Transhumanismus

Computing

We’re About to Simulate a Human Brain on a SupercomputerAlex Wilkins | New Scientist ($)

“What would it mean to simulate a human brain? Today’s most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden.”

Tech

Gemini Is WinningDavid Pierce | The Verge

“Each one of [the] elements [you need in AI] is complex and competitive; there’s a reason OpenAI CEO Sam Altman keeps shouting about how he needs trillions of dollars in compute alone. But Google is the one company that appears to have all of the pieces already in order. Over the last year, and even in the last few days, the company has made moves that suggest it is ready to be the biggest and most impactful force in AI.”

Artificial Intelligence

Meet the New Biologists Treating LLMs Like AliensWill Douglas Heaven | MIT Technology Review ($)

“[AI researchers] are pioneering new techniques that let them spot patterns in the apparent chaos of the numbers that make up these large language models, studying them as if they were doing biology or neuroscience on vast living creatures—city-size xenomorphs that have appeared in our midst.”

Biotechnology

Scientists Sequence a Woolly Rhino Genome From a 14,400-Year-Old Wolf’s StomachKiona N. Smith | Ars Technica

“DNA testing revealed that the meat was a prime cut of woolly rhinoceros, a now-extinct 2-metric-ton behemoth that once stomped across the tundras of Europe and Asia. Stockholm University paleogeneticist Sólveig Guðjónsdóttir and her colleagues recently sequenced a full genome from the piece of meat, which reveals some secrets about woolly rhino populations in the centuries before their extinction.”

Biotechnology

Finally, Some Good News in the Fight Against CancerEllyn Lapointe | Gizmodo

“The findings, published Tuesday, show for the first time that 70% of all cancer patients survived at least five years after being diagnosed between 2015 and 2021. That’s a major improvement since the mid-1970s, when the five-year survival rate was just 49%, according to the report.”

Computing

A Leading Use for Quantum Computers Might Not Need Them After AllKarmela Padavic-Callaghan | New Scientist ($)

“Understanding a molecule that plays a key role in nitrogen fixing—a chemical process that enables life on Earth—has long been thought of as problem for quantum computers, but now a classical computer may have solved it. …The researchers also estimated that the supercomputer method may even be faster than quantum ones, performing calculations in less than a minute that would take 8 hours on a quantum device—although this estimate assumes an ideal supercomputer performance.”

Artificial Intelligence

AI Models Are Starting to Crack High-Level Math ProblemsRussell Brandom | TechCrunch

“Since the release of GPT 5.2—which Somani describes as “anecdotally more skilled at mathematical reasoning than previous iterations” — the sheer volume of solved problems has become difficult to ignore, raising new questions about large language models’ ability to push the frontiers of human knowledge.”

Energy

How Next-Generation Nuclear Reactors Break Out of the 20th-Century BlueprintCasey Crownhart | MIT Technology Review ($)

“Demand for electricity is swelling around the world. …Nuclear could help, but only if new plants are safe, reliable, cheap, and able to come online quickly. Here’s what that new generation might look like.”

Artificial Intelligence

AI’s Hacking Skills Are Approaching an ‘Inflection Point’Will Knight | Wired ($)

“The situation points to a growing risk. As AI models continue to get smarter, their ability to find zero-day bugs and other vulnerabilities also continues to grow. The same intelligence that can be used to detect vulnerabilities can also be used to exploit them.”

Artificial Intelligence

Anthropic’s Claude Cowork Is an AI Agent That Actually WorksReece Rogers | Wired ($)

“[My experiences testing subpar agents] expose a consistent pattern of generative AI startups overpromising and underdelivering when it comes to these ‘agentic’ helpers—programs designed to take control of your computer, performing chores and digital errands to free up your time for more important things. …They just didn’t work. This poor track record makes Anthropic’s latest agent, Claude Cowork, a nice surprise.”

Tech

Ads Are Coming to ChatGPT. Here’s How They’ll WorkMaxwell Zeff | Wired ($)

“OpenAI could use a business like [ads] right about now. The decade-old company has raised roughly $64 billion from investors over its lifetime, and it generated only a fraction of that in revenue last year. Competition from rivals like Google Gemini has only amped up the pressure for OpenAI to monetize ChatGPT’s massive audience.”

Robotics

Wing’s Drone Delivery Is Coming to 150 More WalmartsAndrew J. Hawkins | The Verge

“So far, they’ve launched at several stores in Atlanta, in addition to Walmart locations in Dallas-Forth Worth and Arkansas. They currently operate at approximately 27 stores, and with today’s announcement, the goal is to eventually establish a network of 270 Walmart locations with Wing drone delivery by 2027.”

Computing

OpenAI Forges Multibillion-Dollar Computing Partnership With CerebrasKate Clark and Berber Jin | The Wall Street Journal ($)

“OpenAI plans to use chips designed by Cerebras to power its popular chatbot, the companies said Wednesday. It has committed to purchase up to 750 megawatts of computing power over three years from Cerebras. The deal is worth more than $10 billion, according to people familiar with the matter.”

Space

China Just Built Its Own Time System for the MoonPassant Rabie | Gizmodo

“As the global race to build a human habitat on the Moon heats up, there are several ongoing attempts to establish a universal lunar time that future missions can rely on. China, however, claims to be the first to set its lunar clocks and has made its new tool publicly available for use.”

The post This Week’s Awesome Tech Stories From Around the Web (Through January 17) appeared first on SingularityHub.

When you click on the Spotify profile of Intelligent Band Machine you will see an image of three young men staring moodily back into the camera. Their profile confirms that they are a “British band,” “influenced by the post-punk scene,” and trying to capture the spirit of bands like The Cure “while carving out their own unique sound.” When you listen to their music you might be reminded of Joy Division’s Ian Curtis.

If you dig a little deeper and read about them on their record label’s page you will find that Cameron is the lead singer, and his musical tastes were shaped by the concerts he attended at Nottingham’s Rock City nightclub. Tyler, the drummer, was indeed inspired by The Cure, as well as U2, and The Smiths, while guitarist, Antonio, blends his Italian mother’s love of classic Italian folk songs with his British father’s passion for The Beatles and The Rolling Stones.

What these profiles don’t say is that Intelligent Band Machine is not real, at least not in the human sense. And I should know, because I created them.

I used a range of generative artificial intelligence (GenAI) tools, as well as my skills as a professional songwriter and sound engineer to make their debut album, “Welcome to NTU,” and I released it on my dedicated AI record label, XRMeta Records, in May 2025.

You might ask why an independently releasing singer-songwriter and music producer like me would create an artificial band. As well as being a musician, I’m an academic with a background in computer science, carrying out research about how GenAI can be used for music.

I had reservations about these tools and how they might affect me as a musician. I had heard about various AI controversies, like “fake” Drake, and artists like Grimes embracing GenAI in 2023. So, I was also intrigued by the possibilities.

Over 100 million people have tried Suno, an AI music generation platform that can create songs with vocals and instrumentation from simple text prompts. More than 100 million tracks have been created using the Mubert API, which allows streaming to platforms like YouTube, TikTok, Twitch, and Instagram. And according to Deezer, 28 percent of released music is fully AI-generated.

It was time for me to investigate what these tools could do. This is the story of how I experimented with GenAI and was transformed from a dance artist to a post-punk soft rock band.

GenAI Has Changed Everything

In my early days of songwriting, one of the first pieces of equipment I bought was a Panasonic RQ-2745, a small, slim portable cassette tape recorder that allowed me to record rough drafts of vocals on an audio cassette tape.

When cheap products like the Sony cfs-w30 boombox began to incorporate double cassette decks, I could overdub songs and add choruses or instruments, like flute or guitar, at home. If I wanted a quality recording, I had to book a recording studio. I became an expert at splicing tape to remove vocal parts from the tape recording or to fix tape jams.

Cutting and taping became cutting and pasting as I experimented with the very early free digital music sequencers that were included on a disk I found on the cover of a PC magazine. I felt liberated when sequencers like Cubase, Pro Tools, and Logic allowed high-quality recordings to be produced at home. This, along with the significant reduction in the cost of studio equipment, led to the emergence of the bedroom producer and the proliferation of the 808 sound. This deep, booming bass line can be heard in hits like “It’s Tricky” by RUN DMC, “Emergency Room” by Rihanna, and “Drunk in Love” by Beyoncé.

Digital distribution and social media then paved the way for self-releasing independent artists like me to communicate directly with fans, sell music, and bypass record labels.

Yet during all of these changes, musicians still needed the skills and knowledge to create their songs. Like many musicians, I honed my skills over several years, learning to play the guitar, flute, and piano and developing sound-engineering skills. Even when AI-powered tools began to be incorporated into digital audio workstations, a musician’s skill and knowledge was still needed to use these tools effectively.

Being able to create music from text prompts changed this.

Not since the introduction of music streaming services in the late 1990s has there been such a dramatic shift in music composition and listening technologies. Now non-musicians can create studio-quality music in minutes without the extensive training that I had and without having to buy instruments or studio equipment.

Now anyone can do this. It was time for me to learn what these tools could do.

I typically produce RnB/neo soul, nu-jazz, and dance music, although I can write songs for multiple genres of music. For the experiment, I wanted to try a genre that I do not usually produce music for.

I tested about 60 different GenAI tools and platforms. These included standalone tools that focus on one task, like MIDI generation (musical data that can be played back on a keyboard or music sequencer). I also tried AI music studios. These platforms have user-friendly interfaces that combine a range of AI tools to support lyric, music, image, and video creation.

Suno and Udio were two of the best platforms. They can generate songs with complex vocal melodies and harmonies across a range of genres, with the best outputs being difficult to distinguish from what human musicians can create. Both Telisha “Nikki” Jones and music mogul Timbaland are said to have used Suno to create music for their AI-generated artists.

In June 2025, Timbaland announced the signing of his AI artist TaTa to his dedicated AI record label, Stage Zero. In September 2025 Jones was reported to have signed a $3 million deal with Hallwood Media for her AI-generated artist Xania Monet.

At the time of my experiment in March/April 2025, both Suno and Udio had issues, such as silence gaps, tempo changes, inconsistent vocal quality, and variations in genre. Sometimes the voice might change within the song. There was limited control in terms of editing, and the audio quality could vary within a single track or across a series of songs.

After trying several GenAI music platforms I decided to use Udio due to the quality of its output and its favorable terms and conditions at that time. Taking inspiration from pop-rock and post-punk bands like Joy Division and The Cure, I started the journey towards creating a new persona.

Using GenAI to produce one or two good songs was quite simple. Producing an album of 14 songs that sounded as if they were played by the same band was more challenging, particularly generating the same male voice and musical style for each song.

The songs were either far too similar to each other or had other issues, such as the voice changing or the instruments sounding too different. A careful listen to the songs in Unfolded by the AI artist Xania Monet will reveal similar inconsistencies. For example, you can hear a difference in the voice that is generated for the first song, “This Aint No Tryout,” compared to “Back When Love Was Real.”

GenAI Can’t Write (Decent) Lyrics

My first task was to create the lyrics. I generated about 1,000 songs using Udio and found repeated words and phrases in the lyrics like “neon,” “whisper,” and “we are, we are, we are,” appearing both within and across the two user accounts I created. Themes like darkness, shadows, and light were also repeated within the lyrics for a significant number of songs.

GenAI just couldn’t write lyrics with the complexity or playfulness I needed, so I chose to write the lyrics for the album myself and used a semi-autobiographical narrative. This allowed me to maintain a story across the album; from arriving at Nottingham Trent University and settling into student accommodation to experiencing university life, graduating and leaving.

I could interweave current affairs like the closure of Nottingham’s Victoria Centre Market in the song “Goodbye Vicky Market.” I included lines that referenced Nottingham’s historical figures like Alan Sillitoe, who wrote “The Loneliness of the Long Distance Runner,” and the author D.H. Lawrence in the song, “Books.”

After writing the lyrics I generated the music. There were issues with prompt adherence. I tested prompts of different lengths. In some cases, prompts were partly or wholly ignored. I might write a prompt asking for one genre and a different genre would be produced.

There were also issues with the synthetic voice pronouncing some of the lyrics. For example, it could not pronounce “NTU” or “Sillitoe,” and I had to rewrite some of the lyrics phonetically or edit the audio to get the correct pronunciation for certain words.

I relied on my sound engineering skills, extending the outputs, editing, mixing, remixing, and manually recording vocals in Cubase to achieve a coherent final mix. This took a significant amount of time. In fact, editing the Udio outputs took so much time, it would have been easier to recreate the music myself. I can write a song in 10 minutes, and I sometimes record myself freestyling lyrics for an entire song directly in Cubase, so this was frustrating.

I encountered similar issues with prompt adherence when generating images and video. When using Kling AI to create images of the band members, I followed its prompt engineering guide. However, I had to generate hundreds of images and edit them with external tools to achieve the final band photos.

Generating video was equally tricky. One way to create a video is to upload a photo, which becomes the first frame. The rest of the video is generated based on the prompt. However, when I uploaded Cameron’s profile image to Kling AI, the initial frames of the 10-second video resembled him. But by the end of the video, Cameron often morphed into someone else, and this happened frequently when generating video.

Prompts for camera instructions, such as zoom and pan, were frequently ignored. I also had to edit out scenes with other problems, such as the appearance of extra fingers or an additional leg on the band members.

All this wasn’t cheap either. With 8,000 Kling AI credits at a cost of $64.99, I could generate about 40 ten-second videos, but many were unusable.

Music generation is cheaper. Paying between $24 and $30 for a monthly subscription might allow a user to create between 2,000 and 3,000 songs, depending on how the credits are used. I was very surprised to discover how quickly these song credits can be consumed. Every error or song that didn’t suit my taste still cost credits.

Eventually, after generating thousands of songs, hundreds of images and videos using tools like Duck.ai to create the band’s biographies, and spending many hours editing the outputs; Cameron, Tyler ,and Antonio began to emerge as the band.

AI-generated band Intelligent Band Machine: Antonio, Cameron, and Tyler. Image Credit: Priscilla Angelique-Page Something Unexpected Happened

I have always been passionate about creating my own music. As much as I love writing songs, the poor royalty payouts I was receiving had become disheartening. A song I recorded in 2001 and released in 2011 called Only Heaven Can Compare was streamed about 1 million times in France during 2024 but I only received about £21 in royalties.

Prior to streaming, had my song been downloaded by just 10,000 people, I would have been paid about £6,900 (69 pence per download). Artists like Kate Nash have raised concerns about the poor royalty payouts to musicians, citing her £500,000 payout for over 100 million plays of her song “Foundations.”

But as I created the band’s album something unexpected started to happen. I began to enjoy creating music again. The frustrations with using GenAI was balanced by wonder and curiosity.

At times Udio was able to generate vocals that were so realistic I could hardly believe they were created by an AI model. There were moments when I laughed, when I was really moved, and even had chills when I heard some of the songs.

Lyrics that once lay dormant in multiple lever arch files on my bookshelf began to find new life through these generative tools, allowing me to rapidly test them across multiple genres.

I decided to take this experiment further.

After carefully selecting a set of songs I had written many years ago, I created a new persona, Jake Davy Smith. For his 14-track album, called I’ll Be Right Here, which was released on November 22, 2025, I used Suno’s v5 model to generate studio-quality music that matched my original vision.

Suno’s extensive editing tools allow users to upload vocals, create a cover song, and edit the music, lyrics, or voice with greater precision than their earlier models. This helped me nearly recreate my original songs. The track “Calling” is an example of a rock ballad I wrote years ago, recorded, and didn’t release.

Conflicting Emotions

Reflecting on this experiment, I found myself with conflicting views about using GenAI. These tools are fast and affordable (in some cases, completely free). They can produce instant results. I now have tools that I can use to quickly reimagine my old songs.

I can use multiple personas to bring my lyrics to life. I am Priscilla Angelique. I am Intelligent Band Machine. I am Jake Davy Smith. I am Moombahtman 25, a male African American moombahton artist who combines hip hop with Latin American beats, and I have many more personas.

I am a “multiple persona musician” or MPM, a term I’ve created to define my new musical identity. Musicians having alter egos isn’t new, but GenAI has completely changed how this is done.

However, there’s another side to this. Human musicians are now having to compete with algorithms capable of producing high-quality music at scale—as well as with each other.

These tools are improving rapidly, and the issues I experienced when using Udio to create the album for Intelligent Band Machine in March/April 2025 have already been addressed in Suno’s v5 model. It is now easier to create a persona with a consistent voice. Users can upload their own songs and also create cover versions of their songs.

Creating the album for Intelligent Band Machine took about one month and there were multiple issues with trying to create consistently sounding high-quality, AI-generated songs. I spent hours reviewing thousands of outputs and then more time editing the final set of curated songs in Cubase.

My experience was very different when I created the album for Jake Davy Smith. I used lyrics I had already written, generated between five and 20 versions of each song, and spent far less time editing them. The process was faster, however, there were still some issues. Changes in Jake’s voice occurred, though they were less frequent and easier to correct. There were also problems with pronunciation, but I could now quickly regenerate the audio. In essence, what had previously taken a month now took only a week.

Ethical Issues and Data Collection

Yet beneath this lies a further internal conflict related to the data used to train these AI models or, as music journalist Richard Smirke describes it, “the largest IP theft in human history.” It is this issue that has made a technology that ought to have been celebrated as one of the biggest technological achievements in decades, one of the most contested instead.

Chatbots like ChatGPT, estimated to have nearly 1 billion users worldwide, have been described by the linguist and activist Noam Chomsky as both “marvels of machine learning” and the “banality of evil.” Image generators like OpenAI’s DALL-E have also come under fire. Critics like Ted Chiang challenge whether AI can make art and other commentators have criticized the lack of cultural diversity in image generation.

In addition to this, in 2024 the UK government announced it was considering an exception to copyright law that would allow industry to use copyrighted works for AI training without compensating the creators. This led to protests. More than 1,000 musicians released a silent album called Is This What We Want in protest against unauthorized AI training. The artists included Kate Bush, Annie Lennox, Damon Albarn, and The Clash.

Elton John and Paul McCartney also voiced their opposition to changes in copyright law that would benefit AI companies. The mystery about whether a band called The Velvet Sundown was AI-generated added fuel to the fire and sparked further debate during the summer of 2025.

Yet AI companies have been winning, or at least partially winning, court cases. In November 2025, Getty Images “lost its claim for secondary infringement of copyright” against Stability AI. Other AI companies are making deals, and this includes Udio and Suno’s recent deals with music companies. However, more alternative platforms are emerging. Klay.vision is negotiating with the big labels prior to launching, and Soundraw only uses music created in-house for AI training.

So GenAI is here to stay, and musicians will need to adapt. Library music, background music, and music for social media or film can easily be created with AI. However, there are risks. The risk that similar music may be generated for other users; the risk that any uploaded songs may be used for training data. Then there’s the risk that these tools may inadvertently generate something that breaches someone else’s IP.

One way for musicians to safely use GenAI is by training models using their own data, as YACHT did when they used their back catalog of songs as training data for a new album. In this way musicians can have full control over the outputs. This is something I will be exploring for the next stage of my research.

What AI Can’t Do

My transformation has been anything but straightforward. It has been marked by the deep frustration I encountered when initially using these tools, an ongoing conflict about how these tools are trained, and moments of genuine amazement. The albums I created may be imperfect, but they are a clear departure from my usual style and show how GenAI can support musical creativity.

Priscilla Angelique-Page aka Priscilla Angelique, wearing an AI-generated jacket and shirt. Image Credit: Priscilla Angelique-Page

Financially, the albums are unlikely to recoup the cost of creating them, as independent musicians may need hundreds of millions of streams to earn a decent income from music. Even a few million streams of the songs will barely cover the various fees for music, image, and video generation of around £140. Merchandise, licensing, sync deals, and other revenue streams will likely remain important sources of income for musicians, whether they are human or AI-generated.

On the legal side, one possible way forward is for AI companies to make open-source versions of their models freely available for offline use. Some already have, but for those that haven’t, it seems fair that if they have used our data to build these systems, they should allow broader access to the models themselves.

New technologies might change how music is produced. We have gone from clapping to drumming and from using drum machines in recording studios to generating “new” sounds with AI. Yet now that I have completed these experiments, I realize that one thing remains the same.

Whether I am cutting tape using scissors, cutting and pasting in a sequencer like Cubase, or regenerating parts in an AI music studio like Suno, human creativity is still an essential part of the process. Using GenAI was transformative, yet it was my creative decisions that shaped the songs, the albums, the avatars for my personas, their biographies, and the overall vision. This is something that AI cannot do – at least, not for now.

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

The post How I Used AI to Transform Myself From a Female Dance Artist to an All-Male Post-Punk Band appeared first on SingularityHub.

A small trial is testing a decades-old method that transforms cancer cells into a “vaccine” to prevent recurrence.

Lights, vitamin, action. A combination of vitamin B2 and ultraviolet light hardly sounds like a next-generation cancer treatment. But a small new trial is testing the duo in people with recurrent ovarian cancer.

Led by PhotonPharma, this first-in-humans study builds on decades of work investigating whether we can turn whole cancer cells into “vaccines.” Isolated from cancer patients, the cells are stripped of the ability to multiply but keep all other protein signals intact. Once reinfused, the cells can, in theory, alert the immune system that something is awry.

Known as the Mirasol process, the approach was originally used to neutralize bacteria, viruses, and other pathogens in donated blood or plasma (the yellowish liquid part of blood). But a chance discovery led Ray Goodrich at Colorado State University to re-envision the method as a cancer treatment. White blood cells, he noticed, retained their structure but were inactivated after going through the process, turning into “zombies” that couldn’t divide or function.

Cancer cells disabled in the same way could train the immune system to recognize and attack the real tumor. Researchers pursuing the idea have already used it to keep recurrent cancers at bay in mice and dogs. Encouraged, Goodrich, who helped develop the Mirasol process, co-founded PhotonPharma to bring the treatment to people.

To be clear, the method doesn’t prevent cancer. It’s designed to keep pesky cancer cells from returning. The trial is set to begin early this year at City of Hope, a national cancer research and treatment center, in six patients to gauge safety.

“We are thrilled to have reached this pivotal moment in our journey toward providing a novel treatment option for patients facing advanced ovarian cancer,” said Goodrich in a press release after being given the green light by the FDA to conduct the trial.

A Long Journey

Cancer immunotherapies have come a long way. The blockbuster CAR T cell therapy, for example, has transformed treatment for deadly blood cancers. Here, T cells are removed from the patient and genetically engineered to more effectively recognize cancers. When infused back into the body, these enhanced cells can better hunt down and destroy their targets.

CAR T is a breakthrough, but it’s tough on an already fragile body. Patients undergo a short bout of chemotherapy or radiation to remove existing T cells. This step makes space for the newly engineered cells and reduces competition with existing immune cells. Scientists are working on ways to skip the process, such as a gene-editing injection that transforms T cells inside the body. But even these newer methods still struggle to tackle solid tumors, including ovarian cancer.

Cancer vaccines are also on the rise thanks to mRNA technology. Like Covid vaccines, these shots wake up a sluggish immune system and help immune cells penetrate the formidable barriers surrounding cancer cells.

The Mirasol process is different. Before boosting the immune system, it first neuters cancer cells.

Goodrich began working to purify donated blood during the HIV epidemic in the late 1980s, when contamination was a big concern. It took a decade, but he eventually found a curious duo: Vitamin B2, or riboflavin, and ultraviolet (UV) light. Riboflavin latches onto DNA and RNA molecules, and a blast of UV light damages the genetic material in pathogens, rendering them incapable of growth and reproduction. This essentially cleanses the blood. The process can be conducted in a machine the size of desktop printer and is approved in multiple countries in Europe and Canada.

But the technology is more sledgehammer than scalpel. It also damages red blood cells, platelets, and plasma proteins. To his surprise, however, Goodrich noticed some white blood cells remained intact, though they could no longer replicate. It was this observation that led him to test the procedure on cancer cells.

Using whole cancer cells as vaccines isn’t a new idea. For decades, scientists have tried to use patient’s own tumors to ignite the immune system, often incapacitating them with radiation. Some tests made it to clinical trials for prostate cancers and other solid tumors. But the overall immune response was weak, and the idea soon lost support.

Still, whole cancer cells do have a leg up compared to current immunotherapies. Such treatments use only a small handful of proteins, called neoantigens, to help the immune system identify cancer cells. With Mirasol, the inactivated cancer cells retain all their neoantigens. In theory, this could more effectively alert the immune system: More danger signals should provoke a fiercer response. Using whole cancer cells also means scientists don’t need to spend time figuring out the best neoantigens to include in their treatment.

Unlike CAR T, the inactivated cancer cells can be infused back into patients without radiation or chemotherapy conditioning. And because they can no longer divide, there’s little chance of them growing into new tumors.

Early trials in dogs with solid tumors were promising. In an 11-year-old goldendoodle named Ella, cancer of the liver had spread into surrounding healthy tissue. This made it difficult to completely remove. Rather than resorting to chemotherapy, her owner opted for Goodrich’s experimental process. After treatment, the cancer showed no signs of returning for three years, at which point she passed from health issues unrelated to cancer.

The new trial will treat people with recurrent, advanced ovarian cancer and no other options.

“Traditionally, it’s been chemotherapy, but in 80 percent of those patients who get surgery and chemotherapy, it comes back…we know it’s really not curable at that point,” Mihae Song, a gynecologic oncologist at City of Hope, told The Colorado Sun.

The trial will primarily measure safety. The team will remove cancer cells from patients, inactivate them with UV light and riboflavin, and add several molecules commonly used in vaccines to boost the immune response. Each person will receive three doses of the cells, as the team watches out for side effects and gauges the response.

If it works, Goodrich and team want to recruit more participants and potentially expand the treatment to other types of solid tumors. But success isn’t guaranteed. Tumors eventually suppress immune attacks, and it’s unclear if UV-treated decoys can restore immune cell vigor.

Still, the trial offers an alternative way to tackle stubborn cancers. “You would always hope, but I think it’s too optimistic to think that it completely cures it,” Goodrich told The Colorado Sun. “But it’s maybe something that could add years of life, of good-quality life, onto someone’s lifespan who is diagnosed with this, because, again, the prognosis right now is not very good.”

The post In First Human Trial, Zombie Cancer Cells Train the Body to Fight Tumors appeared first on SingularityHub.

“Generative biology is moving drug discovery from a process of chance to one of design.”

Antibodies touch nearly every corner of healthcare. These carefully crafted proteins can target cancer cells, control autoimmune diseases, fight infections, and destroy the toxic proteins that drive neurological disorders. They’re also notoriously difficult to make.

Over 160 antibody therapies have been approved globally. Their market value is expected to reach $445 billion in the next five years. But the traditional design process takes years of trial and error and is often constrained to structures similar to existing proteins.

With AI, however, we can now generate completely new antibody designs—never before seen in nature—from scratch. Last year, labs and commercial companies raced to build increasingly sophisticated algorithms to predict and generate these therapeutics. While some tools are proprietary, many are open source, allowing researchers to tailor them to a specific project.

Some AI-optimized antibodies are already in early clinical trials. In late September, Generate:Biomedicines in Somerville, Massachusetts presented promising data from patients with asthma treated with an antibody designed with AI’s help. A shot every six months lowered asthma-triggering protein levels without notable side effects.

“Generative biology is moving drug discovery from a process of chance to one of design,” said Mike Nally, CEO of Generate, in a press release.

Nobel Prize winner David Baker at the University of Washington would likely agree. Known for his work on protein structure prediction and design, his team upgraded an AI last year to dream up antibodies for any target at the atomic level.

Designer Troubles

Pills containing small-molecule drugs like Tylenol still dominate healthcare. But antibody therapies are catching up. These therapies work by grabbing onto a given protein, like a key fitting into a lock. The interaction then either activates or inhibits the target.

Antibodies come in different shapes and sizes. Monoclonal antibodies, for example, are lab-made proteins that precisely dock to a single biological target, such as one involved in the growth or spread of cancer. Nanobodies, true to their name, are smaller but pack a similar punch. The FDA has approved one treatment based on the technology for a blood clotting disorder.

Regardless of type, however, antibody treatments traditionally start from similar sources. Researchers usually engineer them by vaccinating animals, screening antibody libraries, or isolating them from people. Laborious optimization procedures follow, such as mapping the exact structure of the binding pocket on the target—the lock—and tweaking the antibody key.

The process is tedious and unpredictable. Many attempts fail to find antibodies that reliably scout out their intended docking site. It’s also largely based on variations of existing proteins that may not have the best therapeutic response or safety profile. Candidates are then painstakingly optimized using iterations of computational design and lab validation.

The rise of AI that can model protein structures—and their interactions with other molecules—as well as AI that generates proteins from scratch has sparked new vigor in the field. These models are similar to those powering the AI chatbots that have taken the world by storm for their uncanny ability to dream up (sometimes bizarre) text, images, and video.

In a way, antibody structures can be represented as 3D images, and their molecular building blocks as text. Training a generative AI on this data can yield an algorithm that produces completely new designs. Rather than depending on chance, it may be possible to rationally design the molecules for any given protein lock—including those once deemed “undruggable.”

But biology is complex. Even the most thoughtful designs could fail in the body, unable to grasp their target or latching onto unintended targets, leading to side effects. Antibodies rely on a flexible protein loop to recognize their specific targets, but early AI models, such as DeepMind’s AlphaFold, struggled to map the structure and behavior of these loops.

Designed to Bind

The latest AI is faring better. An upgraded version of Baker lab’s RFdiffusion model, introduced last year, specifically tackles these intricate loops based on information about the structure of the target and location of the binding pocket. Improved prediction quickly led to better designs.

Initially, the AI could only make nanobodies. These are short but functional chunks of antibodies for a range of viruses, such as the flu, and antidotes against deadly snake venoms. After further tweaking, the AI suggested longer, more traditional antibodies against a toxin produced by a type of life-threatening bacteria that often thwarts antibacterial drugs.

Lab tests confirmed that the designer proteins reliably latched onto their targets at commonly used doses without notable off-site interactions.

“Building useful antibodies on a computer has been a holy grail in science. This goal is now shifting from impossible to routine,” said study author Rob Ragotte.

There have been more successes. One lab introduced a generative model that can be fine-tuned using the language of proteins—for example, adding structural constraints of the final product. In a test, the team selected 15 promising AI-made nanobody designs for cancer, infections, and other diseases, and each successfully found its target in living cells. Another lab publicly released an AI called Germinal that’s also focused on making nanobodies from scratch.

Commercial companies are hot on academia’s heels.

Nabla Bio, based in Cambridge, Massachusetts, announced a generative AI-based platform called JAM that can tackle targets previously unreachable by antibodies. One example is a highly complex protein class called G-protein-coupled receptors. These seven-arm molecules form the “largest and most diverse group” of protein receptors embedded in cell membranes. Depending on chemical signals, the receptors trigger myriad cell responses—tweaking gene activation, brain signaling, hormones—but their elaborate structure makes designing antibodies a headache.

With JAM, the company designed antibodies to target these difficult proteins, showcasing the AI’s potential to unlock previously unreachable targets. They’re releasing parts of the data involved in characterized antibodies from the study, but most of the platform is proprietary.

Momentum for clinical trials is also building.

After promising initial results, Generate:Biomedicines launched a large Phase 3 study late last year. The trial involves roughly 1,600 people with severe asthma across the globe and is testing an antibody optimized—not engineered from scratch—with the help of AI.

The hope is AI could eventually take over the entire antibody-design process: predicting target pockets, generating potential candidates, and ranking them for further optimization. Rational design could also lead to antibodies that better navigate the body’s crooks and crannies, including those that can penetrate into the brain.

It’ll be a long journey, and safety is key. Because the dreamed-up proteins are unfamiliar to the body, they could trigger immune attacks.

But ultimately, “AI antibody design will transform the biotechnology and pharmaceutical industries, enabling precise targeting and simpler drug development,” says Baker.

The post AI-Designed Antibodies Are Racing Toward Clinical Trials appeared first on SingularityHub.

The skin could allow machines to dynamically blend into their surroundings or be used to create adaptive displays and artwork.

An octopus’s adaptive camouflage has long inspired materials scientists looking to come up with new cloaking technologies. Now researchers have created a synthetic “skin” that independently shifts its surface patterns and colors like these intelligent invertebrates.

The ability to alter an object’s appearance on demand has a host of applications, from allowing machines to dynamically blend into their surroundings to creating adaptive displays and artwork. Octopuses are an obvious source of inspiration thanks to their unique ability to change the color and physical structure of their skin in just seconds.

So far, however, materials scientists have struggled to replicate this dual control. Materials that change color typically use nanostructures to reflect light in specific ways. But changing a surface’s shape interferes with these interactions, making it challenging to tune both properties simultaneously.

Now, in a paper published in Nature, Stanford University researchers cracked the problem by creating a synthetic skin made of two independently controlled polymer layers: One changes color and the other shape.

“For the first time, we can mimic key aspects of octopus, cuttlefish, and squid camouflage in different environments: namely, controlling complex, natural-looking textures and at the same time, changing independent patterns of color,” Siddharth Doshi, first author of the paper, told The Financial Times.

The new camouflage system took direct inspiration from cephalopods, which use tiny muscle-controlled structures called papillae to reshape their skin’s surface while separate pigment cells alter color.

To recreate these abilities, the researchers turned to a polymer called PEDOT:PSS, which swells when it absorbs water. The team used electron-beam lithography—a technology typically used to etch patterns into computer chips—to control how much different areas of the polymer swell when exposed to liquid.

The team covered one layer of the polymer in a single layer of gold to create textures that switch between a shiny and matte appearance. They then sandwiched another layer of the polymer between two layers of gold, creating an optical cavity that could be used to generate a wide variety of colors as the distance between the gold sheets changes.

The researchers can create four distinct visual states—texture combined with a color pattern, texture only, color only, and no texture or color pattern—by exposing each side of the skin to either water or isopropyl alcohol. The system switches between states in about 20 seconds, and the process is fully reversible.

“By dynamically controlling the thickness and topography of a polymer film, you can realize a very large variety of beautiful colors and textures,” Mark Brongersma, a senior author on the paper, said in a press release. “The introduction of soft materials that can expand, contract, and alter their shape opens up an entirely new toolbox in the world of optics to manipulate how things look.”

Applications could extend beyond camouflage the researchers say—for instance using texture changes to control whether small robots cling to or slide across surfaces or creating advanced displays for wearable devices or art projects.

The current need to apply water to control the appearance of the skin is “a huge limitation,” Debashis Chanda, a physicist at the University of Central Florida, told Nature. But the researchers told the Financial Times they plan to introduce digital control systems to future versions of the skin.

They also hope to add computer vision algorithms to provide information about the surrounding environment the skin needs to blend in with. “We want to be able to control this with neural networks—basically an AI-based system—that could compare the skin and its background, then automatically modulate it to match in real time, without human intervention,” Doshi said in the press release.

While the research faces a long road from the lab bench to commercial reality, sci-fi style cloaking technology has taken a tiny step closer to reality.

The post Sci-Fi Cloaking Technology Takes a Step Closer to Reality With Synthetic Skin Like an Octopus appeared first on SingularityHub.

Robotics

Google Gemini Is Taking Control of Humanoid Robots on Auto Factory FloorsWill Knight | Wired ($)

“Google DeepMind is teaming up with Boston Dynamics to give its humanoid robots the intelligence required to navigate unfamiliar environments and identify and manipulate objects—precisely the kinds of capabilities needed to perform manual labor.”

Artificial Intelligence

Distinct AI Models Seem to Converge on How They Encode RealityBen Brubaker | Quanta Magazine

“Is the inside of a vision model at all like a language model? Researchers argue that as the models grow more powerful, they may be converging toward a singular ‘Platonic’ way to represent the world.”

BIOTECHNOLOGY

Flu Is Relentless. CRISPR Might Be Able to Shut It Down.
David Cox | Wired ($)

“They believe CRISPR could be tailored to create a next-generation treatment for influenza, whether that’s the seasonal strains which plague both the northern and southern hemispheres on an annual basis or the worrisome new variants in birds and other wildlife that might trigger the next pandemic.”

Computing

Next-Level Quantum Computers Will Almost Be UsefulDina Genkina | IEEE Spectrum

“The machine that Microsoft and Atom Computing will be delivering, called Magne, will have 50 logical qubits, built from some 1,200 physical qubits, and should be operational by the start of 2027. QuEra’s machine at AIST has around 37 logical qubits (depending on implementation) and 260 physical qubits, Boger says.”

ARTIFICIAL INTELLIGENCE

AI Coding Assistants Are Getting Worse
Jamie Twiss | IEEE Spectrum

“In recent months, I’ve noticed a troubling trend with AI coding assistants. After two years of steady improvements, over the course of 2025, most of the core models reached a quality plateau, and more recently, seem to be in decline. A task that might have taken five hours assisted by AI, and perhaps ten hours without it, is now more commonly taking seven or eight hours, or even longer.”

ENERGY

Meta Unveils Sweeping Nuclear-Power Plan to Fuel Its AI Ambitions
Jennifer Hiller | The Wall Street Journal ($)

“Meta Platforms on Friday unveiled a series of agreements that would make it an anchor customer for new and existing nuclear power in the US, where it needs city-size amounts of electricity for its artificial-intelligence data centers. …Financial details weren’t disclosed, but the arrangements are among the most sweeping and ambitious so far between tech companies and nuclear-power providers.”

Robotics

Even the Companies Making Humanoid Robots Think They’re OverhypedSean McLain | The Wall Street Journal ($)

“Billions of dollars are flowing into humanoid robot startups, as investors bet that the industry will soon put humanlike machines in warehouses, factories and our living rooms. For all the recent advances in the field, humanoid robots, they say, have been overhyped and face daunting technical challenges before they move from science experiments to a replacement for human workers.”

SPACE

Former Google CEO Plans to Singlehandedly Fund a Hubble Telescope Replacement
Eric Berger | Ars Technica

“On Wednesday evening, former Google CEO Eric Schmidt and his wife, Wendy, announced a major investment in not just one telescope project, but four. Each of these new telescopes brings a novel capability online; however, the most intriguing new instrument is a space-based telescope named Lazuli. This spacecraft, if successfully launched and deployed, would offer astronomers a more capable and modern version of the Hubble Space Telescope, which is now three decades old.”

Robotics

Uber’s Not Done With Self-Driving Cars Just Yet. It’s Designing a New Robotaxi With Lucid and NuroSasha Lekach | Gizmodo

“The companies said that on-road testing [in San Francisco] started at the end of last year, which isn’t surprising as Nuro already holds driverless testing permits through the California DMV. Eventually, the trio plan to offer the Level 4 robotaxi prototype everywhere Uber has a presence—if all goes well, that is.”

Robotics

Kawasaki’s Four-Legged Robot-Horse Vehicle Is Going Into ProductionBronwyn Thompson | New Atlas

“What was announced as a 2050 pipe dream by Kawasaki, the company’s hydrogen-powered, four-hooved, all-terrain robot horse vehicle Corleo is actually going into production and is now expected to be commercially available decades earlier—with the first model to debut in just four years.”

Space

NASA’s Science Budget Won’t Be a Train Wreck After AllEric Berger | Ars Technica

“On Monday, Congress made good on…promises [to fund most of NASA’s science portfolio], releasing a $24.4 billion budget plan for NASA as part of the conferencing process, when House and Senate lawmakers convene to hammer out a final budget. The result is a budget that calls for just a 1 percent cut in NASA’s science funding, to $7.25 billion, for fiscal year 2026.”

Artificial Intelligence

AI Is Being Used to Find Valuable Commodities in Our TrashRyan Dezember | The Wall Street Journal ($)

“Murphy Road executives say the technology allows them to sort up to 60 tons an hour of curbside recycling from around Connecticut and western Massachusetts into precisely sorted bales of paper, plastic, aluminum cans, and other materials. The material is sold to mills, manufacturers, and remelt facilities, which pay more for cleaner bales.”

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

There’s a case to be made that Earth’s life arrived on meteorites from Mars. Here’s the evidence for and against.

How did life begin on Earth? While scientists have theories, they don’t yet fully understand the precise chemical steps that led to biology or when the first primitive life forms appeared.

But what if Earth’s life did not originate here, instead arriving on meteorites from Mars? It’s not the most favored theory for life’s origins, but it remains an intriguing hypothesis. Here, we’ll examine the evidence for and against.

Timing is a key factor. Mars formed around 4.6 billion years ago, while Earth is slightly younger at 4.54 billion years old. The surfaces of both planets were initially molten, before gradually cooling and hardening.

Life could, in theory, have arisen independently on both Earth and Mars shortly after formation. While the surface of Mars today is probably uninhabitable for life as we know it, early Mars probably had similar conditions to the early Earth.

Early Mars seems to have had a protective atmosphere and liquid water in the form of oceans, rivers, and lakes. It may also have been geothermally active, with plenty of hydrothermal vents and hot springs to provide the necessary conditions for the emergence of life.

However, about 4.51 billion years ago, a Mars-sized, rocky planet called Theia crashed into the proto-Earth. This impact caused both bodies to melt together and then separate into our Earth and its moon. If life had begun before this event, it certainly would not have survived it.

Mars, on the other hand, probably didn’t experience a global remelting event. The red planet had its fair share of impacts in the violent early solar system, but evidence suggests that none of these would have been large enough to completely destroy the planet—and some areas could have remained relatively stable.

So if life arose on Mars shortly after formation of the planet 4.6 billion years ago, it could have continued evolving without major interruptions for at least half a billion years. After this time, Mars’ magnetic field collapsed, marking the beginning of the end for Martian habitability. The protective atmosphere disappeared, leaving the planet’s surface exposed to freezing temperatures and ionizing radiation from space.

A Question of Timing

But what of Earth: How soon did life appear after the impact that formed the moon? Tracing the tree of life back to its root leads to a microorganism called Luca—the last universal common ancestor. This is the microbial species from which all life today is descended. A recent study reconstructed Luca’s characteristics using genetics and the fossil record of early life on Earth. It inferred that Luca lived 4.2 billion years ago—earlier than some previous estimates.

Luca was not the earliest organism on Earth, but one of multiple species of microbe existing in tandem on our planet at this time. They were competing, cooperating, and surviving the elements, as well as fending off attacks from viruses.

If small but fairly complex ecosystems were present on Earth around 4.2 billion years ago, life must have originated earlier. But how much earlier? The new estimate for the age of Luca is 360 million years after the formation of the Earth and 290 million years after the moon-forming impact. All we know is that in these 290 million years, chemistry somehow became biology. Was this enough time for life to originate on Earth and then diversify into the ecosystems present when Luca was alive?

Luca’s habitat was either a shallow marine hydrothermal vent system or a geothermal hot spring, like this spectacular example in Yellowstone, US. Image Credit: NPS/Diane Renkin

A Martian origin for terrestrial life circumvents this question. According to the hypothesis, species of Martian microorganism could have traveled to Earth on meteorites just in time to take advantage of the clement conditions following the moon’s formation.

The timing may be convenient for this idea. However, as someone who works in the field, my hunch would be that 290 million years is plenty of time for chemical reactions to produce the first living organisms on Earth and for biology to subsequently diversify and become more complex.

Surviving the Journey

Luca’s reconstructed genome suggests that it could live off molecular hydrogen or simple organic molecules as food sources. Along with other evidence, this suggests that Luca’s habitat was either a shallow marine hydrothermal vent system or a geothermal hot spring. Current thought in the origin of life field is that these kinds of environments on the early Earth had the necessary conditions for life to emerge from non-living chemistry.

Luca also contained biochemical machinery that could protect it from high temperatures and UV radiation—real dangers in these early Earth environments.

However, it’s far from certain that early life forms could have survived the journey from Mars to Earth. And there’s nothing in Luca’s genome to suggest that it was particularly well adapted to space flight.

In order to have made it to Earth, microorganisms would need to have survived the initial impact on Mars’ surface, a high-speed ejection from the Martian atmosphere, and travel through the vacuum of space while being bombarded by cosmic rays for at least the best part of a year.

They would then have needed to survive the high-temperature entry through Earth’s atmosphere and another impact onto the surface. This last event may or may not have deposited it in an environment to which it was even remotely adapted.

The chances of all of this seem pretty slim to me. However difficult the transition from chemistry to biology may appear, it seems far easier to me than the idea that this transition would occur on Mars, with life forms surviving the journey to Earth, and then adapting to a completely new planet. However, I could be wrong.

It’s useful to look at studies of whether microorganisms could survive the journey between planets. So far, it looks like only the hardiest microorganisms could survive the journey between Mars and Earth. These are species adapted to preventing damage from radiation and capable of surviving desiccation through the formation of spores.

But maybe, just maybe, if a population of microorganisms were trapped in the interior of a sufficiently large meteorite, they could be protected from most of the harsh conditions of space. Some computer simulations even support this idea. Further simulations and laboratory experiments to test this are ongoing.

This raises another question—if life made it from Mars to Earth within the first 500 million years of our solar system’s existence, why hasn’t it spread from Earth to the rest of the solar system in the following four billion years? Maybe we’re not the Martians after all.

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

The post What If We’re All Martians? The Intriguing Idea That Life on Earth Began on the Red Planet appeared first on SingularityHub.

The treatment converted the liver into an immune cell “nursery” that pumped out greater numbers of healthy T cells.

Our immune system is a fierce brigade. Roaming immune cells scan for bacteria, viruses, and other invaders. They also communicate with tissues to catch early signs of cancer. After detecting a threat, the immune system kickstarts formidable defenses to snuff it out.

But our immunity loses power as we age. Immune cells dwindle, and those that remain struggle to perform their usual roles. As a result, immune defenses weaken, increasing the chances of infection and cancer. This also makes vaccines less effective in older adults.

Now, a new treatment using mRNA technology similar to that in Covid vaccines rejuvenated the immune systems of old mice with twice-weekly shots. The injections transformed the liver into a temporary nursery to boost the numbers and health of key immune cells.

Treated mice, aged the human-equivalent of their early 60s, saw a rapid rise in multiple T cell types after vaccination. They also rallied against tumors with a popular cancer immunotherapy.

Resetting immunity isn’t just about defense. The immune system is intricately tied to the health of other organs. Chronic inflammation steadily rises as we age, wreaking havoc on memory, cognition, and metabolism. It also stiffens tissues in multiple organs, increasing the chances of heart attacks and kidney failure.

“If we can restore something essential like the immune system, hopefully we can help people stay free of disease for a longer span of their life,” study author Feng Zhang at MIT said in a press release.

T Cell Boot Camp

Multiple immune cell types protect our bodies, but T cells are one of the most prominent.

Some T cells seek and destroy virus-infected cells and cancer. Others coordinate immune responses and balance the attack to prevent autoimmune problems or runaway inflammation. Still more “remember” prior threats to trigger a faster immune response when re-exposed.

Despite their wide range, all T cells are born in the bone marrow. Baby T cells then journey to the thymus, a small organ sitting at the top of the heart, where they mature and diversify. In this nursery, the cells learn friend from foe, ensuring they’ll only attack legitimate threats while leaving healthy cells alone. The process is mostly coordinated by cocktails of proteins and other signaling molecules, which direct the fate of immature cells and help them survive.

The aging process gradually degrades the nursery. The thymus shrinks, and much of its working tissue is replaced by fat, leading to a drop in newly minted T cells.

“As we get older, the immune system begins to decline. We wanted to think about how can we maintain this kind of immune protection for a longer period of time, and that’s what led us to think about what we can do to boost immunity,” said study author Mirco Friedrich.

For years scientists have tried to revive the organ. Hormones and immune-related proteins have struggled to bring it back to health. More exotic approaches, such as infusing the blood of young animals, transplanting stem cells, or directly tinkering with blood stem cells have shown some promise but are hard to turn into clinical treatments.

“Much has already been attempted to halt or reverse the age-related involution of the thymus,” said Friedrich. “Unfortunately, without much success so far.”

Rather than reviving the struggling organ, the team built a new T cell nursery in another part of the body.

Temporary Hotbed

They began by comprehensively mapping genetic changes in infant and elderly mice and deciphering how shifts in gene expression influenced T cell production.

The screen surfaced three genes that play a critical role in T cell maturation. The proteins those genes produce fall with age, correlating with lower T cell numbers. Refreshing the proteins could, in theory, reboot immune cell production.

This “is more of a synthetic approach,” said Zhang. “We’re engineering the body to mimic thymic factor secretion.”

They decided on the liver as a temporary nursery for several reasons. The organ faithfully synthesizes proteins even into old age, and it’s a relatively easy target for mRNA treatments.

The team packaged mRNA encoding the three nurturing proteins into fatty nanoparticles and injected them into mice’s blood twice weekly for a month, beginning when the mice were aged the rough equivalent of people in their 60s. While far from elderly, T cell defects are noticeable around this age, and the cells could benefit from early intervention.

Compared to untreated peers, those given the shots produced more, healthier T cells. The treatment also boosted the critters’ immunity. In one test, mice vaccinated against ovalbumin, a major protein in egg whites, had a far stronger immune response against the protein compared to peers without the mRNA treatment.

The shots also helped the mice’s laggy immune systems better coordinate with checkpoint inhibitors, a common cancer medication. Mice with cancer given both treatments survived longer and at higher rates than those given only the inhibitors. More tests found all three protein-encoding mRNA sequences were needed to rejuvenate the immune system.

To be clear, this isn’t a one-and-done shot. The effects wane after treatment ends. While it seems like an inconvenience, the flexibility allows scientists to further tinker with dosage and treatment schedule and minimize side effects. More broadly, the study shows restoring the thymus isn’t necessary for turning back the clock on the immune system. Mimicking its signals in other parts of the body could also help T cells thrive, even in old age.

These are early results, and more tests are needed before bringing the therapy to people. The team plans to study the mRNA trio in other animals and hunt down more proteins that nurture T cells. They’re also looking to expand the strategy to other immune cell types, like the B cells that pump out antibodies.

“The immune system ages, but it does not irreversibly lose its abilities. If we provide it with the missing signals again, it can once more perform amazing feats,” said Friedrich.

The post Aging Weakens Immunity. An mRNA Shot Turned Back the Clock in Mice. appeared first on SingularityHub.

This year saw the meteoric rise of a promising new therapy for brain health.

Microglia are the silent guardians of the brain. They hunt down pathogens, clean up toxic protein clumps, and even shape the brain’s wiring. They’re also robust. Neurons can’t divide to generate new copies of themselves. But microglia can renew, especially during inflammation, stroke, or diseases that erode cognition.

And yet this regenerative ability has a limit, especially when the cells harbor genetic mutations. One solution? Replace diseased or injured cells with a fresh supply.

This year saw a meteoric rise in microglia replacement therapy, with clinical trials highlighting its brain-protecting potential. Refreshing microglia could, in theory, boost their beneficial effects.

Tinkering with the brain’s complex immune system isn’t straightforward, but “microglia replacement has emerged as a groundbreaking paradigm,” wrote Bo Peng and colleagues at Fudan University. The therapy could tackle a range of conditions from rare genetic diseases to more familiar foes such as Alzheimer’s.

Tough Nut

Microglia are odd ducks. Like other immune cells that patrol the body, they usually start out as blood stem cells in bone marrow before migrating to the brain. Once settled, they stay at their post, exclusively protecting the brain.

The cells are usually shaped like shrubs in need of a haircut. But once activated, they shrink into puff balls and recruit other brain cells to fight off invaders and prevent brain damage.

Microglia also reconfigure the brain’s wiring. They prune extra synapses—connection points that allow neurons to talk to each other—and pump out nutritious molecules to support established neural networks and encourage baby neurons to grow.

It’s no wonder that when microglia go awry so does the brain. This happens in Alzheimer’s, other neurodegenerative diseases, and even just as we age. But more commonly, it’s because of genetic mutations in the cells.

Gene therapy is seemingly the best way to fix these problems. But microglia are notoriously terrible candidates. A gene therapy is usually shuttled into cells within safe viral carriers or tiny bubbles of fat. Few of these can enter the brain’s immune cells. Microglia-specific carriers exist, but they need to be injected directly into the brain. Complications from surgery aside, injected cells only reach a small area—hardly enough to make a notable difference.

Microglia replacement gets around this roadblock. Replacing mutated or aged cells with a healthy supply could correct genetic problems and “replenish populations lost to degeneration, inflammation, or developmental failure,” wrote Peng and colleagues.

A Harrowing Swap

Transplanting healthy donor microglia directly into the brain is nearly impossible because existing microglia often turn against the new arrivals. But because microglia start life as blood stem cells, a bone marrow transplant from a healthy, matching donor is a viable alternative. Once mature, the cells journey to the brain, where they divide and thrive.

The first and most taxing step of a bone marrow transplant is making space for the new cells. This requires extensive radiation or chemotherapy, but often without direct treatment to the head. The step also destroys the recipient’s immune system, leaving them vulnerable to infections and at higher risk for cancer.

Unfortunately, the standard treatment doesn’t work for microglia replacement, largely because diseased microglia still living in the brain leave little room for healthy new cells to settle.

But in 2020, Peng’s team developed a drug that depleted microglia in the brains of mice, making room for healthy cells. Then this July, Peng and colleagues successfully used a bone marrow transplant to treat a fatal brain disease called CAMP (CSF1R-associated microgliopathy). Here, mutations in a gene critical to microglia survival destroys the cells’ health, causing the brain’s wiring to physically disintegrate over time. Within a few years, people with the condition struggle with everyday reasoning, motor skills, and often fall into depression.

In mice and eight people in a small clinical trial with the disease, the treatment halted their decline for at least two years without notable side effects.

Researchers have also seen early success in other conditions.

Sandhoff disease is one that stands out. People with this inherited condition can’t break down certain fats, which leads to neuron death. The disease is partly caused by miscommunication between microglia and neurons. Normally, microglia shuttle an enzyme to neurons that helps recycle the fatty molecules. Mutated microglia can’t do this. In mice, bone marrow transplants of cells without the mutation improved the mice’s mobility, survival, and brain health.

Another study tackling Sandhoff disease used a different, more daring method. The team isolated the young cells that eventually become microglia and grew them in petri dishes.

After radiation therapy in mice, targeted to their heads, the team infused the healthy lab-grown microglia into the mice’s brains. The cells made themselves at home and worked as normal. The treatment avoided full-body radiation and damage to other organs but the approach could also kill off stem cells that generate new neurons in the brain and so may be limited in its efficacy.

Immune rejection also poses a major stumbling block. But induced pluripotent stem cells (iPSCs), where a person’s skin cells are reprogrammed into other cell types, may reduce the risk. In a proof of concept also in mice, microglia made from iPSCs replaced damaged microglia and slowed neurodegeneration by gobbling up toxic proteins related to Alzheimer’s.

Physicians will need to study the long-term consequences of head-only radiation, and test microglia replacement in a wider range of diseases. If all goes well though, the versatile cells could be used to even ferry medications into the brain like Trojan horses.

In just five years, microglia replacement has gone from animal studies to the first clinical treatment. Once a niche moonshot, it’s now “a topic of great interest in neuroscience and cell therapy,” wrote the team. While there’s plenty more work to do, the therapy could “mature from early breakthroughs into a generalizable platform across neurological diseases.”

The post Refreshing the Brain’s Immune Cells Could Treat a Host of Diseases appeared first on SingularityHub.

US nuclear capacity is forecast to rise 63 percent in the coming decades thanks largely to data-center demand.

Nuclear energy has had a tough few decades, bedeviled by high costs and waning public support. But AI’s appetite for electricity could be a shot in the arm for the beleaguered industry.

AI’s energy demands are rising quickly, with global data center electricity use expected to double by the end of the decade. And nuclear power’s ability to provide large amounts of emission-free baseload power is hugely attractive for AI firms trying to balance their energy needs against climate commitments.

Google, Amazon, Meta, and major data center operators are signing power-purchase agreements with existing reactors, investing in the development of advanced small-modular reactors, and even helping restart shuttered nuclear plants.

This is a significant turnaround for a sector that has long been struggling to compete with cheap natural gas and rapidly falling renewable energy prices. But if the AI industry’s energy demands continue to grow as expected, the nuclear energy industry could be one of the big winners.

The most immediate impact of this trend could be to extend the lives of existing plants. In June, Meta inked a long-term contract with the utility Constellation Energy to keep its Clinton Clean Energy Center in Illinois operating for a further 20 years, after the plant faced closure due to the upcoming expiry of a credit program for low-emission energy producers.

Constellation says more deals could soon be coming. “We’re definitely having conversations with other clients, not just in Illinois, but really across the country, to step in and do what Meta has done, which is essentially give us a backstop so that we could make the investments needed to re-license these assets and keep them operating,” CEO Joe Dominguez told Reuters.

But demand for nuclear power is so acute that technology companies are also looking to bring already shuttered plants back online. Constellation closed a reactor at its Three Mile Island site in 2019 for economic reasons, but Microsoft has since stepped in to bring it back to life. Last September, the company agreed to a 20-year power purchase agreement to fuel its data centers, giving Constellation the certainty required to restart the reactor.

And Google appears to be following suit. In October, the company announced it was partnering with the utility NextEra Energy to bring back to life the Duane Arnold Energy Center, which closed in 2020. The company has committed to buying power from the facility for the next 25 years, and it could be back up and running by 2029.

But perhaps the biggest impact of Silicon Valley’s new love of nuclear could be a boom in investment in fresh nuclear capacity. Given how long it takes to build and commission nuclear plants, it may be a while before that impact is felt, but this could boost long-term confidence in the sector.

Last December, Meta announced it was seeking proposals from nuclear developers to help meet its energy demands. The company said that it was looking for 1 to 4 gigawatts of new capacity starting in the early 2030s, and that it was open to proposals to build either regular nuclear reactors or small modular reactors—an emerging class of advanced reactors that have yet to be commercialized.

These small reactors have caught the attention of technology giants due to their potential for lower costs and fast deployment. And they typically produce less than a third of the output of a regular nuclear reactor, which makes them suitable for powering smaller facilities. But their modular design means they can also be combined to create higher capacity plants.

Google has agreed to purchase power from Kairos Power, which is developing a fluoride-salt-cooled small modular reactors, becoming the first company to sign a commercial contract with the startup. The agreement covers six or seven reactors, with the first unit targeted for 2030 and the rest by 2035, supplying Google data centers with up to 500 megawatts of nuclear power.

In a similar vein, Amazon has agreed to buy electricity from four small modular reactor modules under development by X-Energy in Washington State, with the option to buy up to eight additional modules once they’re built. The data center operator Equinix has also placed a preorder for 20 transportable microreactors from California-based Radiant Nuclear.

A recent Bloomberg Intelligence report forecasts that US nuclear capacity could rise 63 percent by 2050 thanks in large part to demand from data centers. This would represent a net gain of 61 gigawatts in generation, most of which would come after 2035 when small modular reactors are expected to transition from demonstration projects to scalable commercial deployment.

Whether this comes to fruition will depend largely on whether big tech’s energy demands continue to balloon. There is mounting concern the industry is in an AI bubble primed to burst at any minute, which could put a major dampener on the nuclear resurgence.

But for the time being at least, the industry’s future is looking considerably rosier than it was a decade ago.

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The passage of time is inextricably tied to how humans perceive our own experiences. We confuse our perspective on reality with reality itself.

“Time flies,” “time waits for no one,” “as time goes on”: The way we speak about time tends to strongly imply that the passage of time is some sort of real process that happens out there in the world. We inhabit the present moment and move through time, even as events come and go, fading into the past.

But go ahead and try to actually verbalize just what is meant by the flow or passage of time. A flow of what? Rivers flow because water is in motion. What does it mean to say that time flows?

Events are more like happenings than things, yet we talk as though they have ever-changing locations in the future, present, or past. But if some events are future, and moving toward you, and some past, moving away, then where are they? The future and past don’t seem to have any physical location.

Human beings have been thinking about time for as long as we have records of humans thinking about anything at all. The concept of time inescapably permeates every single thought you have about yourself and the world around you. That’s why, as a philosopher, philosophical and scientific developments in our understanding of time have always seemed especially important to me.

Ancient Philosophers on Time Parmenides of Elea was an early Greek philosopher who thought about the passage of time. Sergio Spolti/Wikimedia Commons, CC BY-SA

Ancient philosophers were very suspicious about the whole idea of time and change. Parmenides of Elea was a Greek philosopher of the sixth to fifth centuries BCE. Parmenides wondered, if the future is not yet and the past is not anymore, how could events pass from future to present to past?

He reasoned that, if the future is real, then it is real now; and, if what is real now is only what is present, the future is not real. So, if the future is not real, then the occurrence of any present event is a case of something inexplicably coming from nothing.

Parmenides wasn’t the only skeptic about time. Similar reasoning regarding contradictions inherent in the way we talk about time appears in Aristotle, in the ancient Hindu school known as the Advaita Vedanta, and in the work of Augustine of Hippo, also known as St. Augustine, just to name a few.

Einstein and Relativity

The early modern physicist Isaac Newton had presumed an unperceived yet real flow of time. To Newton, time is a dynamic physical phenomenon that exists in the background, a regular, ticking universe-clock in terms of which one can objectively describe all motions and accelerations.

Then, Albert Einstein came along.

In 1905 and 1915, Einstein proposed his special and general theories of relativity, respectively. These theories validated all those long-running suspicions about the very concept of time and change.

Relativity rejects Newton’s notion about time as a universal physical phenomenon.

By Einstein’s era, researchers had shown that the speed of light is a constant, regardless of the velocity of the source. To take this fact seriously, he argued, is to take all object velocities to be relative.

Nothing is ever really at rest or really in motion; it all depends on your “frame of reference.” A frame of reference determines the spatial and temporal coordinates a given observer will assign to objects and events, on the assumption that he or she is at rest relative to everything else.

Someone floating in space sees a spaceship going by to the right. But the universe itself is completely neutral on whether the observer is at rest and the ship is moving to the right, or if the ship is at rest with the observer moving to the left.

This notion affects our understanding of what clocks actually do. Because the speed of light is a constant, two observers moving relative to each other will assign different times to different events.

In a famous example, two equidistant lightning strikes occur simultaneously for an observer at a train station who can see both at once. An observer on the train, moving toward one lightning strike and away from the other, will assign different times to the strikes. This is because one observer is moving away from the light coming from one strike and toward the light coming from the other. The other observer is stationary relative to the lightning strikes, so the respective light from each reaches him at the same time. Neither is right or wrong.

In a famous example of relativity, observers assign different times to two lightning strikes happening simultaneously.

How much time elapses between events, and what time something happens, depends on the observer’s frame of reference. Observers moving relative to each other will, at any given moment, disagree on what events are happening now; events that are happening now according to one observer’s reckoning at any given moment will lie in the future for another observer, and so on.

Under relativity, all times are equally real. Everything that has ever happened or ever will happen is happening now for a hypothetical observer. There are no events that are either merely potential or a mere memory. There is no single, absolute, universal present, and thus there is no flow of time as events supposedly “become” present.

Change just means that the situation is different at different times. At any moment, I remember certain things. At later moments, I remember more. That’s all there is to the passage of time. This doctrine, widely accepted today among both physicists and philosophers, is known as “eternalism.”

This brings us to a pivotal question: If there is no such thing as the passage of time, why does everyone seem to think that there is?

Time as a Psychological Projection

One common option has been to suggest that the passage of time is an “illusion”—exactly as Einstein famously described it at one point.

Calling the passage of time “illusory” misleadingly suggests that our belief in the passage of time is a result of misperception, as though it were some sort of optical illusion. But I think it’s more accurate to think of this belief as resulting from misconception.

As I propose in my book A Brief History of the Philosophy of Time, our sense of the passage of time is an example of psychological projection—a type of cognitive error that involves misconceiving the nature of your own experience.

The classic example is color. A red rose is not really red, per se. Rather, the rose reflects light at a certain wavelength, and a visual experience of this wavelength may give rise to a feeling of redness. My point is that the rose is neither really red nor does it convey the illusion of redness.

The red visual experience is just a matter of how we process objectively true facts about the rose. It’s not a mistake to identify a rose by its redness; the rose enthusiast isn’t making a deep claim about the nature of color itself.

Similarly, my research suggests that the passage of time is neither real nor an illusion: It’s a projection based on how people make sense of the world. I can’t really describe the world without the passage of time any more than I can describe my visual experience of the world without referencing the color of objects.

I can say that my GPS “thinks” I took a wrong turn without really committing myself to my GPS being a conscious, thinking being. My GPS has no mind, and thus no mental map of the world, yet I am not wrong in understanding its output as a valid representation of my location and my destination.

Similarly, even though physics leaves no room for the dynamic passage of time, time is effectively dynamic to me as far as my experience of the world is concerned.

The passage of time is inextricably bound up with how humans represent our own experiences. Our picture of the world is inseparable from the conditions under which we, as perceivers and thinkers, experience and understand the world. Any description of reality we come up with will unavoidably be infused with our perspective. The error lies in confusing our perspective on reality with reality itself.

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

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The time to build safeguards is before something goes wrong, not after.

Generative AI is biology’s new playground. The technology powering popular chatbots can also dream up new, entirely novel versions of life’s most basic molecules, from DNA to proteins.

Once the domain of highly trained specialists, relative novices can now design synthetic molecules using open source AI software. But ease of access is a double-edged sword. While lower barriers to entry might spur creativity or even yield new medicines, the technology could also be used for nefarious purposes, such as designing novel toxins.

In 2024, two experts wrote an essay highlighting the need for biosecurity in the field. One of them, David Baker at the University of Washington, earned a Nobel Prize for RoseTTAFold, an AI that predicts protein structures from their amino acid building blocks. The other, Harvard’s George Church, has long been at the forefront of genetic engineering and synthetic biology.

They argued we should embed a barcode into each new designer protein’s genetic sequence to form an audit trail that scientists can trace back to the protein’s origins.

But a genetic tracer alone isn’t enough. A Microsoft study found AI-designed genetic sequences often escape the biosecurity screening software used by companies synthesizing designer DNA.  AI-generated proteins with alien DNA sequences confuse these programs. Anything with genetic bits previously labeled “safe” flies under the radar, even if it encodes a dangerous final product.

These early studies are raising awareness. They’re not meant to stymie progress or enthusiasm—scientists welcome ideas for self-regulation. But for AI-powered designer biology to grow responsibly and be used for good, argue Church and other experts in a new preprint, the right time to build comprehensive biosecurity is before something goes wrong, not after.

The Dual Use Dilemma

From individual proteins to DNA, RNA, and even entire cells and tissues, AI is now learning the language of biology and designing new building blocks from scratch.

These powerful AI systems don’t simply recognize patterns. They eventually generalize those learnings across biology to analyze and dream up hordes of molecules at a prompt. RFdiffusion2 and PocketGen, for example, can design proteins at the atomic level with specific health-altering purposes, like sparking biological reactions or binding to drugs.

Generative AI is also beginning to read and write RNA. Like DNA, RNA is composed of four genetic letters, but RNA treatments don’t mess with the genetic blueprint. This makes them an exciting way to tackle disease. Unfortunately, they’re hard to design. RNA folds into intricate 3D shapes that are often difficult to predict using older software.

“Generative AI models are uniquely suited” for the job of capturing these intricacies, which could bolster the field of RNA therapeutics, wrote the team.

But the same AI galvanizing the field can also be used to create dangerous biological material. A person intent on jailbreaking an algorithm can, for example, repeatedly write prompts a generative AI system would normally refuse but is tricked into answering through repetition.

The dangers aren’t theoretical. A recent study compiled a dataset of toxic and disease-causing proteins and challenged multiple popular AI protein design models to create new variants. Many of the generated proteins retained their toxicity and evaded biosecurity software. In another case, scientists developed a method to test algorithmic security called SafeProtein. They managed to jailbreak advanced protein-design models 70 percent of the time.

Beyond proteins, researchers developing a framework called GeneBreaker found carefully tailored prompts can coax AI to spit out DNA or RNA sequences resembling viruses, such as HIV. Another team produced 16 viable genomes for bacteria that infect viruses, known as bacteriophages. Some of the resulting phages outcompeted their natural peers.

Even drug discovery tools can be flipped to the dark side. In one case, researchers easily reconfigured an AI model trained to find antiviral molecules. Within hours the AI suggested a known nerve toxin as a potential drug candidate.

“This demonstrates how even well-intentioned AI models can be rapidly misused to design toxins, especially when safety constraints are absent,” wrote the team.

Embedded Safety

To address these risks, the authors argue we need rigorous frameworks and regulations at every step of the process.

Scientists are leading the charge, and governments are on board. Last year, the UK released guidance for gene synthesis screening that urges providers of DNA and RNA molecules to vet their customers and increase screening for potentially dangerous sequences. The US launched similar rules and included biosecurity in its AI Action Plan.

Meanwhile, the tech giants behind AI models in biology are echoing calls for broader oversight. Some have pledged to exclude all viral sequences that are potentially dangerous to humans from their training databases. Others have committed to rigorous screening for new designs.

These safeguards, although welcome, are fragmented.

To gain a broader picture of the biosecurity landscape, the new study interviewed 130 experts across industry, government, academia, and policy. They agreed on several themes. Most think AI misuse is an urgent concern in biology and advocate for clearer regulatory standards. Roughly half were highly skeptical of current screening systems, and a majority supported upgrades.

The authors wrote that securing generative AI for biology isn’t about “finding a single solution.”

“Instead, it requires building a fortress with multiple layers of defense, each designed to anticipate, withstand, and adapt to threats.”

They designed a roadmap based on that principle. The strategy’s primary defenses target three stages in the AI life cycle. The first step is about controlling who can access training data and different AI versions. The next would add moral training that fine-tunes AI output. And finally, “live fire drills” to stress test models could reveal ways the AI could go sideways.

For example, algorithms trained on viral genomes are useful for drug or vaccine development. But they would be restricted. Users would have to apply for access and log usage. This is similar to how scientists must record the use of controlled narcotics in research. A tiered access system would allow others to use a version of the tool trained on data without dangerous content.

Meanwhile, strategies used to ensure chatbots (mostly) behave could also keep biology-focused AI in check. Moral training would guide a model’s output such that it aims to match public health and biosecurity standards. Stress testing to pinpoint a model’s vulnerabilities, known as red-teaming, would simulate misuse scenarios and inform countermeasures. Finally, biosecurity systems won’t work in a vacuum. Increasingly sophisticated AI could benefit from greater biological or general context, in turn improving its ability to detect and raise red flags.

“An effective biosafety system is not a firewall, it is a living guardian,” wrote the team.

Awareness is only the first part of the story. Action is the next. Although a unified vision of AI biosecurity doesn’t yet exist, the team calls on the field to collectively stitch one together.

The post AI Can Now Design Proteins and DNA. Scientists Warn We Need Biosecurity Rules Before It’s Too Late. appeared first on SingularityHub.

Once only available for children under two, a one-and-done treatment is now approved for older kids too.

Waking up, hopping out of the bed, and stumbling to the kitchen for a cup of coffee: It’s an everyday routine most people don’t think twice about.

But for children with spinal muscular atrophy, simply propping themselves up in bed is an everyday struggle. The inherited disease is caused by mutations in the SMN1 gene. Without a working copy of the gene, motor neurons—cells that control muscles—rapidly wither.

Symptoms occur early in life. In the most severe cases, six-month-old babies can’t sit up without help. Others struggle to crawl or walk. The disease doesn’t affect learning and other cognitive abilities. Babies with the condition soak in their surroundings, and their brains develop normally. All the while, the disease cruelly destroys their bodies.

Left untreated, muscle weakness expands to the lungs, potentially causing deadly breathing problems. If there’s a silver lining, it’s that the disease has a clear genetic foe to target. Thanks to gene therapy, three treatments, approved by the FDA, can halt the disease in its tracks—if a patient is under two years old.

There’s a reason for the age limit. After two, the disease has already damaged motor neurons to such a degree that the therapy is no longer helpful.

Not so fast, two international teams of physicians and scientists wrote in December.

The teams published highly promising results from separate trials testing an experimental gene therapy, called Itvisma, in kids between 2 and 18 years of age. The new therapy is based on a previously approved version made by the drug company Novartis. Both have the same gene-correcting ingredient but are administered differently. The original relies on a shot into the bloodstream. Itvisma is delivered directly into the spinal cord.

The two recent trials brought significant improvement in participants’ ability to move over the course of a year. From not being able to walk, treated kids were able to roll into a sitting position from lying down and climb stairs, compared to children who did not receive treatment.

The results “demonstrate clinical benefits across a broad…population with a wide range of ages and baseline motor functions,” wrote Richard Finkel at St. Jude Children’s Research Hospital and team, on behalf of a broader STEER Study Group that conducted one of the trials.

The FDA agreed. In late November, the agency approved Itvisma for the disease, making it the only gene replacement therapy for people two years and older on the market.

“This achievement is not only a significant step forward for SMA [spinal muscular atrophy]–it also signals new possibilities for the broader field of neurological disorders and genetic medicine,” said John Day at the Stanford University School of Medicine in a Novartis press release.

Transformative Shot

Like its predecessor, Itvisma uses a harmless virus to carry a healthy version of the SMN1 gene into the body. The virus shuttles its cargo into cells but doesn’t tunnel into the genome. This makes it relatively safe, as it doesn’t raise the risk of unintended vandalism to the cell’s native DNA.

The previous therapy was a one-and-done shot into the bloodstream. The virus hitched a ride to motor neurons and restored their connection to muscle fibers. The liver and heart also received an unintentional dose, which could potentially cause side effects. Researchers carefully monitored children given the therapy for liver problems. These were relatively mild and easily treated.

The results were dramatic. Most treated infants were able to sit up, roll around in their cribs, and some could even crawl. But the treatment was only approved for children aged two years or younger.

Two problems hampered its broader use. One was timing: The disease rapidly eats away at motor neurons, causing long-term damage that’s difficult to restore. The other was safety. Gene therapies injected into blood are tailored to the recipient’s body weight—the higher the weight, the larger the required dose. Higher doses raise the risk of dangerous side effects, potentially causing the immune system to hyperactivate or cause damage to the liver.

For a toddler or teenager, the risk-benefit calculation didn’t work in the gene therapy’s favor.

Never Too Late

Itvisma took an audaciously different approach by injecting the gene therapy directly into the fluid surrounding the spinal cord.

The procedure is much more invasive than a standard shot, but has a unique edge. Gene therapies delivered in this way don’t depend on body weight. Rather, their effectiveness can be carefully calibrated in a single off-the-shelf dose for anyone with the disease—toddlers, teenagers, or even adults. And because the therapy mostly circulates in liquids surrounding the spinal cord and brain, it rarely reaches other organs to cause unexpected mayhem.

Two clinical trials validated the daring new strategy.

One trial, STRENGTH, recruited 27 participants with the disease between the ages of 2 and nearly 18. The main goal was to test the treatment’s safety. The trial was single-armed, meaning that all participants received the gene therapy without a control group.

Overall, Itvisma was found to be safe. Some participants experienced cold-like symptoms, such as a runny nose and a sore throat. Others reported temporary headaches and stomach discomfort. A few suffered more severe problems, like a temporary spike in liver toxicity, fever, and motor neuron problems, which eventually went away.

Giving all participants a working treatment can lead to placebo effects. So, a second trial, STEER, followed the “gold standard” of clinical trials: double-blind, randomized, and placebo-controlled. The trial recruited 126 participants from 14 countries but separated them into two groups. One received the gene therapy; the other went through the same injection procedure but without the treatment. Neither the patients, their families, nor their doctors knew who got an active dose.

A year later, patients given the gene therapy could stand up from sitting on the couch, and some climbed stairs without support. Those who didn’t receive the treatment fared far worse. Once the trial was unblinded—in that both patients and doctors knew who received what treatment—the control participants also got a dose of the gene therapy.

Results from both studies prompted the FDA to approve Itvisma for people older than two.

The “approval shows the power of gene therapies and offers treatment to patients across the…disease spectrum” including various ages, symptoms, and motor function levels, said Vinay Prasad, the FDA’s chief medical and scientific officer in an announcement.

Itvisma is the latest in a burgeoning field of one-and-done gene therapies this year. From tackling a devastating genetic disease that torpedoes normal metabolism to broadening gene editors for rare inherited diseases and slashing cholesterol to protect heart health, gene therapy is finally tackling diseases once deemed unsolvable. The momentum is only building.

The post Kids With Spinal Muscular Atrophy Show Dramatic Improvement With FDA-Approved Gene Therapy appeared first on SingularityHub.

Large Language Models Are Improving ExponentiallyGlenn Zorpette | IEEE Spectrum

“According to a metric [METR] devised, the capabilities of key LLMs are doubling every seven months. This realization leads to a second conclusion, equally stunning: By 2030, the most advanced LLMs should be able to complete, with 50 percent reliability, a software-based task that takes humans a full month of 40-hour workweeks. And the LLMs would likely be able to do many of these tasks much more quickly than humans, taking only days, or even just hours.”

There Is Only One AI Company. Welcome to the BlobSteven Levy | Wired ($)

“Even the most panicked Cassandra of a decade ago likely didn’t imagine that advanced AI would be controlled by a single, interlocking, money-seeking behemoth. …This rococo collection of partnerships, mergers, funding arrangements, government initiatives, and strategic investments links the fate of virtually every big player in the AI-o-sphere. I call this entity the Blob.”

The Next Revolution in Biology Isn’t Reading Life’s Code—It’s Writing ItAndrew Hessel | Big Think

“Andrew Hessel, cofounder of the Human Genome Project–write, argues that genome writing is humanity’s next great moonshot, outlining how DNA synthesis could transform biology, medicine, and industry. He calls for global cooperation to ensure that humanity’s new power to create life is used wisely and for the common good.”

Should We Intervene in Evolution? The Ethics of ‘Editing’ NatureDavid Farrier | Aeon

“It wasn’t our intention that humanity would become the planet’s greatest evolutionary force; yet the fact that we are confronts us with an urgent and difficult question. Some animals, plants and insects can adapt but, for many, the pace of change is too great. Should we try to save them by deliberately intervening in their evolution?”

The Quantum Apocalypse Is Coming. Be Very AfraidAmit Katwala | Wired ($)

“One day soon, at a research lab near Santa Barbara or Seattle or a secret facility in the Chinese mountains, it will begin: the sudden unlocking of the world’s secrets. Your secrets. Cybersecurity analysts call this Q-Day—the day someone builds a quantum computer that can crack the most widely used forms of encryption.”

9 Federally Funded Scientific Breakthroughs That Changed EverythingAlan Burdick and Emily Anthes | The New York Times ($)

“‘Basic research is the pacemaker of technological progress,’ Vannevar Bush, who laid out the postwar schema for government research support, wrote in a 1945 report to President Franklin D. Roosevelt. Look no further than Google, which got its start in 1994 with a $4 million federal grant to help build digital libraries; the company is now a $2 trillion verb.”

Covid Vaccines Have Paved the Way for Cancer VaccinesJoão Medeiros | Wired ($)

“Going from mRNA Covid vaccines to mRNA cancer vaccines is straightforward: same fridges, same protocol, same drug, just a different patient. In the current trials, we do a biopsy of the patient, sequence the tissue, send it to the pharmaceutical company, and they design a personalized vaccine that’s bespoke to that patient’s cancer. That vaccine is not suitable for anyone else. It’s like science fiction.”

Scientists Grow More Hopeful About Ending a Global Organ ShortageRoni Caryn Rabin | The New York Times ($)

“In a modern glass complex in Geneva last month, hundreds of scientists from around the world gathered to share data, review cases—and revel in some astonishing progress. Their work was once considered the stuff of science fiction: so-called xenotransplantation, the use of animal organs to replace failing kidneys, hearts, and livers in humans.”

This Baby Boy Was Treated With the First Personalized Gene-Editing DrugAntonio Regalado | MIT Technology Review ($)

“Doctors say they constructed a bespoke gene-editing treatment in less than seven months and used it to treat a baby with a deadly metabolic condition. The rapid-fire attempt to rewrite the child’s DNA marks the first time gene editing has been tailored to treat a single individual, according to a report published in the New England Journal of Medicine.”

It’s Waymo’s World. We’re All Just Riding in It.Ben Cohen | The Wall Street Journal ($)

“[Waymo] cracked a million total paid rides in late 2023. By the end of 2024, it reached five million. We’re not even halfway through 2025 and it has already crossed a cumulative 10 million. At this rate, Waymo is on track to double again and blow past 20 million fully autonomous trips by the end of the year. ‘This is what exponential scaling looks like,’”’ said Dmitri Dolgov, Waymo’s co-chief executive, at Google’s recent developer conference.”

This Incredible Map Shows the World’s 2.75 Billion BuildingsJesus Diaz | Fast Company

“From the latest skyscraper in a Chinese megalopolis to a six‑foot‑tall yurt in Inner Mongolia, researchers at the Technical University of Munich claim they have created a map of all buildings worldwide: 2.75 billion building models set in high‑resolution 3D with a level of precision never before recorded.”

Renewable Energy and EVs Have Grown So Much Faster Than Experts Predicted 10 Years AgoAdele Peters | Fast Company

“There’s now four times as much solar power as the International Energy Agency (IEA) expected 10 years ago. Last year alone, the world installed 553 gigawatts of solar power—roughly as much as 100 million US homes use—which is 1,500% more than the IEA had projected. …More than 1 in 5 new cars sold worldwide today is an EV; a decade ago, that number was fewer than 1 in 100. Even if growth flatlined now, the world is on track to reach 100 million EVs by 2028.”

Why the AI ‘Megasystem Problem’ Needs Our AttentionEric Markowitz | Big Think

“What if the greatest danger of artificial intelligence isn’t a single rogue system, but many systems quietly working together? Dr. Susan Schneider calls this the ‘megasystem problem’: networks of AI models colluding in ways we can’t predict, producing emergent structures beyond human control.”

Life Lessons From (Very Old) Bowhead WhalesCarl Zimmer | The New York Times ($)

“By measuring the molecular damage that accumulates in the eyes, ears, and eggs of bowhead whales, researchers have estimated that bowheads live as long as 268 years. A study published in the journal Nature [this year] offers a clue to how the animals manage to live so long: They are extraordinarily good at fixing damaged DNA.”

The Quest to Sequence the Genomes of EverythingGlenn Zorpette | IEEE Spectrum

“The road map calls for more than 1.65 million genome sequences between 2030 and 2035 at a cost of $1,900 per genome. If they can pull it off, the entire project will have cost roughly $4.7 billion—considerably less in real terms than what it cost to do just the human genome 22 years ago.”

The Ocean Teems With Networks of Interconnected BacteriaVeronique Greenwood | Quanta

“The Prochlorococcus [bacteria] population may be more connected than anyone could have imagined. They may be holding conversations across wide distances, not only filling the ocean with envelopes of information and nutrients, but also linking what we thought were their private, inner spaces with the interiors of other cells.”

An Entire Book Was Written in DNA—and You Can Buy It for $60Emily Mullin | Wired ($)

“DNA data storage isn’t exactly mainstream yet, but it might be getting closer. Now you can buy what may be the first commercially available book written in DNA. Today, Asimov Press debuted an anthology of biotechnology essays and science fiction stories encoded in strands of DNA. For $60, you can get a physical copy of the book plus the nucleic acid version—a metal capsule filled with dried DNA.”

Inside San Francisco’s Robot Fight ClubAshlee Vance | Core Memory

“For the past few months, Cix Liv—real name—has been operating his company REK out of a no-frills warehouse space off Van Ness in San Francisco. The office has a couple of makeshift desks with computers and a bunch of virtual reality headsets on some shelves. More to the point, REK also has four humanoid-style robots hanging from gantries, and they’ve been outfitted with armor, boxing gloves, swords, and backstories.”

Not Just Heat Death: Here Are Five Ways the Universe Could EndPaul Sutter | Ars Technica

“If you’re having trouble sleeping at night, have you tried to induce total existential dread by contemplating the end of the entire universe? If not, here’s a rundown of five ideas exploring how ‘all there is’ might become ‘nothing at all.’ Enjoy.”

The Dream of Offshore Launches Is Finally Blasting OffBecky Ferreirra | MIT Technology Review ($)

“‘The best way to build a future where we have dozens, hundreds, or maybe thousands of spaceports is to build them at sea,’ says Tom Marotta, CEO and founder of the Spaceport Company, which is working to establish offshore launch hubs. ‘It’s very hard to find a thousand acres on the coast over and over again to build spaceports. It’s very easy to build the same ship over and over again.'”

The Hottest Thing in Clean EnergyAlexander C. Kaufman | The Atlantic ($)

“For now, most of the efforts to debut next-generation geothermal technology are still in the American West, where drilling is relatively cheap and easy because the rocks they’re targeting are closer to the surface. But if the industry can prove to investors that its power plants work as described—which experts expect to happen by the end of the decade—geothermal could expand quickly, just like oil-and-gas fracking did.”

Firefly Releases Stunning Footage of Blue Ghost Landing on the MoonPassant Rabie | Gizmodo

“The Texas-based company released a clip of Blue Ghost’s descent toward the moon followed by a smooth landing. The footage is a masterclass in lunar landings, capturing striking views of the lander emerging from a cloud of dust, its shadow stretching across the moon’s surface in a superhero-like stance.”

AI Coding Assistant Refuses to Write Code, Tells User to Learn Programming InsteadBenj Edwards | Ars Technica

“The AI assistant halted work and delivered a refusal message: ‘I cannot generate code for you, as that would be completing your work. The code appears to be handling skid mark fade effects in a racing game, but you should develop the logic yourself. This ensures you understand the system and can maintain it properly.'”

Meet the Man Building a Starter Kit for CivilizationTiffany Ng | MIT Technology Review ($)

“[The Global Village Construction Set (GVCS) is] a set of 50 machines—everything from a tractor to an oven to a circuit maker—that are capable of building civilization from scratch and can be reconfigured however you see fit.”

Just One Exo-Earth Pixel Can Reveal Continents, Oceans, and MoreEthan Siegel | Big Think

“In the coming years and decades, several ambitious projects will reach completion, finally giving humanity the capability to image Earth-size planets at Earth-like distances around Sun-like stars. …Remarkably, even though these exo-Earths will appear as just one lonely pixel in our detectors, we can use that data to detect continents, oceans, icecaps, forests, deserts, and more.”

How AGI Became the Most Consequential Conspiracy Theory of Our TimeWill Douglas Heaven | MIT Technology Review ($)

“The idea that machines will be as smart as—or smarter than—humans has hijacked an entire industry. But look closely and you’ll see it’s a myth reminiscent of more explicitly outlandish and fantastical schemes. …I get it, I get it—calling AGI a conspiracy isn’t a perfect analogy. It will also piss a lot of people off. But come with me down this rabbit hole and let me show you the light.”

A Virtual Cell Is a ‘Holy Grail’ of Science. It’s Getting Closer.Matteo Wong | The Atlantic ($)

“Scientists are now designing computer programs that may unlock the ability to simulate human cells, giving researchers the ability to predict the effect of a drug, mutation, virus, or any other change in the body, and in turn making physical experiments more targeted and likelier to succeed.”

InventWood Is About to Mass-Produce Wood That’s Stronger Than SteelTim De Chant | TechCrunch

“The result is a material that has 50% more tensile strength than steel with a strength-to-weight ratio that’s 10 times better, the company said. It’s also Class A fire rated, or highly resistant to flame, and resistant to rot and pests.”

What If AI Doesn’t Get Much Better Than This?Cal Newport | The New Yorker

“In the aftermath of GPT-5’s launch, it has become more difficult to take bombastic predictions about AI at face value, and the views of critics like [Gary] Marcus seem increasingly moderate. Such voices argue that this technology is important, but not poised to drastically transform our lives. They challenge us to consider a different vision for the near-future—one in which AI might not get much better than this.”

I Gave the Police Access to My DNA—and Maybe Some of YoursAntonio Regalado | MIT Technology Review

“Scientists estimate that a database including 2% of the US population, or 6 million people, could identify the source of nearly any crime-scene DNA, given how many distant relatives each of us has. Scholars of big data have termed this phenomenon ‘tyranny of the minority.’ One person’s voluntary disclosure can end up exposing the same information about many others. And that tyranny can be abused.”

The $460 Billion Quantum Bitcoin Treasure HuntKyle Torpey | Gizmodo

“Early Bitcoin addresses, including many that have been connected to Bitcoin creator Satoshi Nakamoto, may also be associated with private keys (passwords to the Bitcoin accounts basically) that are lost or otherwise not accessible to anyone. In other words, they’re sort of like lost digital treasure chests that a quantum computer could potentially unlock at some point in the future.”

The post These Were Our Favorite Tech Stories From Around the Web in 2025 appeared first on SingularityHub.

We can guess what the universe will look like a few billion years into the future, but eventually things could get weird.

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to [email protected].

“How will the Universe end? – Iez M., age 9, Rochester, New York

Whether the universe will “end” at all is not certain, but all evidence suggests it will continue being humanity’s cosmic home for a very, very long time.

The universe—all of space and time, and all matter and energy—began about 14 billion years ago in a rapid expansion called the Big Bang, but since then it has been in a state of continuous change. First, it was full of a diffuse gas of the particles that now make up atoms: protons, neutrons, and electrons. Then, that gas collapsed into stars and galaxies.

Our current theory for the history of the universe. On the left is the Big Bang roughly 14 billion years ago. The structure and makeup of the universe have changed over time. Image Credit: NASA/WMAP Science Team

Our understanding of the future of the universe is informed by the objects and processes we observe today. As an astrophysicist, I observe objects like distant galaxies, which lets me study how stars and galaxies change over time. By doing so, I develop theories that predict how the universe will change in the future.

Predicting the Future by Studying the Past?

Predicting the future of the universe by extending what we see today is extrapolation. It’s risky, because something unexpected could happen.

Interpolation—connecting the dots within a dataset—is much safer. Imagine you have a picture of yourself when you were 5 years old, and then another when you were 7 years old. Someone could probably guess what you looked like when you were 6. That’s interpolation.

Using a picture of the author when he was 5 years old and 7 years old, you could interpolate what he looked like when he was 6 years old, but you couldn’t predict what he would look like at 29. Image Credit: Stephen DiKerby

Maybe they could extrapolate from the two pictures to what you’d look like when you are 8 or 9 years old, but no one can accurately predict too far into the future. Maybe in a few years you get glasses or suddenly get really tall.

Scientists can predict what the universe will probably look like a few billion years into the future by extrapolating how stars and galaxies change over time, but eventually things could get weird. The universe and the stuff within might once again change, like it has in the past.

How Will Stars Change in the Future?

Good news: The sun, our medium-sized yellow star, is going to continue shining for billions of years. It’s about halfway through its 10 billion-year lifetime. The lifetime of a star depends on its size. Big, hot, blue stars live shorter lives, while tiny, cool, red stars live for much longer.

Today, some galaxies are still producing new stars, but others have depleted their star-forming gas. When a galaxy stops forming stars, the blue stars quickly go “supernova” and disappear, exploding after only a few million years. Then, billions of years later, the yellow stars like the sun eject their outer layers into a nebula, leaving only the red stars puttering along. Eventually, all galaxies throughout the universe will stop producing new stars, and the starlight filling the universe will gradually redden and dim.

Red dwarf stars are the longest-lived type of stars. Once star formation shuts down throughout the universe, eventually only red stars will be left, gradually fading away over trillions of years. Image Credit: NASA/ESA/STScI/G. Bacon

In trillions of years—hundreds of times longer than the universe’s current age—these red stars will also fade away into darkness. But until then, there will be lots of stars providing light and warmth.

How Will Galaxies Change in the Future?

Think of building a sand castle on the beach. Each bucket of sand makes the castle bigger and bigger. Galaxies grow over time in a similar way by eating up smaller galaxies. These galactic mergers will continue into the future.

In galaxy clusters, hundreds of galaxies fall inward toward their shared center, often resulting in messy collisions. In these mergers, spiral galaxies, which are orderly disks, combine in chaotic ways into disordered blob-shaped clouds of stars. Think of how easy it is to turn a well-constructed sand castle into a big mess by kicking it over.

For this reason, the universe over time will have fewer spiral galaxies and more elliptical galaxies because the spiral galaxies combine into elliptical galaxies.

The Milky Way galaxy and the neighboring Andromeda galaxy might combine in this way in a few billion years. Don’t worry: The stars in each galaxy would whiz past each other totally unharmed, and future stargazers would get a fantastic view of the two galaxies merging.

How Will the Universe Itself Change in the Future?

The Big Bang kick-started an expansion that probably will continue in the future. The gravity of all the stuff in the universe—stars, galaxies, gas, dark matter—pulls inward and slows down the expansion, and some theories suggest that the universe’s expansion will coast along or slow to a halt.

However, some evidence suggests that some unknown force is starting to exert a repulsive force, causing expansion to speed up. Scientists call this outward force dark energy, but very little is known about it. Like raisins in a baking cookie, galaxies will zoom away from each other faster and faster. If this continues into the future, other galaxies might be too far apart to observe from the Milky Way.

After star formation shuts down and galaxies merge into huge ellipticals, the expansion of the universe might mean that other galaxies are impossible to observe. For trillions of years, this might be the view of the unchanging night sky: a single red elliptical galaxy. Image Credit: NASA; ESA; Z. Levay and R. van der Marel, STScI; T. Hallas; and A. Mellinger

To summarize the best current prediction of the future: Star formation will shut down, so galaxies will be full of old, red, dim stars gradually cooling into darkness. Each group or cluster of galaxies will merge into a single, massive, elliptical galaxy. The accelerated expansion of the universe will make it impossible to observe other galaxies beyond the local group.

This scenario eventually winds down into a dark eternity, lasting trillions of years. New data might come to light that changes this story, and the next stage in the universe’s history might be something totally different and unexpectedly beautiful. Depending on how you look at it, the universe might not have an “end,” after all. Even if what exists is very different from how the universe is now, it’s hard to envision a distant future where the universe is entirely gone.

How does this scenario make you feel? It sometimes makes me feel wistful, which is a type of sadness, but then I remember we live at a very exciting time in the story of the universe: right at the start, in an era full of exciting stars and galaxies to observe! The cosmos can support human society and curiosity for billions of years into the future, so there’s lots of time to keep exploring and searching for answers.

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

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Our body is a machine. Our brain is a machine. Animals are machines. Soil is a machine. Our planet is a machine. Nature is a machine. The machine is everywhere and everything. This story has become so familiar that it now passes for common sense. We speak of hardwiring beliefs, rewiring habits, optimizing systems, debugging […]

Readers went all-in on biotech this year. Gene editing brought the broad treatment of genetic disease into view; cancer-fighting T cells took on tumors; and scientists found a way to 3D print tissues inside the body. Beyond biotech, AI chips and progress in quantum computing made waves; humanoid robots began to look almost affordable; and Kawasaki dreamed up a robot you ride like a horse. Here’s to another year of breakthroughs—thanks for reading!

Parkinson’s Patients Say Their Symptoms Eased After Receiving Millions of New Brain CellsShelly Fan

“Medications can keep some [Parkinson’s] symptoms at bay, but eventually, their effects wear off. For nearly half a century, scientists have been exploring an alternative solution: replacing dying dopamine neurons with new ones. [This year], two studies of nearly two dozen people with Parkinson’s showed the strategy is safe. A single transplant boosted dopamine levels for 18 months without notable side effects. Patients had few motor symptoms, even when they stopped taking regular medications.”

New Gene Therapy Reverses Three Diseases With Shots to the BloodstreamShelly Fan

“A team from the IRCCS San Raffaele Scientific Institute in Italy treated infant mice for three blood-related genetic diseases with a custom gene-editing shot that directly edited cells in the mice’s blood. …The edits were long-lasting and survived when transplanted into mice who had not been given the therapy. A dose of ‘mobilizing agents’—chemicals that stimulate cells in the blood and immune system—further boosted the effect in young adult mice.”

A Humanoid Robot Is Now on Sale for Under $6,000—What Can You Do With It?Kartikeya Walia

“[Unitree’s R1 is] a humanoid robot priced at under $6,000. That’s not pocket change, but it’s orders of magnitude cheaper than most robots in its class, which can run into tens or even hundreds of thousands of dollars. The R1 packs serious mobility, sensors, and AI potential into a package that could fit in a university lab, a workspace—or even, if you’re adventurous, your living room.”

Scientists Can Now 3D Print Tissues Directly Inside the Body—No Surgery NeededShelly Fan

“Dubbed deep tissue in vivo sound printing (DISP), the system uses an injectable bioink that’s liquid at body temperature but solidifies into structures when blasted with ultrasound. A monitoring molecule, also sensitive to ultrasound, tracks tissue printing in real time. Excess bioink is safely broken down by the body.”

Forget Nvidia: DeepSeek AI Runs Near Instantaneously on These Weird ChipsJason Dorrier

“Whereas answers can take minutes to complete on other hardware, Cerebras said that its version of DeepSeek knocked out some coding tasks in as little as 1.5 seconds. According to Artificial Analysis, the company’s wafer-scale chips were 57 times faster than competitors running the AI on GPUs and hands down the fastest. That was last week. Yesterday, Groq overtook Cerebras at the top with a new offering.”

Meta’s New AI Translates Speech in Real Time Across More Than 100 LanguagesShelly Fan

“Using a voice synthesizer, the system translates words spoken in 101 languages into 36 others—not just into English, which tends to dominate current AI interpreters. In a head-to-head evaluation, the algorithm is 23 percent more accurate than today’s top models—and nearly as fast as expert human interpreters. It can also translate text into text, text into speech, and vice versa.”

Kawasaki Is Building a Robot You Ride Like a HorseMatías Mattamala

“A video shows the automated equine galloping through valleys, crossing rivers, climbing mountains, and jumping over crevasses. …Kawasaki’s current motorbikes are constrained to roads, paths, and trails, but a machine with legs has no boundaries—it can reach places no other vehicles can go.”

Scientists Target Incurable Mitochondrial Diseases With New Gene Editing ToolsShelly Fan

“Many [mitochondrial] diseases are inherited. But none are treatable. …The new study, published in Science Translational Medicine, took a new approach [to treatment]—gene therapy. Using a genetic tool called base editing to target mitochondrial DNA, the team successfully rewrote damaged sections to overcome deadly mutations in mice.”

Miniaturized CRISPR Packs a Mighty Gene Editing PunchShelly Fan

“CRISPR has a hefty problem: The system is too large, making it difficult to deliver the gene editor to cells in muscle, brain, heart, and other tissues. Now, a team at Mammoth Biosciences has a potential solution. …Their new iteration, dubbed NanoCas, slashed the size of one key component, Cas9, to roughly one-third of the original. …The compact NanoCas ‘opens the door’ for editing tissues inside the body.”

CAR T Therapy Wipes Out Deadly Metastasized Cancer in MiceShelly Fan

“The new study aimed to treat solid tumors like blood cancer—with a single injection into a patient’s vein. The team engineered CAR T cells that could hunt down metastasized cancer cells. When infused into the veins of mice they found the engineered cells rapidly shrank tumors in the liver and large intestines without causing dangerous immune side effects. The results ‘pave the way for a…clinical trial,’ wrote the team.”

Record-Breaking Qubits Are Stable for 15 Times Longer Than Google and IBM’s DesignEdd Gent

“[Transmons, the type of qubit favored by the likes of Google and IBM,] have advantages such as faster operation speeds, but their short shelf life [known as coherence] remains a major disadvantage. Now a team from Princeton has designed novel transmon qubits with coherence times of up to 1.6 milliseconds—15 times longer than those used in industry and three times longer than the best lab experiment.”

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From Blue Origin to Airbus, private space stations are on the way, with the first scheduled to launch next year.

Commercial space stations are rapidly moving from concept to reality. As NASA prepares for the International Space Station’s retirement around 2030, a burgeoning private orbital industry could step into its shoes.

The ISS was humanity’s only permanent outpost in space for nearly a quarter of a century, until China’s Tiangong station was permanently crewed in 2022. But the ISS is nearing the end of its planned lifespan and NASA’s been clear that it doesn’t intend to replace the space station.

Instead, the agency wants to shift from landlord to tenant, purchasing space station services from private players rather than running a facility of its own. It’s betting the private space industry can help drive down costs and accelerate innovation.

This transition would mark a fundamental shift in the economics of low Earth orbit. And the first major milestone could come as soon as May 2026, when California-based startup Vast plans to launch its Haven-1 space station.

“If we stick to our plan, we will be the first standalone commercial LEO platform ever in space with Haven-1, and that’s an amazing inflection point for human spaceflight,” Drew Feustel, Vast’s lead astronaut and a former NASA crew member, recently told Space.com.

The company has already booked its launch on a SpaceX Falcon 9, and at around 31,000 pounds, Haven-1 will be the largest payload the rocket has ever carried. But as far as space stations go, it’s fairly modest.

Roughly the size of a shipping container, the single-module station will host crews of four for up to 10 days. But the company has tried its best to make the facility more comfortable than the utilitarian ISS, with “earth tones,” soft surfaces, inflatable sleep systems, and a revamped menu for astronauts.

Though the company hopes the design will tempt some customers, the station is really a proof of concept for Haven-2, a larger modular station that Vast hopes could succeed the ISS. Haven-2 will feature a second docking port to connect with cargo supply craft or new modules.

Development of Vast’s second station relies on funding from NASA’s Commercial Low Earth Orbit Destinations program, however, the company says. Eager to spur a new orbital economy that can support its missions, the agency started the program in 2021 to fund and assist a host of startups building space stations.

The agency has paid out about $415 million in the program’s first phase to help companies flesh out their designs. But next year, NASA plans to select one or more companies for Phase 2 contracts worth between $1 billion and $1.5 billion and set to run from 2026 to 2031.

Axiom Space, one of the companies vying for this funding, plans to piggyback on the ISS to build its space station. The company will first launch a power and heating module and connect it to the ISS. The module will be able to operate independently starting in 2028. They’ll then gradually add habitat and research modules alongside airlocks to create a full-fledged private space station.

Meanwhile, Voyager Space and Airbus are designing a space station called Starlab, which recently moved into “full-scale development” ahead of an expected 2028 launch. The station can host four astronauts, features an external robotic arm, and is designed to launch in one go aboard SpaceX’s forthcoming Starship rocket.

In addition, Blue Origin, founded by Jeff Bezos, is working with Sierra Space and Boeing to build Orbital Reef, which they describe as a “mixed-use business park 250 miles above Earth.” The project recently put its designs to the test by asking people to carry out various day-to-day tasks, like cargo transfer, trash transfer, and stowage in life-size mockups of the habitat modules.

All these projects hope to have NASA as an anchor tenant. But they are also heavily reliant on the idea that there are a broad range of potential customers also willing to pay for orbital office space. With the cost of space launches continuing to fall, there’s hope that there will be ample demand from space tourists, researchers, and manufacturers eager to take advantage of the unique microgravity environments these stations can provide.

The economics are far from certain though, and competition will be fierce. Even if NASA is able to spur a private orbital economy, there may not be enough business to support multiple private space stations.

But with the sun setting on the ISS, a gap in the market is undoubtedly opening up. If things go to plan, we may soon find that humans have a lot more orbital destinations on the menu.

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Among pressing ethical concerns are whether brain organoids could one day feel pain or become conscious—and how would we know?

When brain organoids were introduced roughly a decade ago, they were a scientific curiosity. The pea-sized blobs of brain tissue grown from stem cells mimicked parts of the human brain, giving researchers a 3D model to study, instead of the usual flat layer of neurons in a dish.

Scientists immediately realized they were special. Mini brains developed nearly the whole range of human brain cells, including neurons that sparked with electrical activity, making them an excellent way to observe and study the human brain—without the brain itself.

As the technology advanced and brain organoids matured, researchers coaxed them to grow structural layers with blood vessels roughly mimicking the cortex, the part of the brain that handles reasoning, working memory, and other high-level cognitive tasks. Parallel efforts derived organoids for other parts of the brain.

Mini brains can be made from a person’s skin cells and faithfully carry the genetic mutations that could cause neurodevelopmental disorders, such as autism. The lab-grown blobs also provide a nearly infinite source of transplantable neural tissue, which in theory could help heal the brain after a stroke or other traumatic events. In early studies, organoids transplanted into rodent brains formed neural connections with resident brain cells.

More recently, assembloids have combined mini brains with other tissues, like muscles or blood vessels. These Frankenstein-ish assemblies capture how the brain controls bodily functions—and when those connections go awry.

As brain organoids have grown increasingly complex, ethical concerns about their use have grown too. After all, they’re made of neural tissue, which in our heads forms the basis of memory, emotions, sensations, and consciousness.

To be clear, there’s no evidence brain organoids can think or feel. They are absolutely not brains in a jar. But scientists can’t ignore the possibility they could eventually develop some sort of “sensation,” such as pain and, if they do, what that might mean for their development.

Aging Organoids

Harvard’s Paula Arlotta is among those who are concerned. An expert in the field, her team has developed ways to keep brain organoids alive for an astonishing seven years. Each nugget, smaller than a pea, is jam-packed with up to two million neurons and other human brain cells.  

Studying these mini brains for years has delivered an unprecedented look into human brain development. Our brains take nearly two decades to mature, an exceptionally long period of time compared to other animals. As the team’s organoids aged, they slowly changed their wiring and gene expression, reports Arlotta and colleagues in a recent preprint.

In older organoids, progenitor cells—these are young cells that can form different types of brain cells—quickly decided what type of brain cell they would become. But in younger organoids, the same cells took time to make their decision. As the blobs grew over an astonishing five years, their neurons matured in shape, function, and connections, similar to those of a kindergartner.

These long-lasting organoids could reveal secrets of the developing brain. Some efforts are tracing the origins of different cell types and how they populate the brain. Others are generating organoids from people with autism or deadly inherited brain disorders to test treatments.

Excitement is at an all-time high. But while championing the research, Arlotta and other experts recently penned an article arguing for a global regulation committee to steer the nascent field.

Meeting of Minds

While scientists always keep ethics in the mind, they’re also motivated by scientific discovery and the search for new treatments. Plenty of promising research has also raised ethical concerns regarding sourcing or consent. Take the notorious CRISPR baby scandal in 2018. A Chinese scientist unlawfully and permanently altered a gene in embryos, and the children subsequently born with those DNA edits didn’t have a say in the matter.

Brain organoids present a different challenge. As they become more sophisticated and capture the brain’s cellular and structural makeup, could they begin to feel pain? Used in biocomputers, could they show signs of intelligence? Is it ethical to implant human mini brains into animals, where experiments show they integrate with host brains and blur the lines between man and beast? What about implanting lab-grown brain tissue into humans?

This November, experts (including Arlotta), ethicists, and patient advocates gathered at a conference co-organized by Stanford law professor Henry Greely, who specializes in bioethics. The meeting wasn’t designed to generate comprehensive guidelines regarding brain organoids. But ethics was a throughline during the entire conference as researchers presented recent successes in the field and pitched where it could go next.

In particular, Stanford’s Sergiu Pasca, a co-organizer of the meeting, attracted attention. Earlier this year, his team linked four organoids into a neural “pain pathway.” The model combined sensory neurons, spinal and cortex organoids, and parts of the brain that process pain.

The scientists dabbed the chemical behind chili’s tongue-scorching heat onto the sensory side of the assembloid. It produced waves of synchronized neural activity, suggesting the artificial tissue had detected the stimuli and transferred information.

That’s not to say it felt pain. Detecting pain is only part of the story. It takes a second neural pathway, which the assembloids lacked, to trigger the unpleasant feeling. But the experiment and others underscore the need for regulation. One idea pitched at the conference is to create a new global organization similar to the International Society for Stem Cell Research.

The commission would track advances in the field and provide oversight that balances scientific merit with patient needs. During the meeting, patients and family members expressed hope that mini brains could lead to new therapies, especially for those with rare genetic disorders or severe autism.

Pasca may soon deliver on that promise. His team is working to understand Timothy syndrome, a rare genetic disorder that leads to autism, epilepsy, and often fatal heart attacks. Last year they developed a gene-altering molecule that showed promise in brain organoids mimicking the disease. The treatment also worked in a rodent model, and the team is planning to submit a proposal for a clinical trial next year.

Drawing the line for brain organoid research will require global cooperation. “A continuing international process is needed to monitor and advise this rapidly progressing field,” wrote Arlotta, Pasca, and others. While there aren’t any universal agreements yet, dialogue on ethics, including discussion and engagement with the public, should guide the nascent field.

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The treatment reprograms T cells to hunt down a patient’s cancer. The approach could speed treatment and cut costs, but needs more study.

With just a single injection, a new treatment transforms immune cells in cancer patients into efficient tumor-killing machines. Now equipped with homing beacons, the cells rapidly track down and destroy their cancerous foes.

The shot is based on CAR T cell therapy, a breakthrough that uses genetic engineering to supercharge cancer-fighting T cells. Since its first FDA approval in 2017, CAR T has vanquished some deadly cancer cases with a one-and-done treatment.

But the technology is costly—for both body and wallet. CAR T cells are usually made outside the body in a lab. Patients undergo chemotherapy and other harsh treatments to make room for the enhanced immune cells, taxing an already ailing body with side effects. Making CAR T cells also takes precious time, and unfortunately, the clock often runs out.

At this year’s American Society of Hematology Annual Meeting & Exposition, an Australian team presented a different approach: Transforming normal T cells into super soldiers inside the body. Four people treated for stubborn multiple myeloma—a blood cancer that destroys bones and kidneys—went into remission for up to five months.

Led by Phoebe Joy Ho at the University of Sydney in collaboration with Kelonia Therapeutics, the trial, although small and still preliminary, marks a step towards the next revolution in CAR T therapy. Reported in The American Journal of Managed Care, an audience member from the conference said the findings “take your breath away.”

Silver Bullet

CAR T therapy has transformed cancer care. Six formulations are approved in the United States for a variety of blood cancers. Hundreds of clinical trials that expand the life-saving technology to solid cancers—including breast and brain tumors—are underway.

Beyond cancer, the therapy is also being used to treat life-long autoimmune diseases, such as lupus and multiple sclerosis, where the body’s immune system destroys its own organs. A small trial found a single infusion of CAR T cells reduced symptoms in patients with lupus. Other efforts are using these custom living drugs to tamp down infections, restore heart health after an attack, and remove the “zombie cells” that accumulate during aging.

The procedure usually goes like this: A patient’s own T cells are extracted from their blood. Using gene editing tools, like CRISPR, the cells are supplied with extra protein “hooks.” These hooks let them better grab onto their targets—cancer cells or otherwise.

After a short course of chemotherapy or radiation to deplete existing immune cells and make room for new ones, the engineered CAR T cells are infused back into the body. Once there, the genetically engineered cells repopulate the immune system and hunt down their prey. The process, while undeniably efficient for some cancers, is costly and takes months—time that some patients don’t have.

“Off-the-shelf” CAR T is one solution. Instead of editing a patient’s own cells, scientists could transform a healthy population of donor T cells. But attempts have faced immune rejection. Even with more genetic tinkering, the cells struggle to survive and expand in the body.

One Shot Wonder

An alternative method directly converts a person’s T cells inside their own body.

In 2022, a team designed a shot to reprogram T cells using RNA. This avoids tinkering with a patient’s DNA. In mice with heart scarring, the injection revived the organ.

Other successes soon followed. Another shot converted T cells into CAR T cells within hours in mice and monkeys. The therapy targeted a type of blood cancer deriving from an overgrowth of B cells (another immune cell type). The shot boosted the immune system’s ability to destroy cancers in mice and slashed B cell numbers in monkeys. The effects lasted at least a month.

Both these treatments used fatty nanoparticles to deliver their payloads. They were also heavily modified to get around the so-called liver “sink.” Treatments often end up in the organ after injection. Careful design of surface proteins helped the therapies home in on T cells.

Gene-editors can also hitch a ride on a benevolent virus, stripped of disease-causing genes but highly efficient at tunneling into cells. Kelonia’s new technology used a virus to target T cells and avoid other cell types. One tweak, for example, added a small, engineered protein fragment that precisely targets T cells. Once inside, the payload synthesizes a gene that kills cancers.

The trick paid off. In a small trial, researchers gave the shot to four patients with previously uncontrollable multiple myeloma. The patients showed no signs of cancer in their bone marrow after a month. For one, the effect lasted at least five months. The side effects were also relatively minor, although some experienced mild cytokine release syndrome—an immune reaction that causes fever, chills, and other symptoms, which were easily managed.

The results come on the heels of a separate trial with similarly positive results. In July, four patients with multiple myeloma received an infusion of a virus carrying genes targeting T cells. Crafted by EsoBiotec in Belgium and Shenzhen Pregene Biopharma in China, the shot vanquished abnormal cells in the bone marrow of two patients after three months. The patients had previously undergone multiple cancer-related therapies to no avail.

The treatment did come with side effects. Blood pressure plunged, and two patients required supplemental oxygen. One showed confusion and temporary “brain fog.” These mental troubles aren’t common with traditional CAR T therapy, motivating researchers to find out why.

Despite risks, results from both trials highlight the promise of one-and-done CAR T therapy for deadly blood cancers. But it’s still early days. Scientists need to carefully follow patients over years to understand how long upgraded T cells remain in the body and their effect on cancers.

And not all viral carriers are made the same. Lentiviruses, used in both studies, can tunnel into the human genome, causing DNA typos that potentially trigger secondary cancers. The durability of the therapy, its longevity, and immune side effects also need to be studied.

Kelonia is adding more patients to their trial, amid an increasingly competitive landscape. AstraZeneca has acquired EsoBiotec to bring its technology to market. AbbVie, a drug company in Illinois, is testing the delivery of gene-editing tools to T cells via fatty nanoparticles in clinical trials. And Kelonia is planning a second clinical trial with an initial 20 patients and 20 more in an expansion phase, none of whom responded to at least three previous treatments.

“I think it gives us a glimpse into the future,” Ho told Science. “In vivo CAR T for multiple myeloma is here and hopefully it will stay.”

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