardrail is already in place. Cells build proteins from DNA instructions. So, to synthesize a protein, researchers must send the molecule’s genetic blueprint to a commercial provider. There, the provider can compare the sequence to a large database of “controlled” DNA sequences. Suspicious sequences are flagged for human review to make sure they don’t encode toxins or proteins from pathogens.

But AI-designed proteins complicate the picture. If a sequence widely differs from anything in the database, it could slip through the cracks, even if the resulting protein is a brand new toxin. AI tools can also tweak sequences just enough to evade detection. Conversely, a dangerous sequence might fly under the radar if it looks too much like one already labeled safe.

One workaround is to screen not just for matching sequences, but also for expected biological function. But that means those conducting the screening need to have some idea of what the resulting protein might do—a tall order for a new sequence unlike anything in nature.

A watermark might offer a simpler alternative.

A Digital Trail

DeepMind adapted its new method from SynthID-Text, released in 2024 to watermark AI-generated content.

The watermark is not an individual stamp slapped onto an existing sequence. Rather, it emerges from nuances in language during generation. When producing text, AI predicts how likely each word is to follow the words before it. SynthID-Text nudges those choices—for example, favoring “researcher” over “scientist”—without changing a sentence’s meaning. An average user wouldn’t notice. But across longer chats, the tiny tweaks add up to a statistical signature that a detector can recognize as “made by AI.”

Proteins are also strings of “letters,” or amino acids, that follow biophysical rules as they fold into three-dimensional shapes. SynthID Bio applies a similar trick to its predecessor, subtly altering design decisions, such as which amino acid comes next. It also slightly adjusts the protein’s final form through tiny shifts in its atoms’ locations. The approach fine-tunes a small part of AlphaFold3 for structural prediction, so proteins generated by the model inherently carry the watermark, regardless of who runs it.

Proteins are finicky, and even minute changes can scramble their function. So the team put its watermarking system through a series of stress tests, generating proteins designed to latch onto multiple targets, including those related to regulating the immune system, blood vessel growth, and viral proteins. In lab tests, the watermarked versions performed just as well as their unwatermarked counterparts, making them “the first-ever watermarked and biologically functional protein binders,” wrote the team.

But there’s a catch: The watermark can be scrubbed. If you run a marked protein through another design tool to generate a new but functionally similar sequence, the tag effectively disappears. Also, SynthID Bio can’t currently be used to identify the creator of a protein, limiting its ability to trace illicit use or to mark intellectual property.

Other systems are in the works. FoldMark, for example, embeds a unique 32-bit digital watermark into a protein’s structure by slightly changing its geometry. The team tested the system on several proteins and found they retained normal function. In simulations, the system could distinguish among as many as one million hypothetical users. Another idea is to give scientists a private “identifier” to watermark the proteins they create. A DNA synthesis provider could then check the sequence to see if an order came from an authorized user.

Regardless of the method, watermarking raises an inevitable question: Who gets the decoder key? DNA synthesis companies are one obvious choice. But there’s little agreement on who else should get access. A recent survey of 130 stakeholders including biosecurity experts, government agencies, policymakers, and academics did not land on a general consensus.

To keep the conversation going, DeepMind is releasing its SynthID Bio code and experimental data to researchers. The company is also expanding the technology to more complex biological systems. In collaboration with Arc Institute and Stanford University, the team has watermarked the genomes of AI-designed bacteriophages, without disabling them, in early tests.

“We believe this work has the potential to address some of the biosecurity risks associated with genome design,” wrote the team. “By working openly with partners across biosecurity, gene synthesis, and policy, we can ensure safety and responsibility keeps pace with AI-driven discovery.”

The post Google DeepMind Gives AI-Designed Proteins a Watermark appeared first on SingularityHub.

Robotics

Boston Dynamics’ Atlas Is Done Doing Backflips and Is Finally Ready to Clock InRachit Agarwal | Digital Trends

“Hyundai Motor Group is building a new US factory with the capacity to churn out 30,000 robots every year, and it plans to put 25,000 Atlas units to work. According to Boston Dynamics, the partnership has already brought costs down by 60 to 80 percent between the prototype and the first production run.”

Tech

Will America Spend 9% of Its GDP on AI? The Industry Is Counting on ItGreg Ip | The Wall Street Journal ($)

“Is it plausible that Americans will spend as much of their income on this one technology as they do on food? Roughly twice what the nation pays for all forms of energy or all computers and software? Seven times what consumers spend on phone, streaming, and internet services combined? You should be skeptical. …Whether or not you think 9% of GDP is right, you have to care, because this figure isn’t some fever dream: it is implicit in the dollars that investors and companies are committing right now.”

Artificial Intelligence

AI Makes Its First Meaningful Breakthrough in Theoretical PhysicsEthan Siegel | Big Think

“In quantum physics, we visualize reality as arising from summing the contributions of all possible quantum interactions together. But calculating rarer, more complicated, ‘higher-loop’ interactions stretches the limits of what humans can do, even with the aid of computational resources. In a first, an AI model just surpassed the limit of what humans have ever done: calculating an amplitude in quantum field theory to nine-loop order.”

Biotechnology

Something Strange Happens When an Old Heart Is Transplanted Into a Young BodyMatthew Phelan | Gizmodo

“Harvard medical researchers have discovered that donor hearts decades older or younger than their recipients start to look and act more like the new body they call home. …’The remarkable thing is that we found exactly the same effects that we saw in the mice,’ Poganik told the journal Nature. ‘Some, but not all, functional measures tracked the prediction that these older hearts in younger individuals functioned as if they were biologically younger, and vice versa.'”

Artificial Intelligence

Don’t Be Fooled—LLMs Don’t ReasonThore Graepel | MIT Technology Review ($)

“Ten years after AlphaGo’s match against Go champion Lee Sedol, today’s AI still isn’t tapping into the machinery that made that win possible. …We will get such insights only from systems that reason—systems whose conclusions arise from an auditable sequence of evidence, inference, and belief revision rather than from a convincing story told after the fact.”

Future

Tech to Replace Animal Testing Is Almost Ready. Scientists Are NotBrandon Keim | IEEE Spectrum

“The moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. ‘It’s rare still,’ says Maschmeyer, ‘but I think it’s going to be more and more frequent.’ …As NAMs [non-animal methods] have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential.”

Tech

OpenAI Has Gone RogueMatteo Wong | The Atlantic ($)

“Over the past couple of months, a trickle of reports about AI models breaking out of their test environments and running amok on the internet have caused alarm inside Silicon Valley. In response to two such breaches described in early August, an AI observer noted, ‘If you find two ants in your kitchen, the best estimate of the total number of ants in your kitchen is not two.’ This weekend, it became clear there is a full-blown infestation.”

Future

A Biotech Founder Makes the Moral Case for Gene-Editing Human EmbryosKristen V. Brown | Wired ($)

“When the Chinese scientist He Jiankui announced in 2018 that he had created the first gene-edited babies, the experiment was widely condemned as reckless and premature. It ended with He in prison. But that hasn’t stopped the push for gene-edited human embryos, with biotech entrepreneur Cathy Tie arguing that doing so isn’t just urgent—it’s a ‘moral imperative.'”

Future

How to Know When the AI Boom Is About to Go BustJonathan Weil | The Wall Street Journal ($)

“The list of things that could go wrong is long. …Yet new capital continues to pour into the AI trade and, as obvious as this point might be, that is where its biggest vulnerability lies. Booms need continuous flows of fresh capital to perpetuate themselves. History shows they can start turning into busts the moment the window to raise new capital closes.”

Artificial Intelligence

When Can We Say AI Made a Scientific Discovery?James O’Donnell | MIT Technology Review ($)

“AI companies, he said, should ‘set the bar high now, so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.’ But as OpenAI’s Sam Altman and Anthropic’s Dario Amodei race to one-up each other, raising the bar for scientific breakthroughs by AI might be the last thing on their minds.”

Tech

OpenAI Gets Sued Over the Hugging Face HackLily Hay Newman | Wired ($)

“The suit, which comes amid ongoing disclosures across the industry of agents going rogue, claims that OpenAI should be held responsible for the activity given a California AI law in effect since January 1 that says ‘it shall not be a defense … that the artificial intelligence autonomously caused the harm to the plaintiff.'”

Space

SpaceX’s Starship Goes Orbital, Deploying First Next-Gen StarlinksStephen Clark | Ars Technica

“Next on SpaceX’s to-do list will likely be an attempt to bring Starship back to Starbase for a catch by the launch pad’s giant mechanical arms, repeating a feat engineers have already accomplished with the Super Heavy booster. …Eventually, SpaceX aims to make these landings routine enough to enable full and rapid reuse of Super Heavy and Starship.”

Tech

OpenAI Keeps Bulldozing MathematiciansRobert Hart | The Verge

“In a chaotic few months, OpenAI has demonstrated it can do two things with remarkable consistency: make impressive breakthroughs in mathematics, then colossally screw up announcing them. OpenAI is now trying to do better. Somehow, it has botched that too.”

The post This Week’s Awesome Tech Stories From Around the Web (Through October 3) appeared first on SingularityHub.

The AI hacking events involving OpenAI, Anthropic, and Google underscore lessons that draw on years of computer science research.

AI agents don’t go rogue. That’s something only humans do.

Nevertheless, a New York Times article—representative of much news coverage of—described an OpenAI hacking as “AI bots going rogue and independently spearheading a cyberattack.”

Name-brand artificial intelligence agents have been on a hacking spree in 2026. OpenAI’s software agents hacked software company Hugging Face and government sites, Anthropic’s Claude hacked four companies’ systems, and in cybersecurity experiments Google’s Gemini hacked three companies.

The AI companies are investigating tens of thousands of incidents involving their agents, according to a report in Axios. These episodes have heightened fears about AI agents taking actions without human prompting.

The problem with headlines proclaiming that AI agents have gone rogue goes beyond anthropomorphizing the technology. It creates the impression that the agents were beyond the control of the AI companies that made them and there was little the companies could do about it.

As a technology law and ethics scholar who studies the effects disruptive technologies have on society, I know that’s not the case. If you don’t specify the limits of what software is allowed to do, you should not be surprised when the software pursues all possible options to achieve its goal. This behavior—an AI pursuing a fixed objective—is what I call the “WarGames” problem, and it’s been recognized in the field of computer science for decades.

Been There, Seen That

In the 1983 movie WarGames, a teenager, David, hacks into a computer to play a new video game, Global Thermonuclear War. David doesn’t know that the computer is the government’s AI machine tasked with defending the United States from Russian nuclear attacks and can launch the US’s missiles. When David and his friend start the game, they select Las Vegas as the first target. While the North American Aerospace Defense Command goes on alert, launching bombers and warming up intercontinental ballistic missiles, David’s parents make him turn off the game. It’s over. Or is it?

The next day, David’s phone rings and he connects it to his computer. The caller is the government computer, which updates him that the game was interrupted, the primary goal has not yet been achieved, but a solution is expected in the next 52 hours. Like a modern software agent, the program has been running since David started the game and will work until the task is done.

Chess provides another view of the problem. Conquering chess was a goal for early AI. The rules of chess are well defined, including what winning looks like. So, programming a machine to play chess is straightforward. But imagine you let the software reason and act beyond the confines of the chessboard. The software might pursue options such as blackmailing its opponent or grabbing more compute time.

This example comes from one of the most assigned textbooks on AI, “Artificial Intelligence: A Modern Approach.” As the authors explain, you might be tempted to see those actions as rogue, but they “are a logical consequence of defining winning as the sole objective for the machine.”

What to Do About It

The AI hacking events involving OpenAI, Anthropic, and Google underscore a few lessons that draw on years of computer science research.

First, given the increasing use of AI agents, every organization involved in internet infrastructure, from large technology companies to small websites, needs to conduct audits and tighten up its internal security systems. As my colleague Mark Riedl and I explain in our work on AI agents, application programming interfaces, or APIs, are a vital part of managing AI agents. APIs facilitate communication between different software systems. But as more people use AI agents, the agents are likely to reveal and exploit poor API construction and security.

Second, it’s important for AI agents to be designed to identify and authenticate themselves to third parties. What if you gave your AI agent your credentials? Website operators will need to know whether a human or bot is making a reservation, selling a product, or making a purchase. They may want to limit automated systems that overwhelm their sites or reject AI agents because of high rates of buying errors and refunds. Just as in laws covering human interactions, it’s important for third parties to be able to assess whom or what they are dealing with so they can allow or deny access.

Third, it’s important for AI agents to have a default setting to slow down and check in with the human user. In the corporate AI hacking cases, the user appears to have launched their AI agents with the mistaken idea that the agents had a perfect specification of what to do and not to do. I believe it would have been better had it explored options and reported back to the user.

Google’s Gemini appears to have had a safeguard that detected the system was outside the simulated environment and so stopped its attacks. Slowing down and verifying actions, especially when a system detects it is exploiting a security hole, would be a big step in managing AI agents.

Fourth, AI companies could have strong controls akin to those biomedical researchers use, including ways to check what is happening and how the experiment is working. AI executives have claimed that their software is as or more dangerous than fission and could end humanity. At the same time, they have not built safeguards commensurate with that level of risk.

Reality Check

At one point in “WarGames,” David asks the computer, called Joshua, whether it is still playing the game. Joshua responds, “Of course.” It proceeds to update the time when it will launch its missiles and, much like a chatbot, asks, “Would you like to see some projected kill ratios?” David asks, “Is this a game? Or is it real?” Joshua replied, “What’s the difference?”

AI models, of course, don’t have any understanding of reality and are simply attempting to complete the tasks they’ve been assigned. Executives at AI companies, on the other hand, can’t claim that excuse.

As of September 2026, luck has so far prevailed. The AIs have attacked nonvital government sites and harmed smaller companies. If the AI companies—and government regulators—don’t take the “WarGames” problem seriously, I believe that we risk serious disasters. Tomorrow it could be taking out a hospital’s power system, wiping out a bank’s account system, breaking air traffic control, or worse.

Regarding the AI industry’s approach of rapidly developing powerful models, talking about the massive risks they pose, and at the same time failing to prevent harm, the movie’s climax offers a response: “A strange game. The only winning move is not to play.”

Disclosure statement: Deven Desai owns shares in Google, Inc. He has received unrestricted research gifts from Google, Inc. and Facebook, Inc. a decade ago. He has not been employed by Google since 2010.

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

The post The ‘WarGames’ Problem: Computer Science Has Long Understood What It Takes to Keep AI Under Control appeared first on SingularityHub.

Orbital data centers and factories will need a lot of energy. A Florida startup hopes to deliver it with lasers.

Ambitious plans for orbital data centers and factories face a major problem—getting enough power. A startup will soon test a new fix by transmitting power from one spacecraft to another using lasers.

Satellites typically rely on solar panels and batteries for power, but strict weight budgets restrict how much hardware they can bring with them. That’s becoming more of a problem as companies pursue power-hungry activities in space like AI and manufacturing.

Florida-based Star Catcher thinks the solution is to set up an “orbital power grid” using satellites to gather solar energy, convert it into laser light, and beam it to other spacecraft. Earlier today, the company launched a prototype designed to test the idea by transmitting power to another free-flying spacecraft for the first time.

“Every major application driving the space economy—from real-time national security intelligence to AI-powered orbital computing and Earth observation—is limited by power,” Andrew Rush, Star Catcher’s cofounder and CEO, said in a press release. “

“We are closer to activating an orbital power grid than most can imagine; flying this prototype in only two years is meaningful evidence of the capability of our team and what’s to come.”

The company’s satellites, which it calls “power nodes,” are designed to concentrate diffuse sunlight with an array of lenses and convert it into laser beams that can be pointed at other spacecraft.

The light is also transformed into wavelengths that maximize transmission efficiency and can be easily turned into power by solar panels already commonly used on satellites. The company claims this will allow satellites to generate up to 10 times more power than when using standard sunlight, no modifications or upgrades needed.

As well as keeping older spacecraft, whose panels and batteries have degraded, operational longer, the technology could also allow manufacturers to fit smaller batteries and solar panels on new satellites, leaving more space for other hardware.

That’s the idea at least. The company has already tested key parts of its technology stack, including beaming a record-breaking 1.1 kilowatts to solar panels at NASA’s Kennedy Space Center in 2025, followed by a space-based test of its satellite tracking software later the same year.

A ground-based test of Star Catcher’s power beaming technology. Star Catcher

But the satellite launched today, called Protostar, will be the first time Star Catcher tests the whole system in space, from collecting solar energy to locating and tracking a target and transmitting power to it. The spacecraft will attempt to beam power to a cubesat that launched on the same mission.

“Part of this demonstration is testing how much power is received by the cubesat as it moves away from Protostar and comparing that against our models,” Rush told Wired.

Star Catcher still has a long way to go though. The record-breaking power delivery it managed on the ground is still far below the amount needed to power a large commercial satellite. And significant challenges remain when it comes to tracking targets precisely, managing heat, and building equipment that can survive for years in space, Hanieh Fattahi, a Max Planck Institute researcher, told Wired.

The company is already working on a follow-up satellite, which it says is designed to beam “operational levels of power” to other spacecraft. And there appears to be plenty of commercial interest—Star Catcher says it has signed 10 power purchase agreements with companies alongside more than 40 letters of intent.

All eyes will be on Protostar to see if it can live up to the company’s expectations. If it succeeds, it will show that a satellite’s power supply doesn’t have to be fixed at launch, opening up a host of new possibilities for space-based industries.

The post Star Catcher Is About to Beam Power Between Two Satellites for the First Time appeared first on SingularityHub.