Transhumanismus

ARTIFICIAL INTELLIGENCE

Sam Altman Says Helpful Agents Are Poised to Become AI’s Killer Function
James O’Donnell | MIT Technology Review
“Altman, who was visiting Cambridge for a series of events hosted by Harvard and the venture capital firm Xfund, described the killer app for AI as a ‘super-competent colleague that knows absolutely everything about my whole life, every email, every conversation I’ve ever had, but doesn’t feel like an extension.’ It could tackle some tasks instantly, he said, and for more complex ones it could go off and make an attempt, but come back with questions for you if it needs to.”archive page

COMPUTING

Expect a Wave of Wafer-Scale Computers
Samuel K. Moore | IEEE Spectrum
“At TSMC’s North American Technology Symposium on Wednesday, the company detailed both its semiconductor technology and chip-packaging technology road maps. While the former is key to keeping the traditional part of Moore’s Law going, the latter could accelerate a trend toward processors made from more and more silicon, leading quickly to systems the size of a full silicon wafer. …In 2027, you will get a full-wafer integration that delivers 40 times as much compute power, more than 40 reticles’ worth of silicon, and room for more than 60 high-bandwidth memory chips, TSMC predicts.”

FUTURE

Nick Bostrom Made the World Fear AI. Now He Asks: What if It Fixes Everything?
Will Knight | Wired
“With the publication of his last book, Superintelligence: Paths, Dangers, Strategies, in 2014, Bostrom drew public attention to what was then a fringe idea—that AI would advance to a point where it might turn against and delete humanity. …Bostrom’s new book takes a very different tack. Rather than play the doomy hits, Deep Utopia: Life and Meaning in a Solved World, considers a future in which humanity has successfully developed superintelligent machines but averted disaster.”

TECH

AI Start-Ups Face a Rough Financial Reality Check
Cade Metz, Karen Weise, and Tripp Mickle | The New York Times
“The AI revolution, it is becoming clear in Silicon Valley, is going to come with a very big price tag. And the tech companies that have bet their futures on it are scrambling to figure out how to close the gap between those expenses and the profits they hope to make somewhere down the line.”

ROBOTICS

Every Tech Company Wants to Be Like Boston Dynamics
Jacob Stern | The Atlantic
“Clips of robots running faster than Usain Bolt and dancing in sync, among many others, have helped [Boston Dynamics] reach true influencer status. Its videos have now been viewed more than 800 million times, far more than those of much bigger tech companies, such as Tesla and OpenAI. The creator of Black Mirror even admitted that an episode in which killer robot dogs chase a band of survivors across an apocalyptic wasteland was directly inspired by Boston Dynamics’ videos.”

ETHICS

ChatGPT Shows Better Moral Judgment Than a College Undergrad
Kyle Orland | Ars Technica
“In ‘Attributions toward artificial agents in a modified Moral Turing Test’…[Georgia State University] researchers found that morality judgments given by ChatGPT4 were ‘perceived as superior in quality to humans’ along a variety of dimensions like virtuosity and intelligence. But before you start to worry that philosophy professors will soon be replaced by hyper-moral AIs, there are some important caveats to consider.”

SPACE

New Space Company Seeks to Solve Orbital Mobility With High Delta-V Spacecraft
Eric Berger | Ars Technica
“[Portal Space Systems founder, Jeff Thornburg] envisions a fleet of refuelable Supernova vehicles at medium-Earth and geostationary orbit capable of swooping down to various orbits and providing services such as propellant delivery, mobility, and observation for commercial and military satellites. His vision is to provide real-time, responsive capability for existing satellites. If one needs to make an emergency maneuver, a Supernova vehicle could be there within a couple of hours. ‘If we’re going to have a true space economy, that means logistics and supply services,’ he said.”

AUTOMATION

Google’s Waymo Is Expanding Its Self-Driving ‘Robotaxi’ Testing
William Gavin | Quartz
“Waymo plans to soon start testing fully autonomous rides across California’s San Francisco Peninsula, despite criticism and concerns from residents and city officials. In the coming weeks, Waymo employees will begin testing rides without a human driver on city streets north of San Mateo, the company said Friday.”

VIRTUAL REALITY

Ukraine Unveils AI-Generated Foreign Ministry Spokesperson
Agence France-Presse | The Guardian
“Dressed in a dark suit, the spokesperson introduced herself as Victoria Shi, a ‘digital person,’ in a presentation posted on social media. The figure gesticulates with her hands and moves her head as she speaks. The foreign ministry’s press service said that the statements given by Shi would not be generated by AI but ‘written and verified by real people.'”

Image Credit: Drew Walker / Unsplash

Getting microbes to eat plastic is a frequently touted solution to our growing waste problem, but making the approach practical is tricky. A new technique that impregnates plastic with the spores of plastic-eating bacteria could make the idea a reality.

The impact of plastic waste on the environment and our health has gained increasing attention in recent years. The latest round of UN talks aiming for a global treaty to end plastic pollution just concluded in Ottawa, Canada earlier this week, though considerable disagreements remain.

Recycling will inevitably be a crucial ingredient in any plan to deal with the problem. But a 2022 report from the Organization for Economic Cooperation and Development found only 9 percent of plastic waste ever gets recycled. That’s partly due to the fact that existing recycling approaches are energy intensive and time consuming.

This has spurred a search for new approaches, and one of the most promising is the use of bacteria to break down plastics, either by rendering them harmless or using them to produce building blocks that can be repurposed into other valuable materials and chemicals. The main problem with the approach is making sure plastic waste ends up in the same place as these plastic-loving bacteria.

Now, researchers have come up with an ingenious solution: embed microbes in plastic during the manufacturing process. Not only did the approach result in 93 percent of the plastic biodegrading within five months, but it even increased the strength and stretchability of the material.

“What’s remarkable is that our material breaks down even without the presence of additional microbes,” project co-leader Jon Pokorski from the University of California San Diego said in a press release.

“Chances are, most of these plastics will likely not end up in microbially rich composting facilities. So this ability to self-degrade in a microbe-free environment makes our technology more versatile.”

The main challenge when it came to incorporating bacteria into plastics was making sure they survived the high temperatures involved in manufacturing the material. The researchers worked with a soft plastic called thermoplastic polyurethane (TPU), which is used in footwear, cushions, and memory foam. TPU is manufactured by melting pellets of the material at around 275 degrees Fahrenheit and then extruding it into the desired shape.

Given the need to survive these high temperatures, the researchers selected a plastic-eating bacteria called Bacillus subtilis, which can form spores allowing it to survive harsh conditions. Even then, they discovered more than 90 percent of the bacteria were killed in under a minute at those temperatures.

So, the team used a technique called adaptive laboratory evolution to create a more heat-tolerant strain of the bacteria. They dunked the spores in boiling water for increasing lengths of time, collecting the survivors, growing the population back up, and then repeating the process. Over time, this selected for mutations that conferred greater heat tolerance, until the researchers were left with a strain that was able to withstand the manufacturing process.

When they incorporated the spores into the plastic, they were surprised to find the bacteria actually improved the mechanical properties of the material. In essence, the spores acted like steel rebar in concrete, making it harder to break and increasing its stretchability.

To test whether the impregnated spores could help the plastic biodegrade, the researchers took small strips of the plastic and put them in sterilized compost. After five months, they found the strips had lost 93 percent of their mass compared to 44 percent for TPU without spores, which suggests the spores were reactivated by nutrients in the compost and helped degrade the plastic substantially faster.

It’s unclear if the approach would work with other plastics, though the researchers say they plan to find out. There is also a danger the spores could reactivate before the plastic is disposed of, which could shorten the life of any products made with it. Perhaps most crucially, plastics researcher Steve Fletcher from the University of Portsmouth in the UK told the BBC that this kind of technology could distract from efforts to limit plastic waste.

“Care must be taken with potential solutions of this sort, which could give the impression that we should worry less about plastic pollution because any plastic leaking into the environment will quickly, and ideally safely, degrade,” he said. “For the vast majority of plastics, this is not the case.”

Given the scale of the plastic pollution problem today though, any attempt to mitigate the harm should be welcomed. While it’s early days, the prospect of making plastic that can biodegrade itself could go a long way towards tackling the problem.

Image Credit: David Baillot/UC San Diego Jacobs School of Engineering

To anyone living in a city where autonomous vehicles operate, it would seem they need a lot of practice. Robotaxis travel millions of miles a year on public roads in an effort to gather data from sensors—including cameras, radar, and lidar—to train the neural networks that operate them.

In recent years, due to a striking improvement in the fidelity and realism of computer graphics technology, simulation is increasingly being used to accelerate the development of these algorithms. Waymo, for example, says its autonomous vehicles have already driven some 20 billion miles in simulation. In fact, all kinds of machines, from industrial robots to drones, are gathering a growing amount of their training data and practice hours inside virtual worlds.

According to Gautham Sholingar, a senior manager at Nvidia focused on autonomous vehicle simulation, one key benefit is accounting for obscure scenarios for which it would be nearly impossible to gather training data in the real world.

“Without simulation, there are some scenarios that are just hard to account for. There will always be edge cases which are difficult to collect data for, either because they are dangerous and involve pedestrians or things that are challenging to measure accurately like the velocity of faraway objects. That’s where simulation really shines,” he told me in an interview for Singularity Hub.

While it isn’t ethical to have someone run unexpectedly into a street to train AI to handle such a situation, it’s significantly less problematic for an animated character inside a virtual world.

Industrial use of simulation has been around for decades, something Sholingar pointed out, but a convergence of improvements in computing power, the ability to model complex physics, and the development of the GPUs powering today’s graphics indicate we may be witnessing a turning point in the use of simulated worlds for AI training.

Graphics quality matters because of the way AI “sees” the world.

When a neural network processes image data, it’s converting each pixel’s color into a corresponding number. For black and white images, the number ranges from 0, which indicates a fully black pixel, up to 255, which is fully white, with numbers in between representing some variation of grey. For color images, the widely used RGB (red, green, blue) model can correspond to over 16 million possible colors. So as graphics rendering technology becomes ever more photorealistic, the distinction between pixels captured by real-world cameras and ones rendered in a game engine is falling away.

Simulation is also a powerful tool because it’s increasingly able to generate synthetic data for sensors beyond just cameras. While high-quality graphics are both appealing and familiar to human eyes, which is useful in training camera sensors, rendering engines are also able to generate radar and lidar data as well. Combining these synthetic datasets inside a simulation allows the algorithm to train using all the various types of sensors commonly used by AVs.

Due to their expertise in producing the GPUs needed to generate high-quality graphics, Nvidia have positioned themselves as leaders in the space. In 2021, the company launched Omniverse, a simulation platform capable of rendering high-quality synthetic sensor data and modeling real-world physics relevant to a variety of industries. Now, developers are using Omniverse to generate sensor data to train autonomous vehicles and other robotic systems.

In our discussion, Sholingar described some specific ways these types of simulations may be useful in accelerating development. The first involves the fact that with a bit of retraining, perception algorithms developed for one type of vehicle can be re-used for other types as well. However, because the new vehicle has a different sensor configuration, the algorithm will be seeing the world from a new point of view, which can reduce its performance.

“Let’s say you developed your AV on a sedan, and you need to go to an SUV. Well, to train it then someone must change all the sensors and remount them on an SUV. That process takes time, and it can be expensive. Synthetic data can help accelerate that kind of development,” Sholingar said.

Another area involves training algorithms to accurately detect faraway objects, especially in highway scenarios at high speeds. Since objects over 200 meters away often appear as just a few pixels and can be difficult for humans to label, there isn’t typically enough training data for them.

“For the far ranges, where it’s hard to annotate the data accurately, our goal was to augment those parts of the dataset,” Sholingar said. “In our experiment, using our simulation tools, we added more synthetic data and bounding boxes for cars at 300 meters and ran experiments to evaluate whether this improves our algorithm’s performance.”

According to Sholingar, these efforts allowed their algorithm to detect objects more accurately beyond 200 meters, something only made possible by their use of synthetic data.

While many of these developments are due to better visual fidelity and photorealism, Sholingar also stressed this is only one aspect of what makes capable real-world simulations.

“There is a tendency to get caught up in how beautiful the simulation looks since we see these visuals, and it’s very pleasing. What really matters is how the AI algorithms perceive these pixels. But beyond the appearance, there are at least two other major aspects which are crucial to mimicking reality in a simulation.”

First, engineers need to ensure there is enough representative content in the simulation. This is important because an AI must be able to detect a diversity of objects in the real world, including pedestrians with different colored clothes or cars with unusual shapes, like roof racks with bicycles or surfboards.

Second, simulations have to depict a wide range of pedestrian and vehicle behavior. Machine learning algorithms need to know how to handle scenarios where a pedestrian stops to look at their phone or pauses unexpectedly when crossing a street. Other vehicles can behave in unexpected ways too, like cutting in close or pausing to wave an oncoming vehicle forward.

“When we say realism in the context of simulation, it often ends up being associated only with the visual appearance part of it, but I usually try to look at all three of these aspects. If you can accurately represent the content, behavior, and appearance, then you can start moving in the direction of being realistic,” he said.

It also became clear in our conversation that while simulation will be an increasingly valuable tool for generating synthetic data, it isn’t going to replace real-world data collection and testing.

“We should think of simulation as an accelerator to what we do in the real world. It can save time and money and help us with a diversity of edge-case scenarios, but ultimately it is a tool to augment datasets collected from real-world data collection,” he said.

Beyond Omniverse, the wider industry of helping “things that move” develop autonomy is undergoing a shift toward simulation. Tesla announced they’re using similar technology to develop automation in Unreal Engine, while Canadian startup, Waabi, is taking a simulation-first approach to training their self-driving software. Microsoft, meanwhile, has experimented with a similar tool to train autonomous drones, although the project was recently discontinued.

While training and testing in the real world will remain a crucial part of developing autonomous systems, the continued improvement of physics and graphics engine technology means that virtual worlds may offer a low-stakes sandbox for machine learning algorithms to mature into functional tools that can power our autonomous future.

Image Credit: Nvidia

Imagine the tap of a card that bought you a cup of coffee this morning also let a hacker halfway across the world access your bank account and buy themselves whatever they liked. Now imagine it wasn’t a one-off glitch, but it happened all the time: Imagine the locks that secure our electronic data suddenly stopped working.

This is not a science fiction scenario. It may well become a reality when sufficiently powerful quantum computers come online. These devices will use the strange properties of the quantum world to untangle secrets that would take ordinary computers more than a lifetime to decipher.

We don’t know when this will happen. However, many people and organizations are already concerned about so-called “harvest now, decrypt later” attacks, in which cybercriminals or other adversaries steal encrypted data now and store it away for the day when they can decrypt it with a quantum computer.

As the advent of quantum computers grows closer, cryptographers are trying to devise new mathematical schemes to secure data against their hypothetical attacks. The mathematics involved is highly complex—but the survival of our digital world may depend on it.

‘Quantum-Proof’ Encryption

The task of cracking much current online security boils down to the mathematical problem of finding two numbers that, when multiplied together, produce a third number. You can think of this third number as a key that unlocks the secret information. As this number gets bigger, the amount of time it takes an ordinary computer to solve the problem becomes longer than our lifetimes.

Future quantum computers, however, should be able to crack these codes much more quickly. So the race is on to find new encryption algorithms that can stand up to a quantum attack.

The US National Institute of Standards and Technology has been calling for proposed “quantum-proof” encryption algorithms for years, but so far few have withstood scrutiny. (One proposed algorithm, called Supersingular Isogeny Key Encapsulation, was dramatically broken in 2022 with the aid of Australian mathematical software called Magma, developed at the University of Sydney.)

The race has been heating up this year. In February, Apple updated the security system for the iMessage platform to protect data that may be harvested for a post-quantum future.

Two weeks ago, scientists in China announced they had installed a new “encryption shield” to protect the Origin Wukong quantum computer from quantum attacks.

Around the same time, cryptographer Yilei Chen announced he had found a way quantum computers could attack an important class of algorithms based on the mathematics of lattices, which were considered some of the hardest to break. Lattice-based methods are part of Apple’s new iMessage security, as well as two of the three frontrunners for a standard post-quantum encryption algorithm.

What Is a Lattice-Based Algorithm?

A lattice is an arrangement of points in a repeating structure, like the corners of tiles in a bathroom or the atoms in a diamond crystal. The tiles are two dimensional and the atoms in diamond are three dimensional, but mathematically we can make lattices with many more dimensions.

Most lattice-based cryptography is based on a seemingly simple question: If you hide a secret point in such a lattice, how long will it take someone else to find the secret location starting from some other point? This game of hide and seek can underpin many ways to make data more secure.

A variant of the lattice problem called “learning with errors” is considered to be too hard to break even on a quantum computer. As the size of the lattice grows, the amount of time it takes to solve is believed to increase exponentially, even for a quantum computer.

The lattice problem—like the problem of finding the factors of a large number on which so much current encryption depends—is closely related to a deep open problem in mathematics called the “hidden subgroup problem.”

Yilei Chen’s approach suggested quantum computers may be able to solve lattice-based problems more quickly under certain conditions. Experts scrambled to check his results—and rapidly found an error. After the error was discovered, Chen published an updated version of his paper describing the flaw.

Despite this discovery, Chen’s paper has made many cryptographers less confident in the security of lattice-based methods. Some are still assessing whether Chen’s ideas can be extended to new pathways for attacking these methods.

More Mathematics Required

Chen’s paper set off a storm in the small community of cryptographers who are equipped to understand it. However, it received almost no attention in the wider world—perhaps because so few people understand this kind of work or its implications.

Last year, when the Australian government published a national quantum strategy to make the country “a leader of the global quantum industry” where “quantum technologies are integral to a prosperous, fair and inclusive Australia,” there was an important omission: It didn’t mention mathematics at all.

Australia does have many leading experts in quantum computing and quantum information science. However, making the most of quantum computers—and defending against them—will require deep mathematical training to produce new knowledge and research.

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

Image Credit: ZENG YILI / Unsplash

Blood transfusions save lives. In the US alone, people receive around 10 million units each year. But blood banks are always short in supply—especially when it comes to the “universal donor” type O.

Surprisingly, the gut microbiome may hold a solution for boosting universal blood supplies by chemically converting other blood types into the universal O.

Infusing the wrong blood type—say, type A to type B—triggers deadly immune reactions. Type O blood, however, is compatible with nearly everyone. It’s in especially high demand following hurricanes, earthquakes, wildfires, and other crises because doctors have to rapidly treat as many people as possible.

Sometimes, blood banks have an imbalance of different blood types—for example, too much type A, not enough universal O. This week, a team from Denmark and Sweden discovered a cocktail of enzymes that readily converts type A and type B blood into the universal donor. Found in gut bacteria, the enzymes chew up an immune-stimulating sugar molecule dotted on the surfaces of type A and B blood cells, removing their tendency to spark an immune response.

Compared to previous attempts, the blend of enzymes converted A and B blood types to type O blood with “remarkably high efficiencies,” the authors wrote.

Wardrobe Change

Blood types can be characterized in multiple ways, but roughly speaking, the types come in four main forms: A, B, AB, and O.

These types are distinguished by what kinds of sugar molecules—called antigens—cover the surfaces of red blood cells. Antigens can trigger immune rejection if mismatched. Type A blood has A antigens; type B has B antigens; type AB has both. Type O has neither.

This is why type O blood can be used for most people. It doesn’t normally trigger an immune response and is highly coveted during emergencies when it’s difficult to determine a person’s blood type. One obvious way to boost type O stock is to recruit more donors, but that’s not always possible. As a workaround, scientists have tried to artificially produce type O blood using stem cell technology. While successful in the lab, it’s expensive and hard to scale up for real-world demands.

An alternative is removing the A and B antigens from donated blood. First proposed in the 1980s, this approach uses enzymes to break down the immune-stimulating sugar molecules. Like licking an ice cream cone, as the antigens gradually melt away, the blood cells are stripped of their A or B identity, eventually transforming into the universal O blood type.

The technology sounds high-tech, but breaking down sugars is something our bodies naturally do every day, thanks to microbes in the gut that happily digest our food. This got scientists wondering: Can we hunt down enzymes in the digestive track to convert blood types?

Over a half decade ago, a team from the University of British Columbia made headlines by using bacterial enzymes found in the gut microbiome to transform type A blood to type O. Some gut bugs eat away at mucus—a slimy substance made of sugary molecules covering the gut. These mucus linings are molecularly similar to the antigens on red blood cells.

So, digestive enzymes from gut microbes could potentially chomp away A and B antigens.

In one test, the team took samples of human poop (yup), which carry enzymes from the gut microbiome and looked for DNA that could break down red blood cell sugar chains.

They eventually discovered two enzymes from a single bacterial strain. Tested in human blood, the duo readily stripped away type A antigens, converting it into universal type O.

The study was a proof of concept for transforming one blood type into another, with potentially real-world implications. Type A blood—common in Europe and the US—makes up roughly one-third of the supply of donations. A technology that converts it to universal O could boost blood transplant resources in this part of the world.

“This is a first, and if these data can be replicated, it is certainly a major advance,” Dr. Harvey Klein at the National Institutes of Health’s Clinical Center, who was not involved in the work,  told Science at the time.

There’s one problem though. Converted blood doesn’t always work.

Let’s Talk ABO+

When tested in clinical trials, converted blood has raised safety concerns. Even when removing A or B antigens completely from donated blood, small hints from earlier studies found an immune mismatch between the transformed donor blood and the recipient. In other words, the engineered O blood sometimes still triggered an immune response.

Why?

There’s more to blood types than classic ABO. Type A is composed of two different subtypes—one with higher A antigen levels than the other. Type B, common in people of Asian and African descent, also comes in “extended” forms. These recently discovered sugar chains are longer and harder to break down than in the classic versions. Called “extended antigens,” they could be why some converted blood still stimulates the immune system after transfusion.

The new study tackled these extended forms by again peeking into gut bacteria DNA. One bacterial strain, A. muciniphila, stood out. These bugs contain enzymes that work like a previously discovered version that chops up type A and B antigens, but surprisingly, they also strip away extended versions of both antigens.

These enzymes weren’t previously known to science, with just 30 percent similarity when compared to a previous benchmark enzyme that cuts up B and extended B antigens.

Using cells from different donors, the scientists engineered an enzyme soup that rapidly wiped out blood antigens. The strategy is “unprecedented,” wrote the team.

Although the screen found multiple enzymes capable of blood type conversion, each individually had limited effects. But when mixed and matched, the recipe transformed donated B type cells into type O, with limited immune responses when mixed with other blood types.

A similar strategy yielded three different enzymes to cut out the problematic A antigen and, in turn, transform the blood to type O. Some people secrete the antigen into other bodily fluids—for example, saliva, sweat, or tears. Others, dubbed non-secreters, have less of these antigens floating around their bodies. Using blood donated from both secreters and non-secreters, the team treated red blood cells to remove the A antigen and its extended versions.

When mixed with other blood types, the enzyme cocktail lowered their immune response, although with lower efficacy than cells transformed from type B to O.

By mapping the structures of these enzymes, the team found some parts increased their ability to chop up sugar chains. Focusing on these hot-spot structures, scientists are set to hunt down other naturally-derived enzymes—or use AI to engineer ones with better efficacy and precision.

The system still needs to be tested in humans. And the team didn’t address other blood antigens, such as the Rh system, which is what makes blood types positive or negative. Still, bacterial enzymes appear to be an unexpected but promising way to engineer universal blood.

Image Credit: Zeiss Microscopy / Flickr

ARTIFICIAL INTELLIGENCE

Meta’s Open Source Llama 3 Is Already Nipping at OpenAI’s Heels
Will Knight | Wired
“OpenAI changed the world with ChatGPT, setting off a wave of AI investment and drawing more than 2 million developers to its cloud APIs. But if open source models prove competitive, developers and entrepreneurs may decide to stop paying to access the latest model from OpenAI or Google and use Llama 3 or one of the other increasingly powerful open source models that are popping up.”

BIOTECH

‘Real Hope’ for Cancer Cure as Personal mRNA Vaccine for Melanoma Trialed
Andrew Gregory | The Guardian
“Experts are testing new jabs that are custom-built for each patient and tell their body to hunt down cancer cells to prevent the disease ever coming back. A phase 2 trial found the vaccines dramatically reduced the risk of the cancer returning in melanoma patients. Now a final, phase 3, trial has been launched and is being led by University College London Hospitals NHS Foundation Trust (UCLH). Dr Heather Shaw, the national coordinating investigator for the trial, said the jabs had the potential to cure people with melanoma and are being tested in other cancers, including lung, bladder and kidney.”

DIGITAL MEDIA

An AI Startup Made a Hyperrealistic Deepfake of Me That’s So Good It’s Scary
Melissa Heikkilä | MIT Technology Review
“Until now, all AI-generated videos of people have tended to have some stiffness, glitchiness, or other unnatural elements that make them pretty easy to differentiate from reality. Because they’re so close to the real thing but not quite it, these videos can make people feel annoyed or uneasy or icky—a phenomenon commonly known as the uncanny valley. Synthesia claims its new technology will finally lead us out of the valley.”

ENERGY

Nuclear Fusion Experiment Overcomes Two Key Operating Hurdles
Matthew Sparkes | New Scientist
“A nuclear fusion reaction has overcome two key barriers to operating in a ‘sweet spot’ needed for optimal power production: boosting the plasma density and keeping that denser plasma contained. The milestone is yet another stepping stone towards fusion power, although a commercial reactor is still probably years away.”

FUTURE

Daniel Dennett: ‘ Why Civilization Is More Fragile Than We Realized’
Tom Chatfield | BBC
“[Dennett’s] warning was not of a takeover by some superintelligence, but of a threat he believed that nonetheless could be existential for civilization, rooted in the vulnerabilities of human nature. ‘If we turn this wonderful technology we have for knowledge into a weapon for disinformation,’ he told me, ‘we are in deep trouble.’ Why? ‘Because we won’t know what we know, and we won’t know who to trust, and we won’t know whether we’re informed or misinformed. We may become either paranoid and hyper-skeptical, or just apathetic and unmoved. Both of those are very dangerous avenues. And they’re upon us.'”

ENVIRONMENT

California Just Went 9.25 Hours Using Only Renewable Energy
Adele Peters | Fast Company
“Last Saturday, as 39 million Californians went about their daily lives—taking showers, doing laundry, or charging their electric cars—the whole state ran on 100% clean electricity for more than nine hours. The same thing happened on Sunday, as the state was powered without fossil fuels for more than eight hours. It was the ninth straight day that solar, wind, hydropower, geothermal, and battery storage fully powered the electric grid for at least some portion of the time. Over the last six and a half weeks, that’s happened nearly every day. In some cases, it’s just for 15 minutes. But often it’s for hours at a time.”

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TECH

AI Hype Is Deflating. Can AI Companies Find a Way to Turn a Profit?
Gerrit De Vynck | The Washington Post
“Some once-promising start-ups have cratered, and the suite of flashy products launched by the biggest players in the AI race—OpenAI, Microsoft, Google and Meta—have yet to upend the way people work and communicate with one another. While money keeps pouring into AI, very few companies are turning a profit on the tech, which remains hugely expensive to build and run. The road to widespread adoption and business success is still looking long, twisty and full of roadblocks, say tech executives, technologists and financial analysts.”

ARTIFICIAL INTELLIGENCE

Apple Releases Eight Small AI Language Models Aimed at On-Device Use
Benj Edwards | Ars Technica
“In the world of AI, what might be called ‘small language models’ have been growing in popularity recently because they can be run on a local device instead of requiring data center-grade computers in the cloud. On Wednesday, Apple introduced a set of tiny source-available AI language models called OpenELM that are small enough to run directly on a smartphone. They’re mostly proof-of-concept research models for now, but they could form the basis of future on-device AI offerings from Apple.”

SPACE

If Starship Is Real, We’re Going to Need Big Cargo Movers on the Moon and Mars
Eric Berger | Ars Technica
“Unloading tons of cargo on the Moon may seem like a preposterous notion. During Apollo, mass restrictions were so draconian that the Lunar Module could carry two astronauts, their spacesuits, some food, and just 300 pounds (136 kg) of scientific payload down to the lunar surface. By contrast, Starship is designed to carry 100 tons, or more, to the lunar surface in a single mission. This is an insane amount of cargo relative to anything in spaceflight history, but that’s the future that [Jaret] Matthews is aiming toward.”

Image Credit: CARTIST / Unsplash

Genomics is revolutionizing medicine and science, but current approaches still struggle to capture the breadth of human genetic diversity. Pangenomes that incorporate many people’s DNA could be the answer, and a new project thinks quantum computers will be a key enabler.

When the Human Genome Project published its first reference genome in 2001, it was based on DNA from just a handful of humans. While less than one percent of our DNA varies from person to person, this can still leave important gaps and limit what we can learn from genomic analyses.

That’s why the concept of a pangenome has become increasingly popular. This refers to a collection of genomic sequences from many different people that have been merged to cover a much greater range of human genetic possibilities.

Assembling these pangenomes is tricky though, and their size and complexity make carrying out computational analyses on them daunting. That’s why the University of Cambridge, the Wellcome Sanger Institute, and the European Molecular Biology Laboratory’s European Bioinformatics Institute have teamed up to see if quantum computers can help.

“We’ve only just scratched the surface of both quantum computing and pangenomics,” David Holland of the Wellcome Sanger Institute said in a press release. “So to bring these two worlds together is incredibly exciting. We don’t know exactly what’s coming, but we see great opportunities for major new advances.”

Pangenomes could be crucial for discovering how different genetic variants impact human biology, or that of other species. The current reference genome is used as a guide to assemble genetic sequences, but due to the variability of human genomes there are often significant chunks of DNA that don’t match up. A pangenome would capture a lot more of that diversity, making it easier to connect the dots and giving us a more complete view of possible human genomes.

Despite their power, pangenomes are difficult to work with. While the genome of a single person is just a linear sequence of genetic data, a pangenome is a complex network that tries to capture all the ways in which its constituent genomes do and don’t overlap.

These so-called “sequence graphs” are challenging to construct and even more challenging to analyze. And it will require high levels of computational power and novel techniques to make use of the rich representation of human diversity contained within.

That’s where this new project sees quantum computers lending a hand. Relying on the quirks of quantum mechanics, they can tackle certain computational problems that are near impossible for classical computers.

While there’s still considerable uncertainty about what kinds of calculations quantum computers will actually be able to run, many hope they will dramatically improve our ability to solve problems relating to complex systems with large numbers of variables. This new project is aimed at developing quantum algorithms that speed up both the production and analysis of pangenomes, though the researchers admit it’s early days.

“We’re starting from scratch because we don’t even know yet how to represent a pangenome in a quantum computing environment,” David Yuan from the European Bioinformatics Institute said in the press release. “If you compare it to the first moon landings, this project is the equivalent of designing a rocket and training the astronauts.”

The project has been awarded $3.5 million, which will be used to develop new algorithms and then test them on simulated quantum hardware using supercomputers. The researchers think the tools they develop could lead to significant breakthroughs in personalized medicine. They could also be applied to pangenomes of viruses and bacteria, improving our ability to track and manage disease outbreaks.

Given its exploratory nature and the difficulty of getting quantum computers to do anything practical, it could be some time before the project bears fruit. But if they succeed, the researchers could significantly expand our ability to make sense of the genes that shape our lives.

Image Credit: Gerd Altmann / Pixabay

CRISPR has revolutionized science. AI is now taking the gene editor to the next level.

Thanks to its ability to accurately edit the genome, CRISPR tools are now widely used in biotechnology and across medicine to tackle inherited diseases. In late 2023, a therapy using the Nobel Prize-winning tool gained approval from the FDA to treat sickle cell disease. CRISPR has also enabled CAR T cell therapy to battle cancers and been used to lower dangerously high cholesterol levels in clinical trials.

Outside medicine, CRISPR tools are changing the agricultural landscape, with projects ongoing to engineer hornless bulls, nutrient-rich tomatoes, and livestock and fish with more muscle mass.

Despite its real-world impact, CRISPR isn’t perfect. The tool snips both strands of DNA, which can cause dangerous mutations. It also can inadvertently nip unintended areas of the genome and trigger unpredictable side effects.

CRISPR was first discovered in bacteria as a defense mechanism, suggesting that nature hides a bounty of CRISPR components. For the past decade, scientists have screened different natural environments—for example, pond scum—to find other versions of the tool that could potentially increase its efficacy and precision. While successful, this strategy depends on what nature has to offer. Some benefits, such as a smaller size or greater longevity in the body, often come with trade-offs like lower activity or precision.

Rather than relying on evolution, can we fast-track better CRISPR tools with AI?

This week, Profluent, a startup based in California, outlined a strategy that uses AI to dream up a new universe of CRISPR gene editors. Based on large language models—the technology behind the popular ChatGPT—the AI designed several new gene-editing components.

In human cells, the components meshed to reliably edit targeted genes. The efficiency matched classic CRISPR, but with far more precision. The most promising editor, dubbed OpenCRISPR-1, could also precisely swap out single DNA letters—a technology called base editing—with an accuracy that rivals current tools.

“We demonstrate the world’s first successful editing of the human genome using a gene editing system where every component is fully designed by AI,” wrote the authors in a blog post.

Match Made in Heaven

CRISPR and AI have had a long romance.

The CRISPR recipe has two main parts: A “scissor” Cas protein that cuts or nicks the genome and a “bloodhound” RNA guide that tethers the scissor protein to the target gene.

By varying these components, the system becomes a toolbox, with each setup tailored to perform a specific type of gene editing. Some Cas proteins cut both strands of DNA; others give just one strand a quick snip. Alternative versions can also cut RNA, a type of genetic material found in viruses, and can be used as diagnostic tools or antiviral treatments.

Different versions of Cas proteins are often found by searching natural environments or through a process called direct evolution. Here, scientist rationally swap out some parts of the Cas protein to potentially boost efficacy.

It’s a highly time-consuming process. Which is where AI comes in.

Machine learning has already helped predict off-target effects in CRISPR tools. It’s also homed in on smaller Cas proteins to make downsized editors easier to deliver into cells.

Profluent used AI in a novel way: Rather than boosting current systems, they designed CRISPR components from scratch using large language models.

The basis of ChatGPT and DALL-E, these models launched AI into the mainstream. They learn from massive amounts of text, images, music, and other data to distill patterns and concepts. It’s how the algorithms generate images from a single text prompt—say, “unicorn with sunglasses dancing over a rainbow”—or mimic the music style of a given artist.

The same technology has also transformed the protein design world. Like words in a book, proteins are strung from individual molecular “letters” into chains, which then fold in specific ways to make the proteins work. By feeding protein sequences into AI, scientists have already fashioned antibodies and other functional proteins unknown to nature.

“Large generative protein language models capture the underlying blueprint of what makes a natural protein functional,” wrote the team in the blog post. “They promise a shortcut to bypass the random process of evolution and move us towards intentionally designing proteins for a specific purpose.”

Do AIs Dream of CRISPR Sheep?

All large language models need training data. The same is true for an algorithm that generates gene editors. Unlike text, images, or videos that can be easily scraped online, a CRISPR database is harder to find.

The team first screened over 26 terabytes of data about current CRISPR systems and built a CRISPR-Cas atlas—the most extensive to date, according to the researchers.

The search revealed millions of CRISPR-Cas components. The team then trained their ProGen2 language model—which was fine-tuned for protein discovery—using the CRISPR atlas.

The AI eventually generated four million protein sequences with potential Cas activity. After filtering out obvious deadbeats with another computer program, the team zeroed in on a new universe of Cas “protein scissors.”

The algorithm didn’t just dream up proteins like Cas9. Cas proteins come in families, each with its own quirks in gene-editing ability. The AI also designed proteins resembling Cas13, which targets RNA, and Cas12a, which is more compact than Cas9.

Overall, the results expanded the universe of potential Cas proteins nearly five-fold. But do any of them work?

Hello, CRISPR World

For the next test, the team focused on Cas9, because it’s already widely used in biomedical and other fields. They trained the AI on roughly 240,000 different Cas9 protein structures from multiple types of animals, with the goal of generating similar proteins to replace natural ones—but with higher efficacy or precision.

The initial results were surprising: The generated sequences, roughly a million of them, were totally different than natural Cas9 proteins. But using DeepMind’s AlphaFold2, a protein structure prediction AI, the team found the generated protein sequences could adopt similar shapes.

Cas proteins can’t function without a bloodhound RNA guide. With the CRISPR-Cas atlas, the team also trained AI to generate an RNA guide when given a protein sequence.

The result is a CRISPR gene editor with both components—Cas protein and RNA guide— designed by AI. Dubbed OpenCRISPR-1, its gene editing activity was similar to classic CRISPR-Cas9 systems when tested in cultured human kidney cells. Surprisingly, the AI-generated version slashed off-target editing by roughly 95 percent.

With a few tweaks, OpenCRISPR-1 could also perform base editing, which can change single DNA letters. Compared to classic CRISPR, base editing is likely more precise as it limits damage to the genome. In human kidney cells, OpenCRISPR-1 reliably converted one DNA letter to another in three sites across the genome, with an editing rate similar to current base editors.

To be clear, the AI-generated CRISPR tools have only been tested in cells in a dish. For treatments to reach the clinic, they’d need to undergo careful testing for safety and efficacy in living creatures, which can take a long time.

Profluent is openly sharing OpenCRISPR-1 with researchers and commercial groups but keeping the AI that created the tool in-house. “We release OpenCRISPR-1 publicly to facilitate broad, ethical usage across research and commercial applications,” they wrote.

As a preprint, the paper describing their work has yet to be analyzed by expert peer reviewers. Scientists will also have to show OpenCRISPR-1 or variants work in multiple organisms, including plants, mice, and humans. But tantalizingly, the results open a new avenue for generative AI—one that could fundamentally change our genetic blueprint.

Image Credit: Profluent

The origin of life on Earth is still enigmatic, but we are slowly unraveling the steps involved and the necessary ingredients. Scientists believe life arose in a primordial soup of organic chemicals and biomolecules on the early Earth, eventually leading to actual organisms.

It’s long been suspected that some of these ingredients may have been delivered from space. Now a new study, published in Science Advances, shows that a special group of molecules, known as peptides, can form more easily under the conditions of space than those found on Earth. That means they could have been delivered to the early Earth by meteorites or comets—and that life may be able to form elsewhere, too.

The functions of life are upheld in our cells (and those of all living beings) by large, complex carbon-based (organic) molecules called proteins. How to make the large variety of proteins we need to stay alive is encoded in our DNA, which is itself a large and complex organic molecule.

However, these complex molecules are assembled from a variety of small and simple molecules such as amino acids—the so-called building blocks of life.

To explain the origin of life, we need to understand how and where these building blocks form and under what conditions they spontaneously assemble themselves into more complex structures. Finally, we need to understand the step that enables them to become a confined, self-replicating system—a living organism.

This latest study sheds light on how some of these building blocks might have formed and assembled and how they ended up on Earth.

Steps to Life

DNA is made up of about 20 different amino acids. Like letters of the alphabet, these are arranged in DNA’s double helix structure in different combinations to encrypt our genetic code.

Peptides are also an assemblage of amino acids in a chain-like structure. Peptides can be made up of as little as two amino acids, but also range to hundreds of amino acids.

The assemblage of amino acids into peptides is an important step because peptides provide functions such as catalyzing, or enhancing, reactions that are important to maintaining life. They are also candidate molecules that could have been further assembled into early versions of membranes, confining functional molecules in cell-like structures.

However, despite their potentially important role in the origin of life, it was not so straightforward for peptides to form spontaneously under the environmental conditions on the early Earth. In fact, the scientists behind the current study had previously shown that the cold conditions of space are actually more favorable to the formation of peptides.

The interstellar medium. Image Credit: Charles Carter/Keck Institute for Space Studies

In the very low density clouds of molecules and dust particles in a part of space called the interstellar medium (see above), single atoms of carbon can stick to the surfaces of dust grains together with carbon monoxide and ammonia molecules. They then react to form amino acid-like molecules. When such a cloud becomes denser and dust particles also start to stick together, these molecules can assemble into peptides.

In their new study, the scientists look at the dense environment of dusty disks, from which a new solar system with a star and planets emerges eventually. Such disks form when clouds suddenly collapse under the force of gravity. In this environment, water molecules are much more prevalent—forming ice on the surfaces of any growing agglomerates of particles that could inhibit the reactions that form peptides.

By emulating the reactions likely to occur in the interstellar medium in the laboratory, the study shows that, although the formation of peptides is slightly diminished, it is not prevented. Instead, as rocks and dust combine to form larger bodies such as asteroids and comets, these bodies heat up and allow for liquids to form. This boosts peptide formation in these liquids, and there’s a natural selection of further reactions resulting in even more complex organic molecules. These processes would have occurred during the formation of our own solar system.

Many of the building blocks of life such as amino acids, lipids, and sugars can form in the space environment. Many have been detected in meteorites.

Because peptide formation is more efficient in space than on Earth, and because they can accumulate in comets, their impacts on the early Earth might have delivered loads that boosted the steps towards the origin of life on Earth.

So, what does all this mean for our chances of finding alien life? Well, the building blocks for life are available throughout the universe. How specific the conditions need to be to enable them to self-assemble into living organisms is still an open question. Once we know that, we’ll have a good idea of how widespread, or not, life might be.

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

Image Credit: Aldebaran S / Unsplash

From Covid boosters to annual flu shots, most of us are left wondering: Why so many, so often?

There’s a reason to update vaccines. Viruses rapidly mutate, which can help them escape the body’s immune system, putting previously vaccinated people at risk of infection. Using AI modeling, scientists have increasingly been able to predict how viruses will evolve. But they mutate fast, and we’re still playing catch up.

An alternative strategy is to break the cycle with a universal vaccine that can train the body to recognize a virus despite mutation. Such a vaccine could eradicate new flu strains, even if the virus has transformed into nearly unrecognizable forms. The strategy could also finally bring a vaccine for the likes of HIV, which has so far notoriously evaded decades of efforts.

This month, a team from UC California Riverside, led by Dr. Shou-Wei Ding, designed a vaccine that unleashed a surprising component of the body’s immune system against invading viruses.

In baby mice without functional immune cells to ward off infections, the vaccine defended against lethal doses of a deadly virus. The protection lasted at least 90 days after the initial shot.

The strategy relies on a controversial theory. Most plants and fungi have an innate defense against viruses that chops up their genetic material. Called RNA interference (RNAi), scientists have long debated whether the same mechanism exists in mammals—including humans.

“It’s an incredible system because it can be adapted to any virus,” Dr. Olivier Voinnet at the Swiss Federal Institute of Technology, who championed the theory with Ding, told Nature in late 2013.

A Hidden RNA Universe

RNA molecules are usually associated with the translation of genes into proteins.

But they’re not just biological messengers. A wide array of small RNA molecules roam our cells. Some shuttle protein components through the cell during the translation of DNA. Others change how DNA is expressed and may even act as a method of inheritance.

But fundamental to immunity are small interfering RNA molecules, or siRNAs. In plants and invertebrates, these molecules are vicious defenders against viral attacks. To replicate, viruses need to hijack the host cell’s machinery to copy their genetic material—often, it’s RNA. The invaded cells recognize the foreign genetic material and automatically launch an attack.

During this attack, called RNA interference, the cell chops the invading viruses’ RNA genome into tiny chunks–siRNA. The cell then spews these viral siRNA molecules into the body to alert the immune system. The molecules also directly grab onto the invading viruses’ genome, blocking it from replicating.

Here’s the kicker: Vaccines based on antibodies usually target one or two locations on a virus, making them vulnerable to mutation should those locations change their makeup. RNA interference generates thousands of siRNA molecules that cover the entire genome—even if one part of a virus mutates, the rest is still vulnerable to the attack.

This powerful defense system could launch a new generation of vaccines. There’s just one problem. While it’s been observed in plants and flies, whether it exists in mammals has been highly controversial.

“We believe that RNAi has been antiviral for hundreds of millions of years,” Ding told Nature in 2013. “Why would we mammals dump such an effective defense?”

Natural Born Viral Killers

In the 2013 study in Science, Ding and colleagues suggested mammals also have an antiviral siRNA mechanism—it’s just being repressed by a gene carried by most viruses. Dubbed B2, the gene acts like a “brake,” smothering any RNA interference response from host cells by destroying their ability to make siRNA snippets.

Getting rid of B2 should kick RNA interference back into gear. To prove the theory, the team genetically engineered a virus without a functioning B2 gene and tried to infect hamster cells and immunocompromised baby mice. Called Nodamura virus, it’s transmitted by mosquitoes in the wild and is often deadly.

But without B2, even a lethal dose of the virus lost its infectious power. The baby mice rapidly generated a hefty dose of siRNA molecules to clear out the invaders. As a result, the infection never took hold, and the critters—even when already immunocompromised—survived.

“I truly believe that the RNAi response is relevant to at least some viruses that infect mammals,” said Ding at the time.

New-Age Vaccines

Many vaccines contain either a dead or a living but modified version of a virus to train the immune system. When faced with the virus again, the body produces T cells to kill off the target, B cells that pump out antibodies, and other immune “memory” cells to alert against future attacks. But their effects don’t always last, especially if a virus mutates.

Rather than rallying T and B cells, triggering the body’s siRNA response offers another type of immune defense. This can be done by deleting the B2 gene in live viruses. These viruses can be formulated into a new type of vaccine, which the team has been working to develop, relying on RNA interference to ward off invaders. The resulting flood of siRNA molecules triggered by the vaccine would, in theory, also provide some protection against future infection.

“If we make a mutant virus that cannot produce the protein to suppress our RNAi [RNA interference], we can weaken the virus. It can replicate to some level, but then loses the battle to the host RNAi response,” Ding said in a press release about the most recent study.  “A virus weakened in this way can be used as a vaccine for boosting our RNAi immune system.”

In the study, his team tried the strategy against Nodamura virus by removing its B2 gene.

The team vaccinated baby and adult mice, both of which were genetically immunocompromised in that they couldn’t mount T cell or B cell defenses. In just two days, the single shot fully protected the mice against a deadly dose of virus, and the effect lasted over three months.

Viruses are most harmful to vulnerable populations—infants, the elderly, and immunocompromised individuals. Because of their weakened immune systems, current vaccines aren’t always as effective. Triggering siRNA could be a life-saving alternative strategy.

Although it works in mice, whether humans respond similarly remains to be seen. But there’s much to look forward to. The B2 “brake” protein has also been found in lots of other common viruses, including dengue, flu, and a family of viruses that causes fever, rash, and blisters.

The team is already working on a new flu vaccine, using live viruses without the B2 protein. If successful, the vaccine could potentially be made as a nasal spray—forget the needle jab. And if their siRNA theory holds up, such a vaccine might fend off the virus even as it mutates into new strains. The playbook could also be adapted to tackle new Covid variants, RSV, or whatever nature next throws at us.

This vaccine strategy is “broadly applicable to any number of viruses, broadly effective against any variant of a virus, and safe for a broad spectrum of people,” study author Dr. Rong Hai said in the press release. “This could be the universal vaccine that we have been looking for.”

Image Credit: Diana Polekhina / Unsplash

ARTIFICIAL INTELLIGENCE

15 Graphs That Explain the State of AI in 2024
Eliza Strickland | IEEE Spectrum
“Each year, the AI Index lands on virtual desks with a louder virtual thud—this year, its 393 pages are a testament to the fact that AI is coming off a really big year in 2023. For the past three years, IEEE Spectrum has read the whole damn thing and pulled out a selection of charts that sum up the current state of AI.”

NEUROSCIENCE

The Next Frontier for Brain Implants Is Artificial Vision
Emily Mullin | Wired
“Elon Musk’s Neuralink and others are developing devices that could provide blind people with a crude sense of sight. …’This is not about getting biological vision back,’ says Philip Troyk, a professor of biomedical engineering at Illinois Tech, who’s leading the study Bussard is in. ‘This is about exploring what artificial vision could be.'”

DIGITAL MEDIA

Microsoft’s VASA-1 Can Deepfake a Person With One Photo and One Audio Track
Benj Edwards | Ars Technica
“On Tuesday, Microsoft Research Asia unveiled VASA-1, an AI model that can create a synchronized animated video of a person talking or singing from a single photo and an existing audio track. In the future, it could power virtual avatars that render locally and don’t require video feeds—or allow anyone with similar tools to take a photo of a person found online and make them appear to say whatever they want.”

TECH

Meta Is Already Training a More Powerful Successor to Llama 3
Will Knight | Wired
“On Thursday morning, Meta released its latest artificial intelligence model, Llama 3, touting it as the most powerful to be made open source so that anyone can use it. The same afternoon, Yann LeCun, Meta’s chief AI scientist, said an even more powerful successor to Llama is in the works. He suggested it could potentially outshine the world’s best closed AI models, including OpenAI’s GPT-4 and Google’s Gemini.”

COMPUTING

Intel Reveals World’s Biggest ‘Brain-Inspired’ Neuromorphic Computer
Matthew Sparkes | New Scientist
“Hala Point contains 1.15 billion artificial neurons across 1152 Loihi 2 achips, and is capable of 380 trillion synaptic operations per second. Mike Davies at Intel says that despite this power it occupies just six racks in a standard server case—a space similar to that of a microwave oven. Larger machines will be possible, says Davies. ‘We built this scale of system because, honestly, a billion neurons was a nice round number,’ he says. ‘I mean, there wasn’t any particular technical engineering challenge that made us stop at this level.'”

AUTOMATION

US Air Force Confirms First Successful AI Dogfight
Emma Roth | The Verge
“Human pilots were on board the X-62A with controls to disable the AI system, but DARPA says the pilots didn’t need to use the safety switch ‘at any point.’ The X-62A went against an F-16 controlled solely by a human pilot, where both aircraft demonstrated ‘high-aspect nose-to-nose engagements’ and got as close as 2,000 feet at 1,200 miles per hour. DARPA doesn’t say which aircraft won the dogfight, however.”

CULTURE

What If Your AI Girlfriend Hated You?
Kate Knibbs | Wired
“It seems as though we’ve arrived at the moment in the AI hype cycle where no idea is too bonkers to launch. This week’s eyebrow-raising AI project is a new twist on the romantic chatbot—a mobile app called AngryGF, which offers its users the uniquely unpleasant experience of getting yelled at via messages from a fake person.”

NEUROSCIENCE

Insects and Other Animals Have Consciousness, Experts Declare
Dan Falk | Quanta
“For decades, there’s been a broad agreement among scientists that animals similar to us—the great apes, for example—have conscious experience, even if their consciousness differs from our own. In recent years, however, researchers have begun to acknowledge that consciousness may also be widespread among animals that are very different from us, including invertebrates with completely different and far simpler nervous systems.”

SCIENCE

Two Lifeforms Merge in Once-in-a-Billion-Years Evolutionary Event
Michael Irving | New Atlas
“Scientists have caught a once-in-a-billion-years evolutionary event in progress, as two lifeforms have merged into one organism that boasts abilities its peers would envy. Last time this happened, Earth got plants. …A species of algae called Braarudosphaera bigelowii was found to have engulfed a cyanobacterium that lets them do something that algae, and plants in general, can’t normally do—’fixing’ nitrogen straight from the air, and combining it with other elements to create more useful compounds.”

Image Credit: Shubham Dhage / Unsplash

Dubbed “living drugs,” CAR T cells are bioengineered from a patient’s own immune cells to make them better able to hunt and destroy cancer.

The treatment is successfully tackling previously untreatable blood cancers. Six therapies are already approved by the FDA. Over a thousand clinical trials are underway. These aren’t limited to cancer—they cover a range of difficult medical problems such as autoimmune diseases, heart conditions, and viral infections including HIV. They may even slow down the biological processes that contribute to aging.

But CAR T has an Achilles heel.

Once injected into the body, the cells often slowly dwindle. Called “exhaustion,” this process erodes therapeutic effect over time and has dire medical consequences. According to Dr. Evan Weber at the University of Pennsylvania, more than 50 percent of people who respond to CAR T therapies eventually relapse. This may also be why CAR T cells have struggled to fight off solid tumors in breast, pancreatic, or deadly brain cancers.

This month, two teams found a potential solution—make CAR T cells more like stem cells. Known for their regenerative abilities, stem cells easily repopulate the body. Both teams identified the same protein “master switch” to make engineered cells resemble stem cells.

One study, led by Weber, found that adding the protein, called FOXO1, revved up metabolism and health in CAR T cells in mice. Another study from a team at the Peter MacCallum Cancer Center in Australia found FOXO1-boosted cells appeared genetically similar to immune stem cells and were better able to fend off solid tumors.

While still early, “these findings may help improve the design of CAR T cell therapies and potentially benefit a wider range of patients,” said Weber in a press release.

I Remember

Here’s how CAR T cell therapy usually works.

The approach focuses on T cells, a particular type of immune cell that naturally hunts downs and eliminates infections and cancers inside the body. Enemy cells are dotted with a specific set of proteins, a kind of cellular fingerprint, that T cells recognize and latch onto.

Tumors also have a unique signature. But they can be sneaky, with some eventually developing ways to evade immune surveillance. In solid cancers, for example, they can pump out chemicals that fight off immune cell defenders, allowing the cancer to grow and spread.

CAR T cells are designed to override these barriers.

To make them, medical practitioners remove T cells from the body and genetically engineer them to produce tailormade protein hooks targeting a particular protein on tumor cells. The supercharged T cells are then grown in petri dishes and transfused back into the body.

In the beginning, CAR T was a last-resort blood cancer treatment, but now it’s a first-line therapy. Keeping the engineered cells around inside the body, however, has been a struggle. With time, the cells stop dividing and become dysfunctional, potentially allowing the cancer to relapse.

The Translator

To tackle cell exhaustion, Weber’s team found inspiration in the body itself.

Our immune system has a cellular ledger tracking previous infections. The cells making up this ledger are called memory T cells. They’re a formidable military reserve, a portion of which resemble stem cells. When the immune system detects an invader it’s seen before—a virus, bacteria, or cancer cell—these reserve cells rapidly proliferate to fend off the attack.

CAR T cells don’t usually have this ability. Inside multiple cancers, they eventually die off—allowing cancers to return. Why?

In 2012, Dr. Crystal Mackall at Stanford University found several changes in gene expression that lead to CAR T cell exhaustion. In the new study, together with Weber, the team discovered a protein, FOXO1, that could lengthen CAR T’s effects.

In one test, a drug that inhibited FOXO1 caused CAR T cells to rapidly fail and eventually die in petri dishes. Erasing genes encoding FOXO1 also hindered the cells and increased signs of CAR T exhaustion. When infused into mice with leukemia, CAR T cells without FOXO1 couldn’t treat the cancer. By contrast, increasing levels of FOXO1 helped the cells readily fight it off.

Analyzing genes related to FOXO1, the team found they were mostly connected to immune cell memory. It’s likely that adding the gene encoding FOXO1 to CAR T cells promotes a stable memory for the cells, so they can easily recognize potential harm—be it cancer or pathogen—long after the initial infection.

When treating mice with leukemia, a single dose of the FOXO1-enhanced cells decreased cancer growth and increased survival up to five-fold compared to standard CAR T therapy. The enhanced treatment also tackled a type of bone cancer in mice, which is often hard to treat without surgery and chemotherapy.

An Immune Link

Meanwhile, the Australian team also zeroed in on FOXO1. Led by Drs. Junyun Lai, Paul Beavis, and Phillip Darcy, the team was looking for protein candidates to enhance CAR T longevity.

The idea was, like their natural counterparts, engineered CAR T cells also need a healthy metabolism to thrive and divide.

They started by analyzing a protein previously shown to enhance CAR T metabolism, potentially lowering the chances of exhaustion. Mapping the epigenome and transcriptome in CAR T cells—both of which tell us how genes are expressed—they also discovered FOXO1 regulating CAR T cell longevity.

As a proof of concept, the team induced exhaustion in the engineered cells by increasingly restricting their ability to divide.

In mice with cancer, cells supercharged with FOXO1 lasted longer by months than those that hadn’t been boosted. The critters’ liver and kidney functions remained normal, and they didn’t lose weight during the treatment, a marker of overall health. The FOXO1 boost also changed how genes were expressed in the cells—they looked younger, as if in a stem cell-like state.

The new recipe also worked in T cells donated by six people with cancer who had undergone standard CAR T therapy. Adding a dose of FOXO1 to these cells increased their metabolism.

Multiple CAR T clinical trials are ongoing. But “the effects of such cells are transient and do not provide long-term protection against exhaustion,” wrote Darcy and team. In other words, durability is key for CAR T cells to live up to their full potential.

A FOXO1 boost offers a way—although it may not be the only way.

“By studying factors that drive memory in T cells, like FOXO1, we can enhance our understanding of why CAR T cells persist and work more effectively in some patients compared to others,” said Weber.

Image Credit: Gerardo Sotillo, Stanford Medicine

Graphene has been hailed as a wonder material, but it also set off a rush to find other promising atomically thin materials. Now researchers have managed to create a 2D version of gold they call “goldene,” which could have a host of applications in chemistry.

Scientists had speculated about the possibility of creating layers of carbon just a single atom thick for many decades. But it wasn’t until 2004 that a team from the University of Manchester in the UK first produced graphene sheets using the remarkably simple technique of peeling them off a lump of graphite with common sticky tape.

The resulting material’s high strength, high conductivity, and unusual optical properties set off a stampede to find applications for it. But it also spurred researchers to investigate what kinds of exotic capabilities other ultra-thin materials could have.

Gold is one material scientists have long been eager to make as thin as graphene, but so far, efforts have been in vain. Now though, researchers from Linköping University in Sweden have borrowed from an old Japanese forging technique to create ultra-thin flakes of what they’re calling “goldene.”

“If you make a material extremely thin, something extraordinary happens,” Shun Kashiwaya, who led the research, said in a press release. “The same thing happens with gold.”

Making goldene has proven tough in the past because its atoms tend to clump together. So, even if you can create a 2D sheet of gold atoms they quickly roll up to create nanoparticles instead.

The researchers got around this by taking a ceramic called titanium silicon carbide, which features ultra-thin layers of silicon between layers of titanium carbide, and coating it with gold. They then heated it in a furnace, which caused the gold to diffuse into the material and replace the silicon layers in a process known as intercalation.

This created atomically thin layers of gold embedded in the ceramic. To get it out, they had to borrow a century-old technique developed by Japanese knife makers. They used a chemical formulation known as Murakami’s reagent, which etches away carbon residue, to slowly reveal the gold sheets.

The researchers had to experiment with different concentrations of the reagent and various etching times. They also had to add a detergent-like chemical called a surfactant that protected the gold sheets from the etching liquid and prevented them from curling up. The gold flakes could then be sieved out of the solution to be examined more closely.

In a paper in Nature Synthesis, the researchers describe how they used an electron microscope to confirm that the gold layers were indeed just one atom thick. They also showed that the goldene flakes were semiconductors.

It’s not the first time someone has claimed to have created goldene, notes Nature. But previous attempts have involved creating the ultra-thin sheets sandwiched between other materials, and the Linköping team say their effort is the first to create a “free-standing 2D metal.”

The material could have a range of use cases, the researchers say. Gold nanoparticles already show promise as catalysts that can turn plastic waste and biomass into valuable materials, they note in their paper, and they have properties that could prove useful for energy harvesting, creating photonic devices, or even splitting water to create hydrogen fuel.

It will take work to tweak the synthesis method so it can produce commercially useful amounts of the material, a challenge that has delayed the full arrival of graphene as a widely used product too. But the team is also investigating whether similar approaches can be applied to other useful catalytic metals. Graphene might not be the only wonder material in town for long.

Image Credit: Nature Synthesis (CC BY 4.0)

Yesterday, Boston Dynamics announced it was retiring its hydraulic Atlas robot. Atlas has long been the standard bearer of advanced humanoid robots. Over the years, the company was known as much for its research robots as it was for slick viral videos of them working out in military fatigues, forming dance mobs, and doing parkour. Fittingly, the company put together a send-off video of Atlas’s greatest hits and blunders.

But there were clues this wasn’t really the end, not least of which was the specific inclusion of the word “hydraulic” and the last line of the video, “‘Til we meet again, Atlas.” It wasn’t a long hiatus. Today, the company released hydraulic Atlas’s successor—electric Atlas.

The new Atlas is notable for several reasons. First, and most obviously, Boston Dynamics has finally done away with hydraulic actuators in favor of electric motors. To be clear, Atlas has long had an onboard battery pack—but now it’s fully electric. The advantages of going electric include less cost, noise, weight, and complexity. It also allows for a more polished design. From the company’s own Spot robot to a host of other humanoid robots, fully electric models are the norm these days. So, it’s about time Atlas made the switch.

Without a mess of hydraulic hoses to contend with, the new Atlas can now also contort itself in new ways. As you’ll note in the release video, the robot rises to its feet—a crucial skill for a walking robot—in a very, let’s say, special way. It folds its legs up along its torso and impossibly, for a human at least, pivots up through its waist (no hands). Once standing Atlas swivels its head 180 degrees, then does the same thing at each hip joint and the waist. It takes a few watches to really appreciate all the weirdness there.

The takeaway is that while Atlas looks like us, it’s capable of movements we aren’t and therefore has more flexibility in how it completes future tasks.

This theme of same-but-different is evident in its head too. Instead of opting for a human-like head that risks slipping into the uncanny valley, the team chose a featureless (for now) lighted circle. In an interview with IEEE Spectrum, Boston Dynamics CEO, Robert Playter, said the human-like designs they tried seemed “a little bit threatening or dystopian.”

“We’re trying to project something else: a friendly place to look to gain some understanding about the intent of the robot,” he said. “The design borrows from some friendly shapes that we’d seen in the past. For example, there’s the old Pixar lamp that everybody fell in love with decades ago, and that informed some of the design for us.”

While most of these upgrades are improvements, there is one area where it’s not totally clear how well the new form will fair: strength and power.

Hydraulics are known to provide both, and Atlas pushed its hydraulics to their limits carrying heavy objects, executing backflips, and doing 180-degree, in-air twists. According to the press release and Playter’s interviews, little has been lost in this category. In fact, they say, electric Atlas is stronger than hydraulic Atlas. Still, as with all things robotics, the ultimate proof of how capable it is will likely be in video form, which we’ll eagerly await.

Despite big design updates, the company’s messaging is perhaps more notable. Atlas used to be a research robot. Now, the company intends to sell them commercially.

This isn’t terribly surprising. There are now a number of companies competing in the humanoid robots space, including Agility, 1X, Tesla, Apptronik, and Figure—which just raised $675 million at a $2.6 billion valuation. Several are making rapid progress, with a heavy focus on AI, and have kicked off real-world pilots.

Where does Boston Dynamics fit in? With Atlas, the company has been the clear leader for years. So, it’s not starting from the ground floor. Also, thanks to its Spot and Stretch robots, the company already has experience commercializing and selling advanced robots, from identifying product-market fit to dealing with logistics and servicing. But AI was, until recently, less of a focus. Now, they’re folding reinforcement learning into Spot, have begun experimenting with generative AI too, and promise more is coming.

Hyundai acquired Boston Dynamics for $1.1 billion in 2021. This may prove advantageous, as they have access to a world-class manufacturing company along with its resources and expertise producing and selling machines at scale. It’s also an opportunity to pilot Atlas in real-world situations and perfect it for future customers. Plans are already in motion to put Atlas to work at Hyundai next year.

Still, it’s worth noting that, although humanoid robots are attracting attention, getting big time investment, and being tried out in commercial contexts, there’s likely a ways to go before they reach the kind of generality some companies are touting. Playter says Boston Dynamics is going for multi-purpose, but still niche, robots in the near term.

“It definitely needs to be a multi-use case robot. I believe that because I don’t think there’s very many examples where a single repetitive task is going to warrant these complex robots,” he said. “I also think, though, that the practical matter is that you’re going to have to focus on a class of use cases, and really making them useful for the end customer.”

Humanoid robots that tidy your house and do the dishes may not be imminent, but the field is hot, and AI is bringing a degree of generality not possible a year ago. Now that Boston Dynamics has thrown its name in the hat, things will only get more interesting from here. We’ll be keeping a close eye on YouTube to see what new tricks Atlas has up its sleeve.

Image Credit: Boston Dynamics

Stars like the sun are remarkably constant. They vary in brightness by only 0.1 percent over years and decades, thanks to the fusion of hydrogen into helium that powers them. This process will keep the sun shining steadily for about 5 billion more years, but when stars exhaust their nuclear fuel, their deaths can lead to pyrotechnics.

The sun will eventually die by growing large and then condensing into a type of star called a white dwarf. But stars over eight times more massive than the sun die violently in an explosion called a supernova.

Supernovae happen across the Milky Way only a few times a century, and these violent explosions are usually remote enough that people here on Earth don’t notice. For a dying star to have any effect on life on our planet, it would have to go supernova within 100 light years from Earth.

I’m an astronomer who studies cosmology and black holes.

In my writing about cosmic endings, I’ve described the threat posed by stellar cataclysms such as supernovae and related phenomena such as gamma-ray bursts. Most of these cataclysms are remote, but when they occur closer to home they can pose a threat to life on Earth.

The Death of a Massive Star

Very few stars are massive enough to die in a supernova. But when one does, it briefly rivals the brightness of billions of stars. At one supernova per 50 years, and with 100 billion galaxies in the universe, somewhere in the universe a supernova explodes every hundredth of a second.

The dying star emits high-energy radiation as gamma rays. Gamma rays are a form of electromagnetic radiation with wavelengths much shorter than light waves, meaning they’re invisible to the human eye. The dying star also releases a torrent of high-energy particles in the form of cosmic rays: subatomic particles moving at close to the speed of light.

Supernovae in the Milky Way are rare, but a few have been close enough to Earth that historical records discuss them. In 185 AD, a star appeared in a place where no star had previously been seen. It was probably a supernova.

Observers around the world saw a bright star suddenly appear in 1006 AD. Astronomers later matched it to a supernova 7,200 light years away. Then, in 1054 AD, Chinese astronomers recorded a star visible in the daytime sky that astronomers subsequently identified as a supernova 6,500 light years away.

Johannes Kepler, the astronomer who observed what was likely a supernova in 1604. Image Credit: Kepler-Museum in Weil der Stadt

Johannes Kepler observed the last supernova in the Milky Way in 1604, so in a statistical sense, the next one is overdue.

At 600 light years away, the red supergiant Betelgeuse in the constellation of Orion is the nearest massive star getting close to the end of its life. When it goes supernova, it will shine as bright as the full moon for those watching from Earth, without causing any damage to life on our planet.

Radiation Damage

If a star goes supernova close enough to Earth, the gamma-ray radiation could damage some of the planetary protection that allows life to thrive on Earth. There’s a time delay due to the finite speed of light. If a supernova goes off 100 light years away, it takes 100 years for us to see it.

Astronomers have found evidence of a supernova 300 light years away that exploded 2.5 million years ago. Radioactive atoms trapped in seafloor sediments are the telltale signs of this event. Radiation from gamma rays eroded the ozone layer, which protects life on Earth from the sun’s harmful radiation. This event would have cooled the climate, leading to the extinction of some ancient species.

Safety from a supernova comes with greater distance. Gamma rays and cosmic rays spread out in all directions once emitted from a supernova, so the fraction that reach the Earth decreases with greater distance. For example, imagine two identical supernovae, with one 10 times closer to Earth than the other. Earth would receive radiation that’s about a hundred times stronger from the closer event.

A supernova within 30 light years would be catastrophic, severely depleting the ozone layer, disrupting the marine food chain and likely causing mass extinction. Some astronomers guess that nearby supernovae triggered a series of mass extinctions 360 to 375 million years ago. Luckily, these events happen within 30 light years only every few hundred million years.

When Neutron Stars Collide

But supernovae aren’t the only events that emit gamma rays. Neutron star collisions cause high-energy phenomena ranging from gamma rays to gravitational waves.

Left behind after a supernova explosion, neutron stars are city-size balls of matter with the density of an atomic nucleus, so 300 trillion times denser than the sun. These collisions created many of the gold and precious metals on Earth. The intense pressure caused by two ultradense objects colliding forces neutrons into atomic nuclei, which creates heavier elements such as gold and platinum.

A neutron star collision generates an intense burst of gamma rays. These gamma rays are concentrated into a narrow jet of radiation that packs a big punch.

If the Earth were in the line of fire of a gamma-ray burst within 10,000 light years, or 10 percent of the diameter of the galaxy, the burst would severely damage the ozone layer. It would also damage the DNA inside organisms’ cells, at a level that would kill many simple life forms like bacteria.

That sounds ominous, but neutron stars do not typically form in pairs, so there is only one collision in the Milky Way about every 10,000 years. They are 100 times rarer than supernova explosions. Across the entire universe, there is a neutron star collision every few minutes.

Gamma-ray bursts may not hold an imminent threat to life on Earth, but over very long time scales, bursts will inevitably hit the Earth. The odds of a gamma-ray burst triggering a mass extinction are 50 percent in the past 500 million years and 90 percent in the 4 billion years since there has been life on Earth.

By that math, it’s quite likely that a gamma-ray burst caused one of the five mass extinctions in the past 500 million years. Astronomers have argued that a gamma-ray burst caused the first mass extinction 440 million years ago, when 60 percent of all marine creatures disappeared.

A Recent Reminder

The most extreme astrophysical events have a long reach. Astronomers were reminded of this in October 2022, when a pulse of radiation swept through the solar system and overloaded all of the gamma-ray telescopes in space.

It was the brightest gamma-ray burst to occur since human civilization began. The radiation caused a sudden disturbance to the Earth’s ionosphere, even though the source was an explosion nearly two billion light years away. Life on Earth was unaffected, but the fact that it altered the ionosphere is sobering—a similar burst in the Milky Way would be a million times brighter.

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

Image Credit: NASA, ESA, Joel Kastner (RIT)

AI models are power hogs.

As the algorithms grow and become more complex, they’re increasingly taxing current computer chips. Multiple companies have designed chips tailored to AI to reduce power draw. But they’re all based on one fundamental rule—they use electricity.

This month, a team from Tsinghua University in China switched up the recipe. They built a neural network chip that uses light rather than electricity to run AI tasks at a fraction of the energy cost of NVIDIA’s H100, a state-of-the-art chip used to train and run AI models.

Called Taichi, the chip combines two types of light-based processing into its internal structure. Compared to previous optical chips, Taichi is far more accurate for relatively simple tasks such as recognizing hand-written numbers or other images. Unlike its predecessors, the chip can generate content too. It can make basic images in a style based on the Dutch artist Vincent van Gogh, for example, or classical musical numbers inspired by Johann Sebastian Bach.

Part of Taichi’s efficiency is due to its structure. The chip is made of multiple components called chiplets. Similar to the brain’s organization, each chiplet performs its own calculations in parallel, the results of which are then integrated with the others to reach a solution.

Faced with a challenging problem of separating images over 1,000 categories, Taichi was successful nearly 92 percent of the time, matching current chip performance, but slashing energy consumption over a thousand-fold.

For AI, “the trend of dealing with more advanced tasks [is] irreversible,” wrote the authors. “Taichi paves the way for large-scale photonic [light-based] computing,” leading to more flexible AI with lower energy costs.

Chip on the Shoulder

Today’s computer chips don’t mesh well with AI.

Part of the problem is structural. Processing and memory on traditional chips are physically separated. Shuttling data between them takes up enormous amounts of energy and time.

While efficient for solving relatively simple problems, the setup is incredibly power hungry when it comes to complex AI, like the large language models powering ChatGPT.

The main problem is how computer chips are built. Each calculation relies on transistors, which switch on or off to represent the 0s and 1s used in calculations. Engineers have dramatically shrunk transistors over the decades so they can cram ever more onto chips. But current chip technology is cruising towards a breaking point where we can’t go smaller.

Scientists have long sought to revamp current chips. One strategy inspired by the brain relies on “synapses”—the biological “dock” connecting neurons—that compute and store information at the same location. These brain-inspired, or neuromorphic, chips slash energy consumption and speed up calculations. But like current chips, they rely on electricity.

Another idea is to use a different computing mechanism altogether: light. “Photonic computing” is “attracting ever-growing attention,” wrote the authors. Rather than using electricity, it may be possible to hijack light particles to power AI at the speed of light.

Let There Be Light

Compared to electricity-based chips, light uses far less power and can simultaneously tackle multiple calculations. Tapping into these properties, scientists have built optical neural networks that use photons—particles of light—for AI chips, instead of electricity.

These chips can work two ways. In one, chips scatter light signals into engineered channels that eventually combine the rays to solve a problem. Called diffraction, these optical neural networks pack artificial neurons closely together and minimize energy costs. But they can’t be easily changed, meaning they can only work on a single, simple problem.

A different setup depends on another property of light called interference. Like ocean waves, light waves combine and cancel each other out. When inside micro-tunnels on a chip, they can collide to boost or inhibit each other—these interference patterns can be used for calculations. Chips based on interference can be easily reconfigured using a device called an interferometer. Problem is, they’re physically bulky and consume tons of energy.

Then there’s the problem of accuracy. Even in the sculpted channels often used for interference experiments, light bounces and scatters, making calculations unreliable. For a single optical neural network, the errors are tolerable. But with larger optical networks and more sophisticated problems, noise rises exponentially and becomes untenable.

This is why light-based neural networks can’t be easily scaled up. So far, they’ve only been able to solve basic tasks, such as recognizing numbers or vowels.

“Magnifying the scale of existing architectures would not proportionally improve the performances,” wrote the team.

Double Trouble

The new AI, Taichi, combined the two traits to push optical neural networks towards real-world use.

Rather than configuring a single neural network, the team used a chiplet method, which delegated different parts of a task to multiple functional blocks. Each block had its own strengths: One was set up to analyze diffraction, which could compress large amounts of data in a short period of time. Another block was embedded with interferometers to provide interference, allowing the chip to be easily reconfigured between tasks.

Compared to deep learning, Taichi took a “shallow” approach whereby the task is spread across multiple chiplets.

With standard deep learning structures, errors tend to accumulate over layers and time. This setup nips problems that come from sequential processing in the bud. When faced with a problem, Taichi distributes the workload across multiple independent clusters, making it easier to tackle larger problems with minimal errors.

The strategy paid off.

Taichi has the computational capacity of 4,256 total artificial neurons, with nearly 14 million parameters mimicking the brain connections that encode learning and memory. When sorting images into 1,000 categories, the photonic chip was nearly 92 percent accurate, comparable to “currently popular electronic neural networks,” wrote the team.

The chip also excelled in other standard AI image-recognition tests, such as identifying hand-written characters from different alphabets.

As a final test, the team challenged the photonic AI to grasp and recreate content in the style of different artists and musicians. When trained with Bach’s repertoire, the AI eventually learned the pitch and overall style of the musician. Similarly, images from van Gogh or Edvard Munch—the artist behind the famous painting, The Scream—fed into the AI allowed it to generate images in a similar style, although many looked like a toddler’s recreation.

Optical neural networks still have much further to go. But if used broadly, they could be a more energy-efficient alternative to current AI systems. Taichi is over 100 times more energy efficient than previous iterations. But the chip still requires lasers for power and data transfer units, which are hard to condense.

Next, the team is hoping to integrate readily available mini lasers and other components into a single, cohesive photonic chip. Meanwhile, they hope Taichi will “accelerate the development of more powerful optical solutions” that could eventually lead to “a new era” of powerful and energy-efficient AI.

Image Credit: spainter_vfx / Shutterstock.com

ROBOTICS

Is Robotics About to Have Its Own ChatGPT Moment?
Melissa Heikkilä | MIT Technology Review
“For decades, roboticists have more or less focused on controlling robots’ ‘bodies’—their arms, legs, levers, wheels, and the like—via purpose-driven software. But a new generation of scientists and inventors believes that the previously missing ingredient of AI can give robots the ability to learn new skills and adapt to new environments faster than ever before. This new approach, just maybe, can finally bring robots out of the factory and into our homes.”

ARTIFICIAL INTELLIGENCE

Humans Forget. AI Assistants Will Remember Everything
Boone Ashworth | Wired
“Human brains, Gruber says, are really good at story retrieval, but not great at remembering details, like specific dates, names, or faces. He has been arguing for digital AI assistants that can analyze everything you do on your devices and index all those details for later reference.”

BIOTECH

The Effort to Make a Breakthrough Cancer Therapy Cheaper
Cassandra Willyard | MIT Technology Review
“CAR-T therapies are already showing promise beyond blood cancers. Earlier this year, researchers reported stunning results in 15 patients with lupus and other autoimmune diseases. CAR-T is also being tested as a treatment for solid tumors, heart disease, aging, HIV infection, and more. As the number of people eligible for CAR-T therapies increases, so will the pressure to reduce the cost.”

ETHICS

Students Are Likely Writing Millions of Papers With AI
Amanda Hoover | Wired
“A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing.”

SCIENCE

Physicists Capture First-Ever Image of an Electron Crystal
Isaac Schultz | Gizmodo
“Electrons are typically seen flitting around their atoms, but a team of physicists has now imaged the particles in a very different state: nestled together in a quantum phase called a Wigner crystal, without a nucleus at their core. The phase is named after Eugene Wigner, who predicted in 1934 that electrons would crystallize in a lattice when certain interactions between them are strong enough. The recent team used high-resolution scanning tunneling microscopy to directly image the predicted crystal.”

GADGETS

Review: Humane Ai Pin
Julian Chokkattu | Wired
“Humane has potential with the Ai Pin. I like being able to access an assistant so quickly, but right now, there’s nothing here that makes me want to use it over my smartphone. Humane says this is just version 1.0 and that many of the missing features I’ve mentioned will arrive later. I’ll be happy to give it another go then.”

SPACE

The Moon Likely Turned Itself Inside Out 4.2 Billion Years Ago
Passant Rabie | Gizmodo
“A team of researchers from the University of Arizona found new evidence that supports one of the wildest formation theories for the moon, which suggests that Earth’s natural satellite may have turned itself inside out a few million years after it came to be. In a new study published Monday in Nature Geoscience, the researchers looked at subtle variations in the moon’s gravitational field to provide the first physical evidence of a sinking mineral-rich layer.”

TECH

How Tech Giants Cut Corners to Harvest Data for AI
Cade Metz, Cecilia Kang, Sheera Frenkel, Stuart A. Thompson, and Nico Grantade | The New York Times
“The race to lead AI has become a desperate hunt for the digital data needed to advance the technology. To obtain that data, tech companies including OpenAI, Google and Meta have cut corners, ignored corporate policies and debated bending the law, according to an examination by The New York Times.”

ENERGY

Artificial Intelligence’s ‘Insatiable’ Energy Needs Not Sustainable, Arm CEO Says
Peter Landers | The Wall Street Journal
“In a January report, the International Energy Agency said a request to ChatGPT requires 2.9 watt-hours of electricity on average—equivalent to turning on a 60-watt lightbulb for just under three minutes. That is nearly 10 times as much as the average Google search. The agency said power demand by the AI industry is expected to grow by at least 10 times between 2023 and 2026.”

FUTURE

Someday, Earth Will Have a Final Total Solar Eclipse
Katherine Kornei | The New York Times
“The total solar eclipse visible on Monday over parts of Mexico, the United States and Canada was a perfect confluence of the sun and the moon in the sky. But it’s also the kind of event that comes with an expiration date: At some point in the distant future, Earth will experience its last total solar eclipse. That’s because the moon is drifting away from Earth, so our nearest celestial neighbor will one day, millions or even billions of years in the future, appear too small in the sky to completely obscure the sun.”
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Image Credit: Tim Foster / Unsplash

Elon Musk has long been open about his dreams of using SpaceX to spread humanity’s presence further into the solar system. And last weekend, he gave an updated outline of his vision for how the company’s rockets could enable the colonization of Mars.

The serial entrepreneur has been clear for a number of years that the main motivation for founding SpaceX was to make humans a multiplanetary species. For a long time, that seemed like the kind of aspirational goal one might set to inspire and motivate engineers rather than one with a realistic chance of coming to fruition.

But following the successful launch of the company’s mammoth Starship vehicle last month, the idea is beginning to look less far-fetched. And in a speech at the company’s facilities in South Texas, Musk explained how he envisions using Starship to deliver millions of tons of cargo to Mars over the next couple of decades to create a self-sustaining civilization.

“Starship is the first design of a rocket that is actually capable of making life multiplanetary,” Musk said. “No rocket before this has had the potential to extend life to another planet.”

In a slightly rambling opening to the speech, Musk explained that making humans multiplanetary could be an essential insurance policy in case anything catastrophic happens to Earth. The red planet is the most obvious choice, he said, as it’s neither too close nor too far from Earth and has many of the raw ingredients required to support a functioning settlement.

But he estimates it will require us to deliver several million tons of cargo to the surface to get that civilization up and running. Starship is central to those plans, and Musk outlined the company’s roadmap for the massive rocket over the coming years.

Key to the vision is making the vehicle entirely reusable. That means the first hurdle is proving SpaceX can land and reuse both the Super Heavy first stage rocket and the Starship spacecraft itself. The second of those challenges will be tougher, as the vehicle must survive reentry to the atmosphere—in the most recent test, it broke up on its way back to Earth.

Musk says they plan to demonstrate the ability to land and reuse the Super Heavy booster this year, which he thinks has an 80 to 90 percent chance of success. Assuming they can get Starship to survive the extreme heat of reentry, they are also going to attempt landing the vehicle on a mock launch pad out at sea in 2024, with the aim of being able to land and reuse it by next year.

Proving the rocket works and is reusable is just the very first step in Musk’s Mars ambitions though. To achieve his goal of delivering a million people to the red planet in the next 20 years, SpaceX will have to massively ramp up its production and launch capabilities.

The company is currently building a second launch tower at its base in South Texas and is also planning to build two more at Cape Canaveral in Florida. Musk said the Texas sites would be mostly used for test launches and development work, with the Florida ones being the main hub for launches once Starship begins commercial operations.

SpaceX plans to build six Starships this year, according to Musk, but it is also building what he called a “giant factory” that will enable it to massively ramp up production of the spacecraft. The long-term goal is to produce multiple Starships a day. That’s crucial, according to Musk, because Starships initially won’t return from Mars and will instead be used as raw materials to construct structures on the surface.

The company also plans to continue development of Starship, boosting its carrying capacity from around 100 tons today to 200 tons in the future and enabling it to complete multiple launches in a day. SpaceX also hopes to demonstrate ship-to-ship refueling in orbit next year. It will be necessary to replenish the fuel used up by Starship on launch so it has a full tank as it sets off for Mars.

Those missions will depart when the orbits of Earth and Mars bring them close together, an alignment that only happens every 26 months. As such, Musk envisions entire armadas of Starships setting off together whenever these windows arrive.

SpaceX has done some early work on what needs to happen once Starships arrive at the red planet. They’ve identified promising landing sites and the infrastructure that will need setting up, including power generation, ice-mining facilities, propellant factories, and communication networks. But Musk admits they’ve yet to start development of any of these.

One glaring omission in the talk was any detail on who’s going to be paying for all of this. While the goal of making humankind multiplanetary is a noble one, it’s far from clear how the endeavor would make money for those who put up the funds to make it possible.

Musk estimates that the cost of each launch could eventually fall to just $2 to $3 million. And he noted that profits from the company’s Starlink satellites and Falcon 9 launch vehicle are currently paying for Starship’s development. But those revenue streams are unlikely to cover the thousands of launches a year required to make his Mars dreams a reality.

Still, the very fact that the questions these days are more about economics than technical feasibility is testament to the rapid progress SpaceX has made. The dream of becoming a multiplanetary species may not be science fiction for much longer.

Image Credit: SpaceX

Growing a substitute liver inside a human body sounds like science fiction.

Yet a patient with severe liver damage just received an injection that could grow an additional “mini liver” directly inside their body. If all goes well, it’ll take up the failing liver’s job of filtering toxins from the blood.

For people with end-stage liver disease, a transplant is the only solution. But matching donor organs are hard to come by. Across the globe, two million people die from liver failure each year.

The new treatment, helmed by biotechnology company LyGenesis, offers an unusual solution. Rather than transplanting a whole new liver, the team is injecting healthy donor liver cells into lymph nodes in the patient’s upper abdomen. In a few months, it’s hoped the cells will gradually replicate and grow into a functional miniature liver.

The patient is part of a Phase 2a clinical trial, a stage that begins to gauge whether a therapy is effective. In up to 12 people with end-stage liver disease, the trial will test multiple doses to find the “Goldilocks” zone of treatment—effective with minimal side effects.

If successful, the therapy could sidestep the transplant organ shortage problem, not just for liver disease, but potentially also for kidney failure or diabetes. The math also works in favor of patients. Instead of one donor organ per recipient, healthy cells from one person could help multiple people in need of new organs.

A Living Bioreactor

Most of us don’t think about lymph nodes until we catch a cold, and they swell up painfully under the chin. These structures are dotted throughout the body. Like tiny cellular nurseries, they help immune cells proliferate to fend off invading viruses and bacteria.

They also have a dark side. Lymph nodes aid the spread of breast and other types of cancers. Because they’re highly connected to a highway of lymphatic vessels, cancer cells tunnel into them and take advantage of nutrients in the blood to grow and spread across the body.

What seems like a biological downfall may benefit regenerative medicine. If lymph nodes can support both immune cells and cancer growth, they may also be able to incubate other cell types and grow them into tissues—or even replacement organs.

The idea diverges from usual regenerative therapies, such as stem cell treatments, which aim to revive damaged tissues at the spot of injury. This is a hard ask: When organs fail, they often scar and spew out toxic chemicals that prevent engrafted cells from growing.

Lymph nodes offer a way to skip these cellular cesspools entirely.

Growing organs inside lymph nodes may sound far-fetched, but over a decade ago, LyGenesis’ chief scientific officer and co-founder, Dr. Eric Lagasse, showed it was possible in mice. In one test, his team injected liver cells directly into a lymph node inside a mouse’s belly. They found the grafted cells stayed in the “nursery,” rather than roaming the body and causing unexpected side effects.

In a mouse model of lethal liver failure, an infusion of healthy liver cells into the lymph node grew into a mini liver in just twelve weeks. The transplanted cells took over their host, developing into cube-like cells characteristic of normal liver cells and leaving behind just a sliver of normal lymph node cells.

The graft could support immune system growth and grew cells to shuttle bile and other digestive chemicals. It also boosted the mice’s average survival rate. Without treatment, most mice died within 10 weeks of the start of the study. Most mice injected with liver cells survived past 30 weeks.

A similar strategy worked in dogs and pigs with damaged livers. Injecting donor cells into lymph nodes formed mini livers in less than two months in pigs. Under the microscope, the newborn structures resembled the liver’s intricate architecture, including “highways” for bile to easily flow along instead of accumulating, which causes even more damage and scarring.

The body has over 500 hundred lymph nodes. Injecting into other lymph nodes located elsewhere also grew mini livers, but they weren’t as effective.

“It’s all about location, location, location,” said Lagasse at the time.

A Daring Trial

With prior experience guiding their clinical trial, LyGenesis dosed a first patient in late March.

The team used a technique called endoscopic ultrasound to direct the cells into the designated lymph node. In the procedure, a thin, flexible tube with a small ultrasound device is inserted through the mouth into the digestive track. The ultrasound generates an image of the surrounding tissue and helps guide the tube to the target lymph node for injection.

The procedure may sound difficult, but compared to a liver transplant, it’s minimally invasive. In an interview with Nature, Dr. Michael Hufford, CEO of LyGenesis, said the patient is recovering well and already discharged from the clinic.

The company aims to enroll all 12 patients by mid 2025 to test the therapy’s safety and efficacy.

Many questions remain. The transplanted cells could grow into mini livers of different sizes, based on chemical signals from the body. Although not a problem in mice and pigs, could they potentially overgrow in humans? Meanwhile, patients receiving the treatment will need to take a hefty dose of medications to suppress their immune systems. How these will interact with the transplants is also unknown.

Another question is dosage. Lymph nodes are plentiful. The trial will inject liver cells into up to five lymph nodes to see if multiple mini livers can grow and function without side effects.

If successful, the therapy has wider reach.

In diabetic mice, seeding lymph nodes with pancreatic cellular clusters restored their blood sugar levels. A similar strategy could combat Type 1 diabetes in humans. The company is also looking into whether the technology can revive kidney function or even combat aging.

But for now, Hufford is focused on helping millions of people with liver damage. “This therapy will potentially be a remarkable regenerative medicine milestone by helping patients with ESLD [end-stage liver disease] grow new functional ectopic livers in their own body,” he said.

Image Credit: A solution with liver cells in suspension / LyGenesis

We’re surrounded by ingenious substances: a menu of metal alloys that can wrap up leftovers or skin rockets, paints in any color imaginable, and ever-morphing digital displays. Virtually all of these exploit the natural properties of the underlying materials.

But an emerging class of materials is more versatile, even programmable.

Known as metamaterials, these substances are meticulously engineered such that their structural makeup—as opposed to their composition—determines their properties. Some metamaterials might make long-distance wireless power transfer practical, others could bring “invisibility cloaks” or futuristic materials that respond to brainwaves.

But most examples are solid metamaterials—a Harvard team wondered if they could make a metafluid. As it turns out, yes, absolutely. The team recently described their results in Nature.

“Unlike solid metamaterials, metafluids have the unique ability to flow and adapt to the shape of their container,” Katia Bertoldi, a professor in applied mechanics at Harvard and senior author of the paper, said in a press release. “Our goal was to create a metafluid that not only possesses these remarkable attributes but also provides a platform for programmable viscosity, compressibility, and optical properties.”

The team’s metafluid is made up of hundreds of thousands of tiny, stretchy spheres—each between 50 to 500 microns across—suspended in oil. The spheres change shape depending on the pressure of the surrounding oil. At higher pressures, they deform, one hemisphere collapsing inward into a kind of half moon shape. They then resume their original spherical shape when the pressure is relieved.

The metafluid’s properties—such as viscosity or opacity—change depending on which of these shapes its constituent spheres assume. The fluid’s properties can be fine-tuned based on how many spheres are in the liquid and how big or thick they are.

Greater pressure causes the spheres to collapse. When the pressure is relieved, they resume their spherical shape. Credit: Adel Djellouli/Harvard SEAS

As a proof of concept, the team filled a hydraulic robotic gripper with their metafluid. Robots usually have to be programmed to sense objects and adjust grip strength. The team showed the gripper could automatically adapt to a blueberry, a glass, and an egg without additional sensing or programming required. The pressure of each object “programmed” the liquid to adjust, allowing the gripper to pick up all three, undamaged, with ease.

The team also showed the metafluid could switch from opaque, when its constituents were spherical, to more transparent, when they collapsed. The latter shape, the researchers said, functions like a lens focusing light, while the former scatters light.

The metafluid obscures the Harvard logo then becomes more transparent as the capsules collapse. Credit: Adel Djellouli/Harvard SEAS

Also of note, the metafluid behaves like a Newtonian fluid when its components are spherical, meaning its viscosity only changes with shifts in temperature. When they collapse, however, it becomes a non-Newtonian fluid, where its viscosity changes depending on the shear forces present. The greater the shear force—that is, parallel forces pushing in opposite directions—the more liquid the metafluid becomes.

Next, the team will investigate additional properties—such as how their creation’s acoustics and thermodynamics change with pressure—and look into commercialization. Making the elastic spheres themselves is fairly straightforward, and they think metafluids like theirs might be useful in robots, as “intelligent” shock absorbers, or in color-changing e-inks.

“The application space for these scalable, easy-to-produce metafluids is huge,” said Bertoldi.

Of course, the team’s creation is still in the research phase. There are a plenty of hoops yet to navigate before it shows up in products we all might enjoy. Still, the work adds to a growing list of metamaterials—and shows the promise of going from solid to liquid.

Image Credit: Adel Djellouli/Harvard SEAS