Toggle light / dark theme

Brain dynamics of the « wave of death » highlighted for the first time

In 2023, scientists at the Paris Brain Institute investigated one of the most fascinating and unsettling transitions in neuroscience: what happens to the cortex when the brain is deprived of oxygen.

In a rat model of systemic anoxia, researchers found that the dying brain does not simply “shut off” all at once. Instead, cortical activity follows a structured sequence: brief high-frequency activity, slowing oscillations, electrical silence, and then a massive wave of anoxic depolarization — often called the “wave of death.”

This wave appeared to begin deep in the neocortex, especially around layer 5 pyramidal neurons, before spreading upward toward the cortical surface and downward toward the white matter. These neurons are large, metabolically demanding projection cells, which may make them especially vulnerable when oxygen and ATP collapse.

But the most important part of the study is that this wave did not always represent an absolute point of no return. When oxygenation was restored within a critical window, researchers observed a “wave of resuscitation,” followed by partial recovery of synaptic activity.

That does not mean death has been “reversed” in a simple or sensational sense. But it does suggest something scientifically powerful: the boundary between life and death in the brain may be more dynamic, layered, and measurable than we often imagine.

This is where the implications become fascinating.

If the “wave of death” is an organized biophysical event, future neurocritical care may one day be able to detect the brain’s approach toward irreversible injury in real time. Instead of relying only on broad markers like heartbeat, oxygen saturation, or flat EEG, clinicians may eventually use more detailed brain-state monitoring to identify whether the cortex is entering a reversible, borderline, or irreversible phase.

Airborne AI spots underwater munitions in shallow seas with high precision

A new airborne imaging approach can reliably detect unexploded weapons that lie in shallow coastal waters and remain an ongoing hazard to public safety, marine ecosystems and infrastructure worldwide. By combining advanced multispectral sensing with artificial intelligence, the researchers were able to identify underwater munitions with high confidence, even when they are partially hidden by sediment, biological growth or debris.

Scientists at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science developed and tested the approach and published their findings in the April issue of Frontiers in Marine Science. The study demonstrates that integrating NASA underwater imaging technologies with machine learning enhances detection accuracy while reducing false positives in complex marine environments.

“Unexploded ordnance in shallow waters remains a serious global challenge,” said Ved Chirayath, Vetlesen Endowed Chair of Earth Sciences in the Department of Ocean Sciences, the study’s lead author. “Our results demonstrate a scalable, airborne solution that can help improve detection accuracy and support safer coastal environments.”

Giving drones a sense of ‘pain’ could help them predict instability before it happens

Imagine you’re running and you sprain your ankle. The pain makes you gingerly limp the rest of the way home. This is a great example of how nature adapts to failures in a system. The pain tells you: “If you continue running like normal, the injury will only get worse.” So you naturally adjust the way you run. Drones currently cannot do this with a worn-out propeller.

Researchers from Delft University of Technology and Wageningen University & Research have now demonstrated that a concept we learned from nature, which was originally developed to predict collapse in ecosystems, can also help detect when engineered systems are heading toward failure. This is crucial for ensuring drone and autonomous vehicle safety as they increasingly become part of everyday life.

“You can compare our approach to the way humans experience pain. After an injury, pain provides immediate feedback about our condition and helps us judge what actions remain safe,” says Jasper van Beers, a researcher at Delft University of Technology. “Machines generally lack this form of self-awareness. The new indicators, derived from real-time measurement data, offer a first step toward giving engineered systems a similar ability to recognize when they are approaching their limits.”

Startup activates nuclear microreactor live on stage to power an Nvidia RTX Spark desktop PC — firm working with Nvidia to build a 30MW closed loop AI factory that doesn’t use local water

Will this solve the AI boom’s power and water bottlenecks?

The circuit that lets your brain think and see

Nuttida Rungratsameetaweemana is challenging a story neuroscience has told for decades. According to the conventional account, our eyes collect raw information and relay it through a series of nerves and waystations that lead deep into the brain, eventually reaching the cortex. There, the thinking begins as information is processed and put to use for higher tasks such as reasoning, judgment and decision-making.

Her group’s work is complicating that account. Last year, the team published fMRI scans showing unexpected levels of activity in the earliest visual areas of the cortex, the regions that first receive visual signals. Rather than passively relaying what the eyes take in, those early areas seemed to process the same information differently depending on what the research participant was doing. When asked to sort shapes by one set of rules, a participant’s early visual system behaved one way. When asked to apply a different set of rules to the same shape, it behaved differently.

In a new paper published today in PLOS Biology, Rungratsameetaweemana and her team at Columbia Engineering show how the brain might pull this off. They built a simple neural network that follows many of the rules that govern real brains. Like the brain, their model contained one class of neurons that drive other neurons to fire and another class that suppress firing.

Cyborg Luddite Steve Mann: Technology That Masters Nature Isn’t Sustainable

14 years ago, Steve Mann told me that technology that masters nature is not sustainable.

At the time, that sounded like the poetic caution of a man the media had nicknamed “the cyborg Luddite.” Today it reads like a weather report.

Steve is the person the IEEE named the father of wearable computing. He built the EyeTap decades before Google Glass, invented HDR imaging now sitting in the phone in your pocket, and was called the world’s first cyborg. So when he argues for using less, for choosing which technologies to embrace and which to walk away from, he is not speaking from fear of the machine. He is speaking from a deeper intimacy with it than almost anyone alive.

His core move was to refuse the framing everyone else accepted.

Not more technology. Not less technology. Appropriate technology. Balanced with nature instead of replacing it.

And here is the line that has aged into something close to prophecy:

Is AI the Great Filter?

Artificial intelligence may be civilization’s greatest tool… or its last. Could AI explain why the galaxy seems silent, or does it make the Fermi Paradox even harder?

Get Nebula using my link for 50% off an annual subscription: https://go.nebula.tv/isaacarthur Watch my exclusive video Colonizing Ocean Worlds: https://nebula.tv/videos/isaacarthur–… out Gods \& Monsters: https://nebula.tv/curiousarchive/gods… 🛒 SFIA Merchandise: https://isaac-arthur-shop.fourthwall… 🌐 Visit our Website: http://www.isaacarthur.net ❤️ Support us on Patreon: / isaacarthur ⭐ Support us on Subscribestar: https://www.subscribestar.com/isaac-a… 👥 Facebook Group: / 1,583,992,725,237,264 📣 Reddit Community: / isaacarthur 🐦 Follow on Twitter / X: / isaac_a_arthur 💬 SFIA Discord Server: / discord Credits: Is Artificial Intelligence the Great Filter | The Fermi Paradox Written, Produced & Narrated by: Isaac Arthur Select imagery/video supplied by Getty Images Chapters 0:00 Intro 2:03 The Intelligence Threshold 5:07 The Intelligence Explosion 12:13 The Alignment Bottleneck 15:43 The AI Arms Race Filter 19:44 Autonomous Warfare 23:47 The Intelligence Trap Hypothesis 27:31 Gods & Monsters 28:41 AI as a Civilizational Stabilizer 30:37 The Last Tool Problem 34:33 AiLIEN MINDS 35:22 The Machine Ecology Hypothesis 38:58 Is AI the Great Filter?

🛒 SFIA Merchandise: https://isaac-arthur-shop.fourthwall… 🌐 Visit our Website: http://www.isaacarthur.net ❤️ Support us on Patreon: / isaacarthur ⭐ Support us on Subscribestar: https://www.subscribestar.com/isaac-a… 👥 Facebook Group: / 1,583,992,725,237,264 📣 Reddit Community: / isaacarthur 🐦 Follow on Twitter / X: / isaac_a_arthur 💬 SFIA Discord Server: / discord Credits: Is Artificial Intelligence the Great Filter | The Fermi Paradox Written, Produced \& Narrated by: Isaac Arthur Select imagery/video supplied by Getty Images.

Chapters 0:00 Intro 2:03 The Intelligence Threshold 5:07 The Intelligence Explosion 12:13 The Alignment Bottleneck 15:43 The AI Arms Race Filter 19:44 Autonomous Warfare 23:47 The Intelligence Trap Hypothesis 27:31 Gods \& Monsters 28:41 AI as a Civilizational Stabilizer 30:37 The Last Tool Problem 34:33 AiLIEN MINDS 35:22 The Machine Ecology Hypothesis 38:58

Meituan Trains the First Frontier-Scale LLM Entirely on Chinese Domestic Chips: LongCat-2.0

* Performance: The model is optimized for “agentic coding” tasks. In benchmarks, it scored 59.5 on SWE-bench Pro, surpassing Google’s Gemini 3.1 Pro and slightly exceeding OpenAI’s GPT-5.5. It also performed strongly on other agent and reasoning tests.

* Inference and Release: Before its official launch, it operated anonymously on OpenRouter as “Owl Alpha,” becoming one of the platform’s top three most-used models. The model weights and technical infrastructure are expected to be released soon on platforms like Hugging Face. API pricing is set at $0.75 per million input tokens and $3 per million output tokens, with promotional rates available.


Meituan trained LongCat-2.0 on over 50,000 unnamed Chinese AI ASICs arranged in superpods with high-bandwidth interconnects. The chips share architectural similarities with Huawei’s Ascend 910C series, though Meituan has not publicly named the exact vendor.

The training run consumed more than 35 trillion tokens, including hundreds of billions of tokens with approximately 1-million-token context lengths. This level of scale — previously achieved only on NVIDIA GPUs or Google TPUs — required extensive custom engineering in parallelism, fault tolerance, and numerical stability.

The team implemented 6D parallelism (tensor, context, expert, data, pipeline, and embedding parallelism) to efficiently distribute both the MoE layers and the novel embedding components across the cluster.

DNA-based nanoswitch can flip in milliseconds and stay in one state for days without continuous forcing

Scientists have engineered a nanoscale switch using DNA “origami.” Inspired by macroscale mechanical switches, the device achieves long-term functionality without the continuous forcing mechanism that past versions required while remaining capable of fast switching. The paper is published in the journal Science Robotics.

This is not the first time scientists have used DNA as a building material. DNA origami—a technique that folds a single-stranded DNA scaffold into precise 2D or 3D shapes using short DNA strands—offers a way to build custom nanomachines. It has been used in everything from drug delivery to electrically actuated devices. However, in electrically actuated devices, many prior designs faced a trade-off between speed, stability and durability.

In particular, researchers have been interested in creating nanoscale switches that act like their macroscopic counterparts. So far, attempts at DNA-based nanoswitches have lacked either long-term stability without continuous forcing, millisecond switching or high cycle endurance. Many earlier devices relied on DNA “latches,” but these were slow or prone to spontaneous dissociation from natural nanoscale thermal movements.

/* */