Bij de Zeeuwse ggz-instelling Emergis lopen ze er elke dag tegenaan: het tekort aan gespecialiseerde gezondheidszorgpsychologen. De ggz in Nederland hoeft volgens een adviesorgaan niet te groeien, maar heeft voor de zwaardere problematiek wel beter opgeleide psychologen nodig. Toch sloeg zorgminister Mirjam Sterk (CDA) dit advies in de wind.

…while practising its oldest form.

AIs not only make life-and-death targeting decisions on the battlefield, but also involve humans in only some stages of the decision-making process. To illustrate how that happens and prompt greater scrutiny of model-enabled killing, Airwars, a not-for-profit transparency watchdog, has published a detailed report, taking apart the ways machine learning powers modern warfare. The investigation, titled Anatomy of an AI Kill Chain, offers a visual guide through a fictitious kill chain – the steps real militaries go through to identify targets and eliminate them, with an emphasis on decisions delegated to AI. Authors Sophia Goodfriend, Heidy Khlaaf, Namir Shabibi, Joe Dyke, and Nathan Walker cover six stages: the decision support systems used to assist data gathering, surveillance technology, intelligence and identification, target selection, strikes on targets, and post-strike assessments. The report notes, "A recent book about US military AI revealed that in some operations only two of the six stages of the US military kill chain now involved humans in the loop, with a third involving some human oversight. The rest are now fully automated." Given that targeting mistakes have been widely documented, it's worth wondering whether AI is making such errors more common. And since military officials have, in the past, insisted AI's role cannot be known, there's reason to look for ways to better understand when AI is involved in life-and-death decisions. "In journalism and in policy, there's a tendency to focus on maybe one autonomous weapon system, like an Anduril drone or a Palantir anomaly detection system, and to look at the specific companies that are producing a few single machine learning algorithms that are undergirding those systems," said Sophia Goodfriend, research fellow at the University of Cambridge’s Pembroke College and co-author of the report, in a phone interview with The Register. "What we wanted to do … is to really underscore the stack of AI systems that are upending what it means to wage war, specifically what it means to surveil, target, and kill on the battlefield, and also emphasize how various technologies work together or don't work together throughout the kill chain." The project's goal, she said, is to look beyond specific technical systems at the way warfare is mediated by machine learning algorithms and to highlight the specific limitations of those systems. For each stage of the kill chain, the report touches on sources of potential errors: the reliability and accuracy of decision support systems, automated translation errors during surveillance of a target's text messages, target risk scoring systems derived from algorithmic assessment of social media data, computer vision systems that may misidentify objects and people, the shortcomings of recurrent neural networks used during drone strikes when there's GPS and/or electronic jamming, and the potential problems with AI-assisted battle damage assessment systems. The specific kill chain described is fictitious, but Goodfriend said that this was necessary to avoid making overreaching claims about how specific militaries operate. "Obviously it's hard to reconstruct proprietary systems used by militaries in real time today because they are often censored and secret and reporters cannot pry open exactly how a military is operating," she said, adding that she hopes the report prompts people to question claims about having a human in the loop. "What comes across in this piece is just how difficult it is for a human to intervene in a machine learning decision, the pernicious problem of automation bias, especially under the time constraint of war, and just how thoroughly everything is mediated by AI and automation already on the battlefield," she said. "So even if you have somebody who's reviewing a risk profiling system by looking at a social media post, that social media post will be translated by a machine learning algorithm and they won't be able to verify if that translation is correct because they're dealing with this time constraint and it will be really tempting and much easier to defer to an AI-generated output." Goodfriend acknowledges that it's difficult for civilians to make decisions about technologies they use that might amplify physical safety risks from military AI systems. That's why, she said, it's important to draw attention to the way these systems work. "The stack of AI systems integrated into warfare are the subject of a lot of hype and a lot of mystique and it's rare for those making or deploying these systems to really break down how they actually operate it and what it entails for civilians living under warfare," she said. "So, in illustrating how these technologies work, the massive amounts of data, of people's private information, of passive surveillance that is the foundation and lifeblood of automated warfare, I think that that's one way of moving the needle in terms of advocacy." The other key takeaway of the report, she said, is to underscore that machine learning systems make errors. "The point is not that these are teething problems that will be mitigated or ironed out once these technologies are deployed more and refined more, but really to emphasize the inherent flaws of machine learning and how they can't perform the tasks that militaries expect them to perform when they're deployed on the battlefield," she said. ®

Two agentic bug-hunting systems from Microsoft and Google-owned Wiz show that when it comes to finding and remediating software vulnerabilities, at least two models’ minds work better than one - and Wiz tells us it’s adding a third. Wiz on Monday said Project Atlas, its bug-hunting AI agent, bested Anthropic’s Mythos Preview and OpenAI’s GPT-5.5 Cyber with its vulnerability-analysis skills, achieving a 90.9 percent success rate on CyberGym, and uncovering more than 200 zero-day security holes in widely used open-source code. Meanwhile, Microsoft boasted its MDASH bug-hunting harness scored a 95.95 percent success rate on CyberGym, also beating Mythos, Gemini and GPT on the same benchmark for evaluating how well AI systems find real vulnerabilities in the code. For comparison, OpenAI’s GPT-5.5 Cyber scored 85.6 percent on CyberGym, and its GPT-5.6 Sol scored 83.6 percent. Anthropic’s Mythos 5 reproduced the target vulnerability on 83.8 percent of CyberGym challenges. And Google’s Gemini 3.5 Flash Cyber in CodeMender achieved an 83.2 percent success rate. The secret to both Atlas and MDASH’s success, according to the vendors, is that they use the right model for the right security job. Atlas uses Claude Opus 4.6 with GPT-5.5, Nir Ohfeld, head of vulnerability research at Wiz, told The Register. “We're now working to incorporate Gemini, which is well timed given Wiz's recent work with DeepMind on Gemini Flash Cyber,” he added. Microsoft’s MDASH - a combination of red-team agents that find and simulate real, exploitable vulnerabilities and attack paths, and green-team agents that remediate the issues - combines MAI-Cyber-1-Flash, based on Microsoft AI (MAI)’s internally developed MAI-Thinking-1 reasoning model, and GPT-5.4. MAI-Cyber-1-Flash is designed to handle up to 90 percent of all tasks, with MDASH detecting, patching, and validating vulnerabilities before handing the remaining 10 percent of more complex tasks to the larger GPT-5.4. “We were able to take an off-the-shelf model, within our harness, a multi-agent and multi-model implementation, and we achieved the best results you could have,” Hayete Gallot, executive vice president of Microsoft Security, said on Monday. Atlas isn’t commercially available yet - it’s used internally, and stems from Wiz’s efforts to understand how frontier models can be used for advanced code scanning. But it’s proof that “no single model is best at everything, and none stays state of the art for long,” Ohfeld and fellow Wiz kid Yuval Avrahami wrote in a Monday blog. The cloud security biz evaluates every new model using its internal benchmarking tool, Cyber Model Arena, which scores each one on its success at completing various security-investigation tasks: threat modeling, hunting, validation, and proof generation. “The results are rarely uniform: the model that reasons best through a complex exploit chain is often not the one that triages most precisely,” the duo wrote. “Atlas routes each stage to whichever model wins on that task.” In addition to doing a better job of finding and fixing vulnerabilities, a multi-model system also saves customers’ money, according to Microsoft and Wiz. Combining its much smaller, in-house model with GPT-5.4 halves customers’ costs, according to Mustafa Suleyman, CEO of Microsoft AI. “As the models hand off between each other, they are not just able to deliver better performance than all of the other models combined, they do so at 50 percent of the cost,” he said on Monday. And while “each new generation of models expands what is possible,” they are also expensive, Ohfeld told us. “We have also learned that pointing a frontier model at a codebase once is not a sustainable security strategy: deep scans are expensive, their results become stale as code changes by the minute, and a point-in-time analysis cannot provide the continuous coverage organizations need across every repository,” he said. In fact, the real question for code security shouldn’t be which model a scanner uses, Ohfeld added. It’s this: “How does your system take advantage of the best model available today, continuously and economically, and what continues to work when a better one arrives,” he said. “That is the bet behind Atlas: frontier-model depth where expert reasoning is required, an architecture that improves as models evolve, and rigorous validation so every finding arrives with evidence, not just a plausible answer.” ®

Henry Campbell, a New Brunswick high school student, has used 15,000 pieces of Lego to recreate an iconic Bill Reid print as part of a school project. Before he started the project, he had received permission from Reid's estate for the recreation.

Bill Reid Gallery Instagram CBC The National news clip (YouTube, 1:27 min) Background: Who was Bill Reid? From the Wikipedia page, "William Ronald Reid Jr. (12 January 1920 – 13 March 1998) also known as Iljuwas, was a Haida artist whose works include jewelry, sculpture, screen-printing, and paintings. Producing over one thousand original works during his fifty-year career, Reid is regarded as one of the most significant Northwest Coast artists of the late twentieth century." Iconic Bill Reid works: Spirit of Haida Gwaii (be sure to scroll down to the picture of people standing next to it so you can see the scale of the sculpture) The Raven and the First Men

GrapheneOS is defending its duress-password feature after an environmental activist used it to wipe his Pixel phone during a U.S. Customs search and was later indicted for allegedly destroying property under government control. The nonprofit says the operating system is "completely legal," cannot recover the erased data, and should not be weakened with encryption backdoors. Meanwhile, the activist faces up to five years in prison if found guilty. PCMag reports: In a post on Saturday, the Canadian nonprofit behind the operating system, the GrapheneOS Foundation, explained that the software offers a range of features to prevent data extraction. For example, one safeguard is the "auto-reboot timer" that'll reboot a locked device after a set period of time to put the data at rest, leaving all files inside encrypted.

The group's post subtly suggests that GrapheneOS phones can withstand law enforcement searches without requiring users to resort to a duress password. "People should carefully consider how to use it in an actual duress situation where there can be physical or legal consequences for wiping the device," the nonprofit wrote. "GrapheneOS doesn't require it to protect data from being extracted from the device, but it takes recovering it completely off the table even with the PIN/password for each profile on the device."

On X, the nonprofit has also said it can do nothing to help US law enforcement recover data from Tunick's phone. "Data cannot be recovered after the key derivation material is reliably wiped. It's not possible and there's nothing we can do to assist with it," the group wrote. "Similarly, it's not possible to assist with bypassing encryption because the hardware and software has been designed to prevent it."

Read more of this story at Slashdot.

The review embargo has lifted for the new Framework Laptop 13 Pro, and the consensus across the board is that it is a massive leap forward in terms of build quality and battery life. The main issue reviewers complained about is the sky-high price, with the higher-end model jumping dramatically from $2,100 up to $2,900 due to the memory shortage crisis. (Some note that the price "nearly doubled" overnight while they were in the middle of testing.)

In his video review, Marques Brownlee says, "This is their best build yet. They're finally doing what people have been asking for: a premium... modular... laptop." ZDNET agrees that this device "represents a new approach for Framework and a maturation of the brand's catalog," noting that the new construction is "sleek and airtight, with no gaps, spaces, or evidence that was even put together by your hands at all." Tom's Hardware echoes this enthusiasm, declaring that "The Framework Laptop 13 Pro's sturdy design, haptic trackpad, and bigger battery feel like they should have been there all along".

Battery life, which has historically been a weak point for Framework, is now a standout feature. Ars Technica points out that the combination of Intel's efficient Panther Lake chips and a new 74-watt-hour battery is "finally long enough to decisively eliminate 'mediocre-to-poor battery life' as the laptop's biggest downside." They also said it's "Framework's nicest-looking, nicest-feeling, most polished laptop design."

For Linux users, the machine appears to deliver on its promises. Phoronix says the device "is designed with great Linux compatibility in mind," offering a seamless out-of-the-box experience for distributions like Ubuntu and Fedora.

As mentioned above, the overarching complaint is the extreme cost. Ars Technica notes that Framework unfortunately "switched to an exotic new upgradeable LPDDR5X RAM format just in time for those modules to become astronomically expensive." Still, Marques Brownlee summarizes the long-term value proposition perfectly: while it might be painfully expensive on day one, "the longer you keep this laptop, the more it feels worth it" because you can easily repair and upgrade it over time. Compared to Apple's flagship, the Framework is "80% of the quality, way more modular," he says.

Read more of this story at Slashdot.

Een nieuwe dag, een nieuwe manier om het voetbal nog dooier maken dan het al is. Gianni Infantino zit nog in een rush na dat mega succesvolle pracht-WK en wil de wereldbeker nu in de verkoop zetten. De FIFA, een Zwitserse non-profit die op elk toernooi verlies draait behalve het WK, moet een FIFA-bedrijf oprichten met de naam FIFA Forward Enterprise. Private investeerders kunnen daar miljoenenmiljarden inpompen en daarmee kan de FIFA vervolgens het WK organiseren om die miljoenenmiljarden in nog veel meer miljoenenmiljarden te veranderen. U mag 3x raden in welke kringen de eerste investeerders worden gezocht. Juist. Afijn iedereen boos en de UEFA woest en de voetbalbonden nog opvallend stil maar die gaan dadelijk gewoon tekenen bij het kruisje. Straks gewoon weer naar een of ander corrupt kudtland voor een corrupt kudttoernooi en kudthartjes onder een kudtpost van die kudtvoorzitter plaatsen want het gaat uiteindelijk om de sport, of zo.

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As you may recall, my beloved Gakken World Eye shit the bed during a power failure a couple weeks ago, after having been on 24/7 for nine years. Since these are going for almost a thousand dollars on eBay, my first attempt was to buy the cheapest desktop video projector I could find (HY300 Pro, $30) but that projector threw too small an image, and couldn't focus closer than about 2½ feet.

So then I ordered the cheapest fisheye camera lens I could find (49mm wide angle macro, $30) whose smaller lens was just slightly larger than the output lens of the projector. Success! It worked the first time.

(Someone who knew what they were doing might have looked at the focal length and done math to determine what kind of lens was needed. I very much did not do that. But I got lucky!)

I enlarged the various holes to make the new lens fit inside the dome, and got an 82mm to 95mm step-up adapter ring to screw onto the front of the fisheye, to have something I could drill into to hold it in place inside the dome. Then I cut the old electronics housing in half and dremeled out all of the interior greeblies to have a sleeve to slip over the new projector. The rest is duct tape.

I had to open up the new projector to extend the IR receiver into the dome, since, like the old eyeball, it doesn't remember its settings and you have to manually select HDMI on a remote after a power failure.

The image is 16:9 and doesn't fill the whole dome, and it's a little fuzzy on the edges, but for eyeball purposes, it's close enough. Once I get an ethernet cable in there, I'm going to see what tweaks I can make on the image in software to make it fill the frame a little better.

Previously, previously.