Will this solve the AI boom’s power and water bottlenecks?
Category: robotics/AI
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:
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/ 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.
Beyond 3D: Data scientists introduce novel AI tool to interpret complex biological data
As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, possess depth perception, and visually examine and enjoy all manner of objects and happenings.
But trying to envision subvisible structures and high-dimensional processes that our human-engineered scopes can’t capture is a challenge for data scientists and visualization experts, who turn to machine learning and AI tools to amplify visual exploration.
“Biological processes are an example of complex, high-dimensional data,” says Kevin Moon, director of USU’s Data Science and Artificial Intelligence (DSAI) Center and associate professor in the Department of Mathematics and Statistics.
Critical Cursor Flaws Could Let Prompt Injection Escape Sandbox and Run Commands
Two flaws in Cursor, an AI code editor, could let a single, ordinary-looking prompt break out of the editor’s safety sandbox and run any command on a developer’s computer. There is no click to fall for and no approval box to ignore.
Cato AI Labs found the pair and named them DuneSlide. They are tracked as CVE-2026–50548 and CVE-2026–50549, both rated 9.8 out of 10 (or 9.3 under the newer CVSS 4.0 scale).
The fix is already out. Both bugs are patched in Cursor 3.0, released April 2, and every version before 3.0 is affected. Cursor’s maker says more than half the Fortune 500 use the tool, so if you run it, update now.
