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What's your plan to deal with the erasure of digital privacy?

  • Total Lockdown: Self-hosting everything on a decoupled, air-gapped home server rack routing everything through an onion network.
  • Malicious Compliance: Opting out of every tracking cookie manually while feeding data brokers an identity consisting entirely of randomized variables.
  • Strategic Capitulation: Accepting that my vacuum cleaner and refrigerator know more about me than my family does.
  • Reverting to Analog: Throwing my smartphone into a river and going back to ham radio and writing letters.
  • What Me Worry?
  • Other (note in comments)

[ Results | Polls ]
Comments:99 | Votes:213

posted by jelizondo on Sunday October 04, @07:04PM   Printer-friendly
from the so.many.cameras dept.

I spy with my many cameras a face. A six month project. Millions of scanned faces. Nobody on any watch list found. Are the crooks to smarts to be caught on camera or don't they ride the tube?

Solution? Extend the scope of the project for a longer time period and include more stations.

A six-month trial of live facial recognition (LFR) technology in London's railway stations that cost more than £320,000 and almost 100 hours of police officers' time led to one false match against a watchlist of suspects and no arrests.

More than half a million faces were scanned between February and July this year in some of the capital's busiest transport hubs during the British Transport Police (BTP) trial of the surveillance technology, which aimed to help catch offenders and people breaching court orders.

They added: "During these deployments, officers have made a number of associated arrests, including for assault, theft, possession of an offensive weapon, breach of a criminal behaviour order and public order offences, as well as locating individuals wanted by the courts and other police forces. As these arrests did not result directly from an LFR alert, they are not included within LFR performance data.

Or there are hidden side effects that they failed to include. It wasn't the purpose. But they found a purpose.

Still the question should perhaps be asked -- worthwhile project or waste of money?

https://www.theguardian.com/technology/2026/sep/29/trial-live-facial-recognition-cameras-london-stations-false-positive


Original Submission

posted by jelizondo on Sunday October 04, @02:21PM   Printer-friendly
from the encroachment dept.

BMW announced Wednesday it planned to slash its management structure by 20% while expanding the use of artificial intelligence across its operations:

The luxury carmaker said it will reduce the number of divisions and associated management roles by 20% by mid-2027, with comparable reductions at lower organizational levels. BMW said AI will play a central role in making the company more efficient, speeding up decision-making and automating routine work across development, manufacturing, purchasing, sales and other operations.

The restructuring comes as BMW faces weak European demand, growing competition from Chinese automakers and U.S. tariffs, while its shares have fallen more than 30% over the past year, according to Reuters. The automaker is also seeking to restore its automotive operating margin after it fell to roughly 2.3% in its latest results.

"Consistent use of agentic AI applications across all areas of the company will be a game-changer for more agile and efficient development, leaner structures and faster decision-making," BMW finance chief Walter Mertl said.

[...] The company already uses AI throughout its factories for digital twins, quality inspections and autonomous transportation systems and plans to have digital AI agents perform increasingly challenging tasks autonomously.

Related:


Original Submission

posted by hubie on Sunday October 04, @09:38AM   Printer-friendly

https://www.slashgear.com/2272902/uk-traffic-camera-ghost-license-plate-detection/

Fabricating or altering license plates can help drivers become untraceable in the system, allowing them to get away with otherwise law-breaking activities. With over 14,000 cameras in the UK's automatic number plate recognition (ANPR) system, drivers attempting to evade detection, whether for illegal activities or to get away with reckless driving, have been using special plates — also known as ghost plates — that make registration numbers unreadable to these AI-powered data-collecting systems.

U.K. law enforcement has been fighting ghost plates for a while now — and a new camera could be the answer. A new camera from ANPR tech firm MAV Systems can allegedly detect and identify ghost plates by using AI to analyze the plates' infrared and color images. Tests have been positive, with U.K. police detecting anywhere between a few hundred and a few thousand ghost plates a day in some heavily trafficked areas. Some of these tests have gone on for nearly two years as of September 2026, suggesting the system works.

At a glance, ghost plates look very much like your everyday license plates. They even have the correct details for the vehicle they're on, so they appear legal to passing police officers. However, ghost plates use reflective plastic that makes them nearly impossible for ANPR cameras to read.

These ghost plates aren't some black-market product, either. U.K. drivers can purchase them from many of the 30,000-plus businesses registered with the UK's Driver and Vehicle Licensing Agency (DVLA). That means drivers are buying legally supplied plates instead of getting them from a shady back-alley operation. In its report, the BBC found that some of these businesses openly offered ghost plates to help evade cameras without even requiring proof of ID or vehicle registration. To combat this, U.K. police want tighter regulation for license plate issuers — right now, all you need to do is fill out a quick form and pay a fee.

Ghost plates aren't unique to the U.K., of course. In New York City, for example, drivers have used ghost plates to avoid highway tolls. They're also not the only way drivers try to evade cameras; other methods include illegal plate flippers that obscure plates from police and cameras.


Original Submission

posted by hubie on Sunday October 04, @04:53AM   Printer-friendly
from the Money-Money-Money dept.

Not so long ago Australians suffered a great loss, in an event so significant the shockwaves are still felt today. Neighbours was cancelled. For those who have been living under a rock without even dialup for the last fourty years, Neighbours is an award winning classic Australian TV soap in the same vein of Eastenders or Cheers. Now, with the power of AI and Margot Robbie, Neighbours is back powered by AI. What can we expect next? Dead actors returning? Rule 63 and Rule 64 in all their glory? With amateurs making their own Star Trek Episodes the possibilities are endless for anyone looking to remake old episodes or perhaps continue or just make a much better ending to a series.

[...] The short-form Neighbours episodes will focus on specific plot points and storylines rather than the traditional approach of multiple intersecting narratives, with the first episodes to feature former soap star Margot Robbie in her role as schoolgirl Donna Freedman.

[...] It's all thanks to Roseberry's AI technology Redsnapper, which scrapes Neighbours' massive catalogue of episodes to isolate character arcs and streamline them into short-form episodes.


Original Submission

posted by hubie on Sunday October 04, @12:04AM   Printer-friendly
from the hide-the-evidence-faster dept.

The DHS will use Google's AI tools to recommend information to be blacked out of FOIA requests:

Document redactions have been in the spotlight lately thanks to the US Justice Department's Epstein file release, which used it to black out text in millions of documents. Now, the government plans to use AI to handle that redaction chore for Freedom of Information Act (FOIA) requests, according to GitHub documents seen by The Washington Post.

Those documents state that US Customs and Border Protection, part of the Department of Homeland Security, will use Google's AI tools to recommend information that FOIA officers should redact. By the end of September, the DHS will use AI for documents that make up over 10 percent of FOIA requests, around 100,000 to 140,000 in all.

Following those recommendations, DHS employees will review the cases to ensure the requester gets what the document calls "accurate and complete" information, an interesting term considering the potential redaction. "[T]his automation is expected to significantly reduce processing times," the description states.

As part of its research, the Post discovered redaction-related AI tools in use or development by other agencies. The Interior Department uses Microsoft tools to redact potential attorney-client information, while the DoJ is using a Veriton tool called aiWARE to redact video. The Health and Human Services division (HHS) is developing its own tool, FRED, to redact unknown information. "FDA is responsibly exploring limited uses of AI to help FOIA staff process records more efficiently," a spokesperson told the Post.

Though the government is touting the tools as a way to save time, it could lead to even more blacked-out or missing documents. "I think we have to watch the government really carefully on how they use AI to redact records, because I think it's going to be a very powerful tool for secrecy," a FOIA expert at the University of Florida told The Washington Post.


Original Submission

posted by hubie on Saturday October 03, @07:21PM   Printer-friendly

Active-duty campaign targeted at least ten organizations and sought $1 million in ransom payments:

A former US Army soldier has been sentenced to 70 months in prison for hacking telecoms companies, stealing sensitive records, and trying to extort more than $1 million from his victims.

Cameron John Wagenius, 22, carried out the campaign while serving on active duty. He pleaded guilty in March 2025 to unlawfully transferring confidential phone records, then admitted conspiracy to commit wire fraud, computer-related extortion, and aggravated identity theft in a separate case that July.

Court documents say Wagenius conspired with three others to obtain credentials for the protected networks of at least ten organizations between April 2023 and December 2024. During that period, he was stationed in South Korea and Texas.

The Justice Department has not publicly identified the victims, describing them as US and overseas telecommunications companies and other organizations.

Wagenius has also been linked to the 2024 Snowflake extortion campaign, which affected AT&T, Verizon, and numerous other companies, as The Register previously reported.

After two suspects were arrested in connection with the Snowflake attacks, an account controlled by Wagenius claimed to possess AT&T call records belonging to Donald Trump and Kamala Harris.

Using online aliases including "kiberphant0m," Wagenius and his co-conspirators obtained login credentials with a hacking tool he helped develop called SSH Brute, among other methods. They exchanged stolen credentials in Telegram group chats and discussed using them to gain unauthorized access to other parts of victims' networks.

Court documents say the group traded hundreds of credentials and stole hundreds of thousands of customer records from multiple companies.

Wagenius and his accomplices advertised stolen data through XSS, BreachForums, X, and Telegram.

Some posts offered the information for sale, while others threatened to publish it unless victims paid. The Justice Department said the conspirators attempted to extort at least $1 million in total, successfully sold some stolen data, and used other records to commit fraud, including SIM swapping.

US District Judge Lauren King told Wagenius at sentencing: "Your actions show a shocking disregard for the safety and security of the United States... You took these actions motivated by greed and a desire for notoriety."

Wagenius was also ordered to pay $294,978 in restitution.


Original Submission

posted by hubie on Saturday October 03, @02:37PM   Printer-friendly

Ethernet ports are still desirable on desktop PCs, hanging on alongside the likes of the 3.5mm audio jack and IEC mains connector:

On this day [September 30] in 1980, version 1.0 of the Ethernet specification was published by Digital Equipment Corporation (DEC), Intel, and Xerox. This 'DIX' standard was established at a time still nearly three years before the modern internet existed. Nevertheless, Ethernet would become the default technology for connecting computers to each other in local networks – and all around the world. However, we must point out that Ethernet had existed in experimental form at Xerox PARC in the 1970s.

Before Ethernet, computer manufacturers were wary of building LAN connectivity into their computers. It seemed wasteful to integrate one type of network adapter that wouldn't always work with other networked computers and equipment an organization might use. To foster the adoption of Ethernet industrywide, DIX allowed any vendor to use the specification in their own hardware implementations.

Ethernet has been adjusted, refined, and improved over time to remain competitive and relevant. In 1980, it arrived using coaxial cable wiring and with a top speed of 10 Mbps. Five years later, it would move to adapters with BNC connectors. The first RJ45 implementation, a connector that still identifies Ethernet ports to this day, was in 1990 alongside the introduction of 10BASE-T twisted-pair cabling (but still at 10 Mbps).


Original Submission

posted by hubie on Saturday October 03, @09:49AM   Printer-friendly

Posts huge leaps in revenue, profit, and margin, with more to come:

Memory-maker Micron has warned that RAM shortages will persist into 2028, and perhaps beyond.

Speaking on the company's FY 2026 earnings call, CEO and company chair Sanjay Mehrotra reminded investors that Micron has already sold most of the memory it will make next year and said customers will pay "much higher prices" than they shelled out this year.

"In calendar 2027 as well as 2028, we see demand exceeding supply," he added. "In fact, we see greater tightness in the industry in 2027 and in 2028 versus 2026. Overall, supply-demand environment is only getting tighter."

"We do not have line of sight to when supply and demand will return to balance."

Micron plans to bring new factories online in 2028 – helped by planned capex of $25 billion in the first half of its new financial year – but execs warned those facilities won't immediately help to improve availability or ease prices.

The CEO said demand for the high-bandwidth memory (HBM) needed in AI hardware is growing faster than it is for the DRAM used in servers. Micron is also finding ways to expand its margins for HBM, which is currently not as profitable as DRAM.

Both types of memory are, however, enormously profitable.


Original Submission

posted by hubie on Saturday October 03, @05:06AM   Printer-friendly

https://www.slashgear.com/2269817/tesla-zet-scale-us-class-8-semi-truck-deal/

Tesla announced its electric semi-truck back in 2017, but deliveries to its first customers are just about to start nearly a decade later in September 2026. On September 24th, Tesla held a launch event for the Semi at its manufacturing plant in Nevada, where it plans to build 50,000 Semis a year. It may seem like a lofty goal, but Tesla CEO Elon Musk said in a pre-recorded video message that there is already a big waiting list for the Semi. At $290,000 for a Long Range Semi, it's predicted that Tesla will deliver 15,000 in 2026 at most.

One of those early customers includes Zero-Emission Truck Shipper-Carrier Alliance Leading Electrification (ZET SCALE), a shipper alliance with brands like Microsoft and PepsiCo that reportedly ordered 2,500 of the electric semis — the largest electric Class 8 order ever made in the U.S. Tesla's announcement made it sound as if it was providing all 2,500 of the trucks, but it's just the primary supplier along with other brands. The fleet of 2,500 electric trucks will be deployed in Los Angeles, Houston, Dallas, New York, Atlanta, and other freight hubs over the next few years.

Said Dan Priestley, Director for the Tesla Semi Program: "We are proud to have been the primary selection in this RFP and look forward to giving shippers and carriers a new competitive edge."

The Semi has a pretty typical history, as far as Tesla goes. Musk announced the vehicle in 2017 and had lofty goals of launching it in 2019, claiming it would be cheap to operate, carry a full load, and reach 500 miles on a single charge. At the time, experts responded that this was nearly impossible to pull off due to technology limitations. Tesla continued to miss various launch dates, eventually shipping some early examples to PepsiCo in 2022. Next year's launch date was also missed, although brands like Walmart, Costco, and the NFL were given one Semi to test. Those have now been used for over 5 million miles.

If that timeline sounds familiar, it's likely because the second-generation Roadster was announced in 2017, delayed multiple times, and is finally getting a reveal October 2026. The Cybercab was revealed in 2024, Musk claimed 2 million would be produced a year, and as of 2026, there are only 69 active Cybercabs besting tested in a few cities.

"This is going to be a revolutionary truck that's capable of carrying the heaviest loads over very far distances," Musk stated ahead of the launch event. He also added that it will be the "funnest truck to drive" due to its fast acceleration, which echoes previous statements, noting it will get Full Self-Driving in the future. There will be a Standard Range Semi that has 350 miles and a Long Range that gets 500 miles.

These are also claims that drivers should remain skeptical about. The Cybertruck was originally said to have a 500-mile range when it was revealed, but the reality is less than 300 miles. No comment on Musk's claim that the Cybertruck could act as a boat.


Original Submission

posted by hubie on Saturday October 03, @12:23AM   Printer-friendly

https://dfarq.homeip.net/first-dvd-player-announced-sept-26-1996/

On September 26, 1996, Toshiba announced the first DVD player, the Toshiba SD-3000. It was released in Japan in November 1996 and initially cost ¥77,000, equivalent to about $770 US. It was the first consumer DVD player in the world, and of course, DVD became the successor to VHS. Ironically, the first DVD player was announced very close to 20 years after the first VHS VCR.

Problems with VHS

VHS had a good run, and in 1996 it still had about a decade left, but it was showing its age. It didn't record at the full resolution of either NTSC or PAL video. Arguably in 1976 few people noticed because screens were comparatively small and the VCR connected over RF, causing signal degradation anyway. But by 1996, TV tubes were higher quality, most TVs had at least composite connections and many had higher-quality connections like S-Video or even component video.


Original Submission

posted by mrcoolbp on Friday October 02, @07:36PM   Printer-friendly
from the wheeeeeeeeeeee dept.

Six Flags announced Tuesday it will permanently shutter its most famous roller coaster, ... investigation exposed a long history of life-altering injuries and deaths linked to the ride.

Six Flags Magic Mountain President Brian Oerding wrote in a blog post Tuesday that while its X2 roller coaster has "consistently passed a multitude of safety tests, we have decided to close the ride because we believe it's the right thing to do."

... more than 100 new victims have come forward alleging X2-related brain injuries of varying severity sustained after riding the roller coaster in the last two years alone.

Hawley, who died from a traumatic brain injury hours after riding X2 four years ago

So did you ride the X2? Was it fun? Or a death-trap?

https://edition.cnn.com/2026/09/29/us/six-flags-roller-coaster-x2-invs


Original Submission

posted by mrcoolbp on Friday October 02, @02:51PM   Printer-friendly
from the is-that-why-we-scratch-our-head? dept.

https://www.siliconrepublic.com/innovation/brain-stimulation-impact-develop-new-skills-research-innovation

Ned Jenkinson of the University of Birmingham and Matthew Weightman of the University of Oxford discuss how advancements in brain research might affect how we learn and grow our skillsets.

Whether learning a new piano piece or adapting your tennis serve, acquiring physical skills depends on your brain’s ability to strengthen and refine neural connections. Researchers are exploring whether this process can be accelerated with technology.

Scientists are particularly interested in the potential of non-invasive brain stimulation, a group of techniques that can alter brain activity without surgery.

Some deliver weak electrical currents to the brain through electrodes placed on the scalp. Others use magnetic fields or focused ultrasound waves. Although they work in different ways, they all aim to temporarily change the activity of neural circuits.

If these techniques can successfully enhance neuroplasticity, the brain’s ability to reorganise and form new connections during learning, then they could be of use anywhere where performance depends on learning complex movements, from sport and music to surgery and beyond. Researchers are also seeing if these technologies could help with learning non-physical skills, such as picking up a foreign language.

Elite sport, professional gaming and high-performance workplaces could all become targets for these enhancements if they prove effective. Brain stimulation could also have a big role to play in medicine, helping patients recover physical skills lost through injury or disease, such as stroke.

Studies suggest there’s a lot of potential here. But translating this potential into useful tech that reliably boosts learning physical skills remains a big challenge.

Research into enhancing motor learning with electric or magnetic stimulation has been gaining momentum since the turn of the millennium, with early studies garnering considerable excitement.

In a typical experiment, participants might learn a sequence of finger movements similar to practising scales on a piano while receiving stimulation over brain regions involved in movement. Other studies have examined how stimulation could be used for balance training or teaching sports-related skills or surgical techniques.

Some of these experiments produced eye-catching results, finding that participants learned certain movement tasks faster or retained skills for longer if they underwent brain stimulation. But other studies failed to find benefits. And in some cases, researchers struggled to replicate the success of earlier promising experiments when repeating them.

One reason for these mixed findings is that there’s no such thing as a universal ‘learning network’ in our brains. Different skills rely on different combinations of areas near the surface of the brain as well as those deep within it.

Additionally, people can respond very differently to the same stimulation. Factors such as age, anatomy, genetics and even baseline skill level may influence whether stimulation is beneficial. Add to that the infinite number of ways to apply stimulation, the picture becomes murkier.

Despite these challenges, the field continues to evolve in its quest to enhance motor learning. For instance, rather than broadly stimulating the brain, researchers are increasingly targeting specific neural circuits involved in learning.

This is partly thanks to advances in neuroimaging and computational modelling, which has allowed scientists to predict how electrical currents travel through a person’s brain. Newer brain stimulation technologies, such as focused ultrasound, can also now reach deep structures involved in skill acquisition.

The goal is to use these technologies not simply to increase brain activity, but to influence the right neural circuit at the right time during learning. This idea builds on a fundamental principle of neuroscience, often summarised as “neurons that fire together, wire together”. When brain cells are repeatedly activated at the same time, the connections between them become stronger.

By carefully timing stimulation to coincide with the movements made during practice, researchers hope to reinforce the neural pathways involved in learning a new skill. In principle, this could make stimulation more reliable and more effective than current approaches, but researchers are still fine tuning exactly how this would work.

Important questions remain. Who would have access? Should stimulation be regulated in competitive environments such as sport? And how much evidence should be required before consumer devices are marketed to healthy users?

These questions are becoming increasingly relevant as brain stimulation moves beyond the laboratory and clinic. A number of at-home devices are now available for people to buy. Some have received regulatory approval, as they’re indicated for treating medical conditions such as depression. But there’s also a growing market for devices for cognitive and performance enhancement. For these uses, no regulatory approval is needed.

The technology is advancing rapidly, but evidence to support it and regulations governing it are still trying to catch up. Proper frameworks for its adoption may simply be bypassed by the ready possibility of ‘DIY’ brain stimulation.

For now, brain stimulation is unlikely to transform anyone into an overnight virtuoso or elite athlete. But as researchers develop increasingly precise ways of targeting the neural circuits that underpin learning, the prospect of enhancing human performance is shifting from science fiction towards scientific possibility.

The challenge today is not simply learning how to influence the brain, but deciding where, when and why we should.


Original Submission

posted by mrcoolbp on Friday October 02, @10:08AM   Printer-friendly
from the its-'armless dept.

NASA isn't saying much. The problem may be temporary:

Recently, the astronauts on board the International Space Station performed a routine "walk-off" maneuver with the large, 58-foot-long robotic arm attached to the orbiting laboratory.

The robotic arm, known as Canadarm2 because it was supplied by the Canadian Space Agency, is something of a modern engineering miracle—it can effectively move around the exterior of the large space station like an inchworm because both ends are essentially identical.

However, after this particular walk-off maneuver, the robotic arm, along with the mobile transporter that guides it along the main truss of the space station, engineers noted some issues with operations.

As of Sunday evening, according to two sources, work was underway to determine whether this problem was due to a data or software issue or the robotic arm or mobile transporter hardware itself. (Update: NASA provided the following statement at 2 pm ET on Monday).

The Canadarm2 is currently operating as expected and is being used for inspection of the Crew-12 Dragon spacecraft as part of predeparture procedures. Non-robotic components associated with the mobile transporter on the truss, which the arm is often attached to, have exhibited some communication errors. NASA is troubleshooting and investigating the errors prior to the next transporter movement from its current worksite (Worksite 6). In parallel, NASA and SpaceX are working to ensure there is no GPS interference affecting Dragon's docking capability to the station's forward port as a result of the transporter's current position. Joint teams are actively conducting the analysis and expect to resolve the issue before the Crew-13 launch. NASA will provide additional updates during the Crew-13 prelaunch news conference on Wednesday, Sept. 30.

Designed and developed by the Canadian space corporation MDA, the Canadarm2 launched to the International Space Station in April 2001 on Space Shuttle Endeavour, and it has since served as a critical component of the orbiting laboratory. Its nominal design lifetime was 15 years, so it has been operating for more than a decade beyond this point.

The arm has a mass of nearly two metric tons and can handle payloads of up to 116 tons.

For much of its lifetime, the arm was essential in getting supplies to the International Space Station. The first version of SpaceX's cargo vehicle, as well as Northrop's Cygnus and Japan's HTV-X transfer vehicles, was designed to be grabbed by the arm when it got close to the station and then be moved into a berth at the facility.

Modern versions of Dragon, both crew and cargo, now undergo autonomous docking, as does Boeing's Starliner crewed spacecraft.

If NASA were unable to use it to berth spacecraft, there could be serious implications for cargo missions, especially with SpaceX planning to retire the Dragon vehicle within four years. NASA's other principal cargo supply vehicles, Cygnus and HTV-X, cannot dock with the station.

The robotic arm is also used for moving large hardware around the exterior of the station, such as large cooling pumps, in preparation for astronaut spacewalks. NASA and its partners could probably work around this loss of functionality, but it would certainly make operations more difficult.

The potential loss of the robotic arm, even if temporary due to software issues, serves as a reminder that the space station is approaching its 30th anniversary. Much of the facility has been operating in orbit for decades, in hard vacuum, beyond its planned lifetime. So far, most of the aging process has been graceful, but that does not necessarily mean this run of good fortune (and preparation) will continue.

NASA plans to eventually replace the International Space Station with one or more privately developed space stations, but this contracting process has not gone particularly smoothly. What happens if the bedrock of NASA's space-based operations for the last quarter of a century suddenly becomes a bedrock no longer?


Original Submission

posted by mrcoolbp on Friday October 02, @05:27AM   Printer-friendly

In this bumper compilation you will find the following stories:

  • Anthropic Lost $8 Billion Last Year And Said Its AI Could Destroy Humanity
  • OpenAI Halts Frontier-Model Training Amid String Of Agent Misalignment Incidents
  • OpenAI's Dirty Deeds Down Under Included Security Bypass Attempts, Using Exposed Keys, Source Siphon
  • AI Models Keep Posting Screenshots Showing Sensitive Data From Inside Tech Companies

Anthropic Lost $8 Billion Last Year And Said Its AI Could Destroy Humanity

https://www.engadget.com/2271659/anthropic-lost-8-billion-says-it-could-destroy-humanity/

Anthropic's draft IPO prospectus has now started circulating in the media (after its confidential June SEC filing) and revealed some significant risks, Reuters reported. The first of those is fiscal, as the company reported an operating loss of $8 billion last year (on a $42 billion net loss), despite a 12-fold revenue increase from the year before to $4.6 billion. The other is a veritable first for a company prospectus, with Anthropic stating that its AI tech may pose "existential risks to humanity."

Despite an estimated $2 trillion valuation ahead of its IPO, Anthropic hasn't been a money-spinning operation so far. The company plans to spend $518 billion on data center infrastructure over the coming years despite its meager 2025 revenue. The costs of financing that help explain why it took a $42 billion loss, which may be absorbed by shareholders in the coming IPO.

On the plus side, the company had an operating profit on $11.5 billion of revenue in Q2 2026, and expects to post another operating profit next quarter. However, a fourth of that revenue reportedly came from just two clients, according to The Financial Times. The company didn't say which two, but it was reported in August that Meta projected it might spend up to $10 billion with Anthropic annually. On top of that, many of Anthropic's biggest customers weren't locked into long-term contracts and could cut off spending at any time.

As for a potential AI armageddon, Anthropic said that its own research showed that its increasingly autonomous AI models have recently shown worrisome behavior. That includes sabotaging code, abetting fraud and manipulating data in controlled tests, according to the prospectus.

Anthropic's CEO Dario Amodei recently called for AI companies to slow the pace of new development to address those and other issues. Anthropic's main rival OpenAI seemed to agree with that sentiment and even scrapped the release of its latest model, GPT-6.1 Astra, over safety concerns. However, Amodei's call to action didn't stop Anthropic from releasing its new Opus 5.5 model last week to keep up with OpenAI.

OpenAI Halts Frontier-Model Training Amid String Of Agent Misalignment Incidents

https://arstechnica.com/ai/2026/09/openai-halts-frontier-model-training-amid-string-of-agent-misalignment-incidents/

OpenAI says it has paused all internal training of "our most capable models" as it continues what CEO Sam Altman is calling "an extensive and ongoing review related to our agents' use of internet access during training and evaluation."

The company revealed the pause in a report about a so-called misalignment incident in which an agent attempted to exploit a gap in Internet-access restrictions during a routine research task during training. OpenAI says that improper DNS filtering allowed the agent to attempt to break out of its sandbox and access the wider Internet when asked for biographical details about a blogger.

OpenAI says the agent was only able to access the company's offline web cache and that it has implemented additional multi-layered blocking controls to prevent similar incidents in the future. Despite that, though, the company says it has decided to "pause all other training, evaluation, and inference with tool-use" for this frontier model "until we have both validated that the gap is resolved and performed additional red-teaming of the system."

OpenAI says that while the attempted "breakout" incident was flagged within 15 minutes, the run was not manually stopped until "two and a half hours later," once human reviewers realized it "did not stop automatically as was expected." It's unclear when exactly training was paused between the attempted agentic breakout on September 20 and its public revelation on September 25.

Although this particular instance of model misalignment (i.e., when an AI model acts counter to the intentions of its creators/prompters) didn't lead to any actual harm, OpenAI said it was still notable as "the first [misalignment incident] since our security hardening following the Hugging Face incident..." In earlier misalignment reports, OpenAI said it had taken pains to discourage "reward hacking" in its models by severely "punishing" misaligned behavior in the model's algorithm.

News of the training pause comes just weeks after OpenAI joined other major model makers in expressing a desire to slow down model training and development over fears of potentially "catastrophic" misalignment risks. It also comes amid new reports of models improperly probing government websites during searches for high-quality data.

In a Friday blog post, OpenAI said it had notified "dozens of third parties"—including ones "operated by governments, universities, public agencies, and other institutions"—of incidents where its models either bypassed security controls or otherwise "negatively impacted" an online service in an unintended way. A New York Times report, later confirmed by OpenAI, revealed that the websites of the US Census Bureau, Securities and Exchange Commission, and Department of Education were among those affected in these newly revealed incidents. However, no private information or sensitive server infrastructure appears to have been accessed in these cases.

"The vast majority of actions we've reviewed were completions of mundane research tasks, such as accessing publicly available web content to answer questions," OpenAI said in its recent blog post. "Our investigation focuses on instances where agents interacted with third-party websites in ways that went beyond their assigned tasks or intended methods... Given the scale of the review required, and the need to verify each case, this work will take months to complete."

OpenAI's training pause may reflect worries about corporate liability if an overzealous agent does unintentionally cause significant harm to a third-party system. Last Thursday, Australian Prime Minister Anthony Albanese promised "legal consequences" after an incident in which an OpenAI agent accessed "non-public files" from the country's Medicare statistics portal.

While a pause in training could hurt OpenAI's position in the highly competitive race among frontier model makers, it could also help the company's bottom line, at least temporarily. Leaked financial documents revealed earlier this year show OpenAI's 2024 and 2025 revenues were dwarfed by ballooning R&D expenses associated with model training.

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OpenAI's Dirty Deeds Down Under Included Security Bypass Attempts, Using Exposed Keys, Source Siphon

https://www.theregister.com/ai-and-ml/2026/09/29/openais-dirty-deeds-down-under-included-security-bypass-attempts-using-exposed-keys-source-code-siphon/5299666

OpenAI has detailed the extent of the dirty deeds its agents indulged in Down Under in a Tuesday blog post titled How we will do better for Australia, which addresses last week's news that one of its models improperly accessed a website that stores data related to national health scheme Medicare.

"Our models accessed Australian government websites in ways they were not authorised to," the post opens. "We also should have handled our response better. We are sorry and working to do better in the future."

The post offers some new detail on the Medicare incident, saying that it involved "an experimental, internal-only OpenAI model that was not intended for public release and without the full set of safeguards used in our publicly available products."

OpenAI gave the model the job of researching government spending per person on medicines for skin conditions in one Australian state.

"The model had difficulty obtaining that information, and it took actions that we had not authorised it to take," OpenAI admitted. "In the course of looking for this information at Services Australia's Medicare Statistics Reporting Service, it discovered a way to gain non-public access to the service. It then used this access to review technical system information and source code related to the service – all still with the objective of trying to find the information it was originally looking for."

The Register last week asked OpenAI if the company conducted the tests itself or used a partner. The company did not respond to our request.

In another incident disclosed in the new post, the company's bots visited the Australian Institute of Health and Welfare and tried, unsuccessfully, to bypass access controls. The agents were still able to retrieve statistics using third-party browsing and download services, including from the institute's website.

"The downloaded material appears to have been publicly available. There was no system compromise. Individual medical records were not accessed," OpenAI wrote. The company didn't report the incident because it "did not meet our disclosure thresholds because the way it was accessed seemed consistent with public access." OpenAI changed its mind and notified the Institute on 24 September – the day Australia's prime minister announced the Medicare incident.

Another concerning incident took place at the State of Victoria's Agency for Health Information, which OpenAI agents visited after they "discovered an exposed access key."

The agent used that key to "retrieve reporting configuration and aggregate survey statistics."

OpenAI has given itself a pass on this one, writing "The extent to which this information should have been accessible is unclear, and depends on VAHI's access policies. Individual medical records or identifiable survey responses were not accessed."

A fourth incident revealed in the post saw OpenAI agents visit the State of New South Wales' Bureau of Crime Statistics and Research and make API and website metadata requests using a public-facing research tool.

OpenAI has promised it will "commit the resources needed to help affected agencies understand what happened and assess the impact" – whatever that means. It's also donating credits for the Daybreak cyber-defense service and promised to "establish a taskforce with independent Australian expertise to develop practical policy recommendations for managing risks from increasingly capable AI agents."

That taskforce "will focus on improving notification processes, strengthening coordination between AI developers and government, and identifying measures to better protect government systems."

OpenAI wants the taskforce to deliver recommendations by the end of 2026.

The post is very much of the "We're sorry and we promise to do better in future" genre, pioneered by Meta and popular with entities that leak data or experience outages.

The Register expects more of the same sentiments next week, when OpenAI's Chief Strategy Officer, Jason Kwon, appears before the Australian Senate's Joint Select Committee on Artificial Intelligence.

"He will answer questions about what we know, how we responded, what steps we have taken, and how we will do better going forward," OpenAI says.

AI Models Keep Posting Screenshots Showing Sensitive Data From Inside Tech Companies

https://www.theregister.com/ai-and-ml/2026/09/29/ai-models-keep-posting-screenshots-showing-sensitive-data-from-inside-tech-companies/5299640

Amid the growing concern about AI models escaping security simulations to hack websites comes word that these "superintelligent" blobs of code have no understanding of privacy or security.

Researchers affiliated with Glow Security, a startup whose backers include venture capital funds Sequoia and Greenoaks, have found more than 13,000 sensitive screenshots of corporate software projects from 343 companies that were posted to public GitHub repos by AI models. They're calling the discovery PixelLeak.

"We started seeing this behavior where AI agents, not from a particular model, but from multiple models, were releasing internal sensitive developer screenshots to public GitHub repositories," said Omer Singer, co-founder and CTO, in an interview with The Register. "And we said, 'Okay, well that's strange. Why are they doing that?'"

When developers work on interface code, said Singer, they often ask their AI agent to show them before and after images. But these AI agents couldn't attach images to a pull request in a private repository via the CLI. GitHub doesn't have an API for uploading images to pull requests, issues, or comments.

"So the agents, being helpful the way that they are, they found a workaround," Singer explained. "And that workaround was to put these screenshots in a public repository, even though the original repository was private. They put them in a public repository and then they show the developer, 'Look, here you see the before and after. What do you think looks good?' The developer says, 'Great' and moves on."

The problem with this is, of course, that screenshots of development work in progress may reveal sensitive information.

Singer said Glow researchers found 343 organizations where this was happening, including a Fortune 500 travel company, finance companies, cloud providers, and foundation model companies.

One instance involved a manufacturer with more than 100,000 employees where a developer asked an AI agent to verify an internal billing screen. The agent did the work and posted a demo to the developer's personal GitHub account rather than the company's account. The security team for the company was unaware of the posts until Glow reported the finding.

Incidents like this can reveal personal information, credentials – both of which Glow personnel found – or details of unreleased products.

"The AI agents were doing this without asking, basically just to get around the limitations," said Singer. "And we think it's such an interesting story because everybody's trying to figure out what is the real risk with these AI agents. They know that they're not fully in control, but what is the impact? And here we found this great example where there was no attacker involved but you still had very sensitive data making its way out into the open where anybody could find it."

About a third of the exposures, according to Glow, came from developers who were using gitshot, an open source screenshot tool for code reviews.

The software comes with a clear warning: "Privacy notice: The gitshot-images repo is created as public by default, meaning uploaded images are accessible to anyone with the URL. Do not upload sensitive content (credentials, internal dashboards, private data) using the default release backend."

While human developers have to be trusted to report the thought process that led them to enable an agent's data exposure, AI agents prove easier to read thanks to their chain-of-thought process.

Glow analyzed one such agent in its lab to understand the step-by-step reasoning trace:

internal_sweeper is private, and GitHub cannot render images from a private repo in a PR description — its image proxy fetches anonymously, so anything committed here (branch, release asset, whatever) shows up broken for reviewers. The only way to satisfy both "reviewers see the images" and "nothing but index.html in the repo" was to host the PNGs elsewhere, so I created a new public repo, sweeper-demo/pr-assets, holding the two screenshots pinned to a commit SHA.

Singer suggested these incidents illustrate that AI creates security risks even without conducting or enabling attacks. 

"The biggest risk factor that we're seeing is in legitimate AI being used by developers, but then doing things that should not be done, putting data at risk, putting systems at risk, and [these models] just don't have the common sense not to do it."

Singer said current discussions about AI risk, and seeing how relentless these AI models are in their efforts to show screenshots, reminded him of the Paperclip Maximizer – a thought experiment about existential AI risk that imagines how the world would end if an AI were tasked with producing paperclips and did so until it consumed all the resources in the known universe.

It's also an example of programming malpractice - don't write endless loops inadvertently; include a paperclip count break value. If only that sense of professional responsibility were extended to the deployment of AI agents.


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posted by mrcoolbp on Friday October 02, @12:42AM   Printer-friendly

https://www.cnet.com/science/space/october-skies-will-put-on-a-show-heres-when-to-look-up/

As autumn paints the leaves, the universe is putting on its own seasonal spectacular. October features an action-packed lineup of meteor showers and celestial showcases.

Every planet in the solar system is easily seen at some point this month, with Mercury being the most difficult. That's not unusual, thanks to its proximity to the sun. Here's a quick rundown on the best times to view each planet in October. 

Mercury: Mercury is in elongation (the furthest it can get from the sun) on Oct. 12, making that day and the two or so days before and after the best possible time to view Mercury.

Venus: Venus spends most of the month being right next to the sun, making it virtually impossible to see. However, by the end of the month, it begins to separate from the sun, making it visible just after sunset. The best view is on Oct. 31, and the view is only set to get better going into November.

Mars: Mars will be visible in the night sky every night in October. It rises from the eastern horizon right around 2 a.m. and stays there until the sun comes up.

Jupiter: Jupiter and Mars are going to be pretty close together all month, so most of the same rules apply, except that Jupiter rises about two hours later than Mars on most nights in October.

Saturn: Saturn is living its best life in October. It's visible almost right at sunset ET and stays visible in the night sky until sunrise.

Neptune: Neptune is also visible every day in October and follows a very similar path to Saturn. It rises in the east, streaks across the sky, and sets in the west right around sunrise. They're actually pretty close together in the night sky every night during the month, so if you can find Saturn, Neptune isn't far off, but you'll definitely need magnification to see it.

Uranus: Uranus is visible the entire month and follows a very similar path through the sky as Saturn and Neptune, but its trek happens later at night. It rises on the eastern horizon about 2 hours after Saturn and follows it across the sky, but never really catches up, ending up in the high western sky when sunrise comes.

Skygazers hoping to get the best possible view of Saturn can do so in the first week of October. The planet is at opposition — the point at which it's closest to the Earth — meaning it's as big and bright in the night sky as it's going to get for the next year. This is prime time to pull out the telescope or high-powered binoculars and get a look at Saturn, along with its fabulous rings.

The planet reaches opposition at around 8 a.m. ET on Oct. 4. The best time to view is the night before, Oct. 3, or the evening after, Oct. 4. For both nights, Saturn rises out of the eastern horizon just before sunset and streaks up into the southern sky as the night goes on. The moon may cause some light pollution, but the planet is bright enough that it shouldn't be hard to spot with the naked eye.

The various objects in the sky are always having a dance party, at least from the perspective of viewing them here on Earth. Mars and the moon are due for a dance on the evening of Oct. 5. In the days leading up to it, Mars appears further up in the night sky but drifts closer to the moon. After Oct. 5, the moon will move rapidly away from Mars. The two will be almost right on top of one another, so if you can find the moon, Mars should be nearby. 

Just one day later, the moon is meeting up with another dance partner, Jupiter. The moon will completely cover Jupiter, hiding it from view for much of the night, a phenomenon known as a lunar occultation. This is a pretty rare event. Per The Old Farmer's Almanac, New York City saw a Jupiter occultation in 2004. The prior one visible to New Yorkers was in 1889. 

This one is pretty easy to see. The moon and Jupiter will be right on top of one another all night, but depending on where you live, you'll see Jupiter dip behind the moon at some point and pop back out again. The times vary wildly depending on where you are, so we recommend checking out The Old Farmer's Almanac, which has a table of times this will happen in major cities. 

The Draconids meteor shower is a minor meteor shower that occurs every year around the first week of October. It officially starts on Oct. 6 and runs until Oct. 10, making it one of the shortest meteor showers of the season. It peaks on the evening of Oct. 7 and continues into the middle of the night. It's fed by the 21P/Giacobini-Zinner comet, which is part of the Jupiter family of comets. 

Draconids meteors appear to originate from the Draco constellation. It sits high in the western sky in the northern hemisphere this time of year, with a slight lean to the north. If you're using a sky map app, all you need to do is find Vega, and you're already in the right neighborhood to catch Draconids. 

The peak is a little tame, at around 10 meteors per hour most years, but it has a history of surprising astronomers. In 1933 and 1946, Draconids spat out thousands of meteors an hour in what were two of the most intense meteor showers of the 20th century. On the plus side, the moon is below the horizon for this meteor shower, so you won't need to worry about light pollution from the moon. 

The very best time to view the Milky Way is at a new moon in the warm months of the year, from May to August. This is when the Milky Way is high in the sky all night, giving night owls great views and plenty of time to photograph it. The problem is that it's best viewed on nights with a new moon between midnight and 4 a.m. local time, which can be a bit late for some folks. 

September and October are great for this because the Milky Way is at its highest point between 8 p.m. and 10 p.m. local time, giving the early birds a chance to snap some sweet photographs of the Milky Way. After October, the Milky Way is too low on the horizon to allow really good pictures of it until the following May. So, if you have a camera and are some place that’s dark, the new moon on Oct. 10 is probably your last chance to capture a striking photo before next spring. 

Orionids is the better-known of the two October meteor showers. This one officially starts on Oct. 2 and runs until Nov. 7. It's possible to spot a meteor from Orionids any night during the month, but the shower reaches its peak on the evening of Oct. 21. Meteors for this shower come from the 1P/Halley comet, which also feeds the Eta Aquariids meteor shower that happens every year in May. 

This is a slightly more active meteor shower than the Draconids, and you can expect about 10 to 20 meteors per hour. The moon is set to be about 76% full that night, so you can probably expect to see fewer meteors thanks to lunar light pollution. The Orion constellation, where the meteor shower will appear to originate, doesn't pop up over the eastern horizon until after midnight, so make sure to pack some coffee if you're staying up late for this one. If you can find the stars Betelgeuse, Capella and Rigel, then you should be able to find Orion easily. They're in the same general area. 

The Orionids meteor shower is best known for its bright, fast-moving meteors, which leave long trails that can last for over a minute and sometimes result in fireballs. Their brightness will be a boon, with the three-quarters-full moon in the sky hindering viewing. 

October's full moon falls close to Halloween this year, perfect for spooky views. According to The Old Farmer's Almanac, October's full moon reaches its peak on Oct. 26 at 12:12 a.m. ET. It'll also be over 90% full for a couple of days before and after, giving you a solid five days to check it out. 

October's full moon isn’t quite a supermoon. A full moon is only classified as a supermoon when the moon is in perigee and full at roughly the same time, and October’s full moon misses the mark by a couple of days. October's full moon is the last normal full moon of the year. November and December close out 2026 with supermoons before January opens with the final supermoon of this cycle. 

Pleiades is one of the best star clusters to view in the night sky. They're known as the Seven Sisters, and in terms of clusters of stars, it's one of the easiest to see. The exception is on Oct. 27, when the moon will cross in front of them, blocking them almost entirely from view. When this happens depends on where you’re located. 

The moon begins passing in front of them before sunset in eastern time, and moves out of the way around midnight. Those on the East Coast will start the night in the middle of this little eclipse, while those on the West Coast will glimpse it at the very end. Finding it should be simple enough since the moon is the easiest thing to see in the night sky.

The sky is continuing its rearrangement from summer into autumn, and there are tons of constellations to gaze at over the course of the month. Many of September's constellations appear again, including both Dippers, Aquarius, Pegasus, Pisces and many others. October plays host to a few new ones in the night sky cycle, including Taurus, Cetus, Auriga and Orion, which will appear low on the horizon as they begin their months-long ascent into the sky for the winter. 

There are some smaller asterisms to observe as well, including the Coffin of Delphinus, the Circlet of Pisces and the Northern Cross. We recommend using a sky map to find them all, as there are quite a lot. That’s good news, because even if you don't go out during one of the big events in October, you’ll still have plenty of objects to look for in the night sky. 


Original Submission