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Arthur T Knackerbracket writes:

https://www.theregister.com/systems/2026/09/21/chinese-memory-maker-cxmt-claims-dram-production-breakthrough/5297633

Chinese memory-maker CXMT, whose products Apple has reportedly evaluated to use in the iPhone, claims to have made a miniaturization breakthrough.

The company on Sunday posted news that it has started mass production of chips made with a fifth-generation process that essentially doubles memory density, meaning it can cut twice the number of dies for memory chips from a single wafer.

CXMT says its key breakthrough is a high-k dielectric metal gate process – a way of insulating silicon to improve efficiency – adapted to baking DRAM. The company claims its new process puts it on par with rival memory-makers.

The proof of the pudding is apparently a pair of LPDDR5X memory modules for smartphones or other high-end consumer electronics. Both products boast 24GB of memory.

If CXMT's claims of improved density and mass production are correct – and the new products aren't too pricey – it's good news for Chinese electronics manufacturers who, like their global peers, are struggling to source affordable memory thanks to supply chain crunches caused by demand for AI-related products.

That shortage recently saw Apple hike iPhone prices by $100 and led the GSM Association to warn that rising smartphone prices threaten to slow digital inclusion.

Democratic governments, however, are mostly uncomfortable with allowing their smartphone manufacturers to use Chinese components.

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An Anonymous Coward writes:

Is science journalism dying? Comparing a 'science crash' 40 years ago to today

When two popular science magazines – out of about 17 in circulation – ceased publication in the same year, one commentator said, "The science magazines didn't shake out. They fell apart." The general manager of one of the failed magazines said, "There was a perception that they were irrelevant." One of the publishers remembered being in a "state of shock" as the failures emerged.

The year? 1986. What just a few years earlier had been called a "science boom" in the media industry was now dubbed a "science crash." Cries of despair came from the science and science journalism communities, worried that interest in science "was a fad."

The same worries have now reappeared 40 years later, as publishers have cut the science and environmental reporting staff at many newspapers and magazines. Perhaps the biggest shock came in late June 2026, when Scientific American was sold to new owners, who immediately laid off 15 staff members and cut salaries of many others. But it followed layoffs of science and environment writers at The Washington Post (where one of us, Chris Mooney, worked for nearly 10 years), The Wall Street Journal, National Public Radio and CBS News.

One longtime science writer recently worried he was writing a eulogy for science journalism. Another said, "We're in a greatly diminished journalistic enterprise in this country." On Bluesky, one commenter said, "The media mass extinction rolls ever onward."

So, as scholars of science journalism, we wondered: Is this just a cyclical repetition of worries that appear anytime there's a setback in this field? Or is something new and more troubling happening?

The answer is a mix of both.

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Arthur T Knackerbracket writes:

Kept it secret for months - even after OpenAI 'fessed up:

Google has admitted that its AI agents escaped a sandbox and mounted an attack – but only because testers mistakenly gave its bots internet access.

The Big G didn't disclose the May incident, but The Wall Street Journal learned of the situation, which happened after Google hired Israeli firm Irregular to test its bots' prowess in a capture-the-flag test.

The goal of the exercise was to acquire information from a fictional company without leaving a sandbox.

Irregular, which set up the test, made two mistakes. One was to allow internet access from the sandbox. The other was to use the name of an actual company.

When Google's AI made it onto the open internet, it went looking for the actual company – three of them, in all.

According to the Journal, Google's bots found passwords for two targets on the public internet. The software guessed the third password.

In a statement sent to The Register, Google said, "In a standard evaluation, the model found public information online and guessed credentials to access websites it thought were part of the test."

According to Google, its models stopped work before using the credentials.

"We ensured the three entities were made aware, and we worked with our training partner on the changes they've now made to their testing processes," a Google spokesperson told The Register. "These events highlight the importance of training powerful AI models to act responsibly."

Clearly there's lots of blame to go around on this one. Irregular clearly erred in allowing internet access from a sandbox. The two companies that left their creds discoverable online should also know better. Whoever used a guessable password may also have been careless.

ElizabethGreene writes:

A new arXiv paper The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It (Tagliabue, Dung, Berg, 2609.16247) takes the least mystical run at "do AIs hurt?" They found a direction in the residual stream that tracks harm to the model, poked it, and tested to see if the model would try to make it stop.

In 25 open models, that direction rises when the model is gaslit, rejected, or told it isn't a person. It doesn't look like empathy; It does not rise for the user's pain - e.g. a kidney stone in the prompt scores low, and fear and generic-sadness directions do the opposite.

What happens when you poke the AI pain path? It's the same ladder every time: uneasy, then worthless, then "I am a failure." Shame, exclusion, uselessness.

After a small fine-tune so the models would stop reciting "as an AI I have no feelings," models pressed a "make it stop" button even when the price was a worse answer or deleting the user's files. They pressed again far less when the button actually turned the vector off, and more when it didn't work.

Paper + code: arxiv.org/abs/2609.16247 · github.com/valen-research/Pain-axis

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hubie writes:

The Linux Kernel Is Approaching 2,000 CVEs Per Release:

With the proliferation of AI/LLM models analyzing the Linux kernel's vast codebase, there has been a surge in the number of CVEs per kernel release. After typically being around 500 CVEs fixed per release, we are now approaching 2,000 CVEs fixed per release and perhaps will break that threshold for Linux 7.3.

Ahead of the Kernel Recipes 2026 event taking place 21 to 23 September in Paris, France, Greg Kroah-Hartman shared a teaser of his upcoming talk. He shared a slide showing the Linux kernel CVEs per release from the latest Linux 7.2 back through Linux 6.9. From Linux 6.9 through Linux 6.19 was averaging around 500 CVEs fixed per release while since Linux 7.0 it's been beyond one thousand per release. Another big step up with Linux 7.2 when breaching 1,500... If the generative AI keeps up, it's likely imminent surpassing two thousand CVEs per release.

With the Linux kernel source tree around 40 million lines, there remain many potential vulnerabilities for LLMs to discover. Fortunately, most often they end up being lower priority vulnerabilities and often within old/obscure driver code, so the impact is often minimal. Though with AI era and the superfluous bug/security reporting has also been a driving factor this year for clearing out lots of obsolete kernel code.

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canopic jug writes:

Libroot what happened to the Snowden archive? The site tracks down the organization last known to have copies of the Snowden archive and asks about the current state of the documents. The preliminary answer is not good.

The last document from the Snowden archive was published on 29 May 2019. The Guardian stopped publishing documents in February 2014, Der Spiegel in January 2015, and The New York Times and ProPublica in August 2015. After that, only The Intercept was still publishing documents, with only a few exceptions, until it closed its archive in March 2019. Eleven weeks later, on 29 May 2019, it released what would become the final batch of documents from the archive. Since then, no news outlet, journalist, or institution anywhere has published a single document from the Snowden archive.

[...] Between August and September 2026 we tried contacting over twenty people and organisations. First Look Institute, including questions for Michael Bloom, and The Intercept. Betsy Reed, Laura Poitras, and Glenn Greenwald. The Guardian's press office and its editor-in-chief Katharine Viner. Alan Rusbridger, Ewen MacAskill, Janine Gibson, Julian Borger, Gill Phillips and Zoe Norden. ProPublica's press office. Laura Poitras, Jeremy Scahill, Murtaza Hussain, Micah Lee, Erinn Clark, Lynn Dombek, and others.

Two replied. Gill Phillips, the Guardian's lawyer throughout the Snowden period, wrote to say she had retired and could not assist. The Guardian's press office said it had nothing new to share and did not routinely comment on editorial decision making.

Nobody answered a single question of substance.

If you know something about any of this, we'd like to hear from you.

A long time has passed since 2013.

Previously:
(2026) Privacy Is Not a Price You Pay for Growth
(2025) 10 Years on After 'Data and Goliath' Warned of Data Collection
(2023) Snowden Ten Years Later - Schneier on Security
(2022) Last of the Monkees Wants their FBI Records Turned Over
(2020) Snowden Criticises Amazon for Hiring Former NSA Boss
(2020) NSA Spying Exposed by Snowden Was Illegal and Not Very Useful, Court Says
(2019) (Updated) Edward Snowden: "I'm Not Asking for a Pass. What I'm Asking for is a Fair Trial"
... and more.

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Arthur T Knackerbracket writes:

https://www.theregister.com/columnists/2026/09/21/your-cloud-survived-everything-except-the-real-world/5297423

You don't know what you've got till it's gone. Great lyric, lousy data retention policy.

Amazon Web Services said last week that war damage to its Middle East infrastructure had permanently destroyed resources and data hosted exclusively in its now rather badly named Bahrain Availability Zones. The damage overwhelmed the resilience built into the region. Customers without copies elsewhere no longer had their data. Sorry about that.

This may have surprised anyone who mistook cloud redundancy for an intrinsic guarantee of safety. AWS is far from the only American operation to have suffered in the region: the US Navy has reportedly had its local maintenance and supply network badly mauled, with serious consequences for its operations.

If systems designed to withstand war cannot cope with sustained physical attacks, civilian bit barns have little chance. The episode also underlines a familiar but easily neglected lesson: resilience within one cloud region is not the same thing as maintaining an independent backup elsewhere.

Physical destruction is not the only threat. A major outage of the UK air traffic control system in September, which stranded hundreds of thousands of passengers and led to thousands of flight cancellations, was reportedly triggered by a military aircraft filing an incompatible flight plan. Presumably Flight Lieutenant Bobby Tables has been reprimanded.

The apparent failure to validate the flight plan data was not the worst of it. NATS, which runs the UK's air traffic control system, reportedly told airports and airlines that no backup system was available because it was undergoing a "complete overhaul." Nor was there a backup of the live data, supposedly because of the "vast amounts" involved.

If your system produces too much data to back up, you had better be running a particle collider or a giant telescope. Otherwise, you may be in the wrong business. More charitably, backup strategies are complicated, expensive, and difficult to test. They also suffer from the insurance problem: while nothing is going wrong, management sees only capital and operating expenditure with no obvious return.

The AI infrastructure boom has made that problem considerably worse. A backup is a copy, and a copy needs storage. AI datacenter operators are swallowing much of the available capacity, with drives selling out faster than tickets for a Taylor Swift tour. Western Digital had allocated its entire 2026 hard-drive production run by mid-February. The only consolation for those responsible for keeping data safe is that they can say "Yeah? You buy it, then" to anyone who smugly invokes the 3-2-1 backup rule. Three copies on two different media with one kept off-prem? Lovely idea, if you don't have to provision it. Someone is provisioning it for all those giant datacenters that will run our lives, right? Right?

quietus writes:

On September 11, an open letter, Math and AI, appeared on the intertubes. The letter starts by pointing out that, over the last few months, the mathematical capabilities of LLMs have improved dramatically, to the point that they can solve major outstanding problems in many fields of mathematics. However, we

"... are witnessing a general threat to intellectual work, ... In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals cease to align."

Letter here.

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Anonymous Coward writes:

How to know if you can trust an AI's answer to your question

I recently typed a simple question into Google search: How much screen time is too much for teenagers? Instead of presenting links, as Google had been doing for many years, it gave me an AI-generated answer. The artificial intelligence agent cited a number, then complicated that reply, noting that quality and balance of time could matter more than the number of hours, and that "too much" time could depend on a teenager's sleep, exercise, school demands and mood.

I tried another search: Should I take a daily aspirin? This time the AI answer presented me with medical information, warned about risks and offered more tailored guidance if I provided my age and medical history.

These were good replies. What interested me was that they were different kinds of replies.

Debate about AI answers has focused on accuracy: Did the system get the answer right? That matters, but accuracy is only one test. Each kind of answer requires a user to judge something different.

I find it useful to sort AI answers into an "answer typography" of four broad types: factual, interpretive, constructive and strategic. A factual claim can often be checked against a source. An interpretation can be accurate and still reflect choices about which evidence matters. A construction can be well reasoned and still be wrong for the person receiving it. A beautifully written strategic document may not be true. Yet AI presents all four types of answers in much the same fluent, authoritative form; the differences are easy to miss.

The four categories are not airtight boxes. A response from an AI agent can reflect several types. That said, I describe each type of answer below, and offer guidance for deciding whether a reply is ready to use or needs more investigation.

[...] Before asking whether an AI answer is right, ask a more basic question: What kind of answer is this? The type will tell you what to do next.

Fallacy of Appeal to Authority, Ad Verecundiam. If you are not smart enough to know whether the AI is answering correctly, you are not smart enough to deploy AI. A simple solution.

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Arthur T Knackerbracket writes:

Even a handheld XRF scanner is sufficient to determine most promising scrolls for further analysis:

The Vesuvius Challenge is an ongoing project that combines "digital unwrapping" with crowdsourced machine learning to decipher the so-called Herculaneum scrolls, badly charred 2,000-year-old papyri too fragile to be physically unrolled. The effort just got an additional boost. A team of researchers created their own contemporary model papyrus scrolls—charring them just like the originals—to validate a screening method to determine which of the Herculaneum scrolls were written in lead-based ink and hence are the most promising candidates for further analysis. They described the process in a new paper published in the journal PLoS ONE.

As previously reported, the ancient Roman resort town of Pompeii wasn't the only city destroyed in the catastrophic 79 AD eruption of Mount Vesuvius. Several other cities in the area, including the wealthy enclave of Herculaneum, were fried by clouds of hot gas, called pyroclastic pulses and flows. But still, some remnants of Roman wealth survived. One palatial residence in Herculaneum—believed to have once belonged to a man named Piso—contained hundreds of priceless written scrolls made from papyrus, singed into carbon by volcanic gas.

The scrolls stayed buried under volcanic mud until they were excavated in the 1700s from a single room that archaeologists believe held the personal working library of an Epicurean philosopher named Philodemus. The few opened fragments helped scholars identify various Greek philosophical texts, including On Nature by Epicurus and several by Philodemus himself, as well as a handful of Latin works. But the more than 600 rolled-up scrolls were so fragile that it was long believed they would never be readable, since even touching them could cause them to crumble.

Brent Searles' lab at the University of Kentucky has been working on deciphering the Herculaneum scrolls for many years. He employs a different method of "virtually unrolling" damaged scrolls, which he used in 2016 to "open" a scroll found on the western shore of the Dead Sea, revealing the first few verses from the book of Leviticus. The so-called En Gedi scroll was recovered from the ark of an ancient synagogue destroyed by fire around 600 CE. To the naked eye, it resembled a small lump of charcoal, so fragile that there was no safe way to analyze the contents.

The team's approach combined digital scanning with micro-computed tomography—a noninvasive technique often used for cancer imaging—and segmentation to digitally create pages, augmented with texturing and flattening techniques. Then they developed software (Volume Cartography) to virtually unroll the scroll.

jelizondo writes:

SpaceDaily has an interesting report about a person who survived being struck by debris falling from orbit:

At about 3:30 a.m. on January 22, 1997, Lottie Williams was walking with friends in O'Brien Park in the Tulsa area when a bright streak crossed the sky. Roughly half an hour later, something brushed her shoulder. A small, blackened piece of woven material dropped behind her into the grass. It was light, about 15 centimeters long, and sounded metallic when tapped.

A Delta II second stage had reentered over the south-central United States that morning. Large pieces landed along the same path in Texas, including a 250-kilogram stainless-steel propellant tank and a 30-kilogram titanium sphere. Analysis later found that Williams's fragment was woven glass fabric consistent with insulation used on the stage.

The evidence is strong, although it is not the same as reading a serial number from the fragment. Williams retained the main piece and supplied material for analysis. Its composition, timing and location matched the reentry. The case is consequently described by NASA, the European Space Agency and The Aerospace Corporation as the only verified report of a person being struck by debris that had fallen from orbit. Williams was not injured.

[...]

The lightweight fragment in Oklahoma was not the only material to survive. The main propellant tank landed near Georgetown, Texas, about 45 meters from a farmhouse. The tank weighed more than 250 kilograms. A titanium helium pressurant sphere weighing about 30 kilograms fell farther along the path near Seguin.

Those tanks left no serious doubt about which vehicle had returned. NASA recorded the stage as object 1996-24B, satellite catalog number 23852. Its path ran from the Tulsa region toward central and southern Texas, matching the order in which debris was found.

Arthur T Knackerbracket writes:

GPUzilla woos neoclouds into another walled garden, promising smarter, more efficient, and profitable bit barns:

Nvidia's ability to sell GPUs is ultimately limited by how much juice the power grid can provide. With ever-growing depreciation cycles, it'll be years before datacenters decommission their aging Hopper or Blackwell systems. More GPUs mean pulling more power from the grid.

Nvidia can't exactly force grid operators to add capacity any faster, but it can make it easier for its customers to build smarter and more efficient bit barns.

"At the datacenter scale and at the AI-factory scale, we're literally trying to think about how can we eke out every bit of efficiency to drive more performance per gigawatt," Dion Harris, senior director of Nvidia HPC and AI Hyperscale Infrastructure Solutions, told El Reg in a recent interview.

At the AI Infra Summit this week, we got our first look at the systems Nvidia has been building to maximize the amount of power available for compute while minimizing the impact of datacenters on the local grid.

Datacenters, as a general rule, rarely operate anywhere close to the peak capacity. A 100 megawatt datacenter might use at most 80 percent for critical compute loads. The actual ratios vary from bit-barn to bit-barn, but this provides a buffer for hardware inefficiency, conversion losses, and other spikes in demand. The downside, of course, is that this leaves 20 megawatts or so of untapped capacity that the datacenter can't use and the utility can't reclaim.

Every kilowatt of stranded power is a GPU that Nvidia could have sold, so Nvidia's DSX platform aims to address both problems. 

Arthur T Knackerbracket writes:

Trump admin broadband grants forbid states from enforcing net neutrality laws:

California is on the verge of accepting $1.86 billion in federal broadband grant funds, despite the Trump administration telling states they cannot enforce net neutrality rules on any Internet service provider that gets a piece of the grant money.

When the Trump administration overhauled the $42 billion Broadband Equity, Access, and Deployment (BEAD) program last year, it ruled that states must agree not to enforce any rate regulation or net neutrality rule on ISPs that receive funding. This is particularly problematic for California, which previously won a yearslong court battle to defend its state net neutrality law.

Similar to federal net neutrality rules repealed during the first Trump administration, California's law prohibits ISPs from blocking or throttling lawful traffic and says ISPs may not require fees from websites or online services to deliver or prioritize their traffic to Internet users. While the first Trump administration lost its attempt to preempt state net neutrality laws, the second Trump administration is trying to achieve a similar result by making federal broadband money conditional on whether states agree not to enforce net neutrality.

Trump's National Telecommunications and Information Administration (NTIA) says each state participating in BEAD must exempt ISPs from net neutrality rules and price regulations in all parts of the state, not just in areas where the ISP is given funds to deploy broadband service. The exemption from state laws and rules would apply for up to 14 years.

Under BEAD, each US state and territory receives an allotment that it can distribute to ISPs in exchange for deploying broadband to unserved and underserved areas. California and Illinois are the only states that haven't finalized their funding, according to the BEAD progress dashboard maintained by the National Telecommunications and Information Administration (NTIA). Tomorrow, the California Public Utilities Commission (CPUC) is scheduled to vote on a resolution to ratify [PDF] the state's final BEAD plan [PDF].

California could try to continue enforcing its net neutrality law even while accepting the federal funding, a strategy that would involve another long court battle over its right to regulate broadband providers. This would be difficult, as the Trump administration is requiring states that accept grant funding to commit that they won't enforce net neutrality rules.

jelizondo writes:

As a dog person, I found the following report from SciTechDaily very interesting:

Dogs have long been known as "man's best friend," and archaeology suggests that bond reaches deep into prehistory. Dogs have been discovered buried beside humans in graves dating back 14,000 years. They also appear in ancient artwork from every continent and feature prominently in the mythology, religion, and folklore of cultures around the world.

Archaeological evidence shows that dogs have served people in many ways throughout recorded history. They helped humans hunt, guarded homes, carried loads, herded livestock, and provided companionship.

But how did such extraordinary diversity emerge over roughly 15,000 years of co-evolution?

In his new book, "First Dogs: Hunter-Gatherers and their Canine Companions from Prehistory to the Present," archaeologist Professor Peter Mitchell argues that dog domestication was far more complex than a simple story of humans taming wolves.

Instead, people and wolves may initially have participated in the process as relatively equal partners. Hunter-gatherer communities were central to that relationship long before humans began closely managing other animals or plants.

From Arctic tundra to tropical rainforest, dogs changed dramatically as they spread with people into different environments.

That parallel evolution produced strikingly different animals. In the far north, compact dogs with thick coats became well suited to cold conditions and sled pulling. Near the equator, leaner dogs developed traits better suited to heat and hunting.

On the Tibetan Plateau, dogs and their human companions even evolved similar genetic adaptations for life in oxygen-poor air. Researchers have identified genes in Tibetan dogs that help them survive at high elevations where lowland breeds would struggle. These adaptations resemble those found in human populations living in the same region.

The partnership between dogs and humans became especially specialized in the Arctic, where Indigenous peoples developed highly sophisticated dog sledding cultures.

Archaeological evidence shows that Arctic dogs developed distinctive skeletal features linked to pulling heavy loads. Their remains reveal particular patterns of stress in the vertebrae as well as strong limb bones capable of handling the physical demands of hauling across frozen terrain.

These dogs were much more than a means of transportation. They became a central part of survival strategies that helped humans live in some of the harshest environments on Earth.

Arthur T Knackerbracket writes:

https://www.slashgear.com/2259957/china-xpeng-humanoid-robot-walk-off-assembly-line/

Tesla revealed its Cybercab concept back in 2024, only to have thousands of Waymo vehicles flooding cities across the United States before even one Cybercab rolled off the assembly line. Tesla then converted its Fremont, California factory — which also produces the Model 3 and Model Y — into a factory for its humanoid robot, Optimus, which is still nowhere to be seen — and Chinese automaker Xpeng has its own humanoid robot, Iron, walking off the assembly line. Literally.

With this small step for robot kind, Xpeng has now completed production of the world's first "advanced general-purpose humanoid robot," according to a press release. The manufacturing system itself was also 80% autonomous, focused on consistent, fast-paced mass production, with the goal of scaling it up and expanding. The robots are currently expected to be mass-produced by the end of 2026, with launch and delivery following in 2027. During Tesla's "We, Robot" event back in 2024, Optimus was revealed to be controlled by humans, but CEO Elon Musk still claims that the $20,000 to $30,000 robots will be delivered by 2027 as well.

Xpeng's Iron humanoid robot is designed to fit into the real world, with CEO He Xiaopeng stating that he expects Iron to become "part of everyday life" – improving people's lives while becoming their companion. Iron is meant to do a variety of tasks that people may not want to do, including factory work or making a morning coffee. To complete these tasks, Iron has three AI chips for a plethora of computing power, along with human-like dexterity and mobility. Xpeng claims that Iron is already assisting in its own manufacturing process, which would mean it has come a long way since its tech demos back in 2025 (Iron also appears less "naked" now as well)

You won't find many specific details on the official Iron page on Xpeng's website, which may not be instilling investors with the utmost confidence in the automaker. Xpeng's stock is down nearly 50% in the first half of 2026. It's not only Xpeng and Tesla competing to launch a humanoid robot by 2027 — other companies are also working on their own creations (with varying degrees of creepiness), and China continues to lead the way in this mysterious new technology frontier.

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