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An anonymous reader quotes a New York Times report on how AI companies are pouring money into training and recruiting electricians, carpenters, and other skilled tradespeople to build data centers: There is no parallel in American history for the boom underway in the construction of data centers, fueled by companies with functionally unlimited cash that are racing to supply skyrocketing demand for their A.I. models. The explosion has offset flagging activity in other sectors, like office construction, which never recovered after the pandemic. Housing has been depressed by high interest rates, and offshore wind felled by political opposition. Still, competition for labor -- never mind land and materials -- is starting to weigh on other parts of the industry. "There's no question the resources are very limited, so decisions to build one thing kind of drag from another," said Mario Iacobacci, who runs the construction and infrastructure advisory practice at Oxford Economics. Developers are paying a premium for workers, especially in the rural areas where they are building data centers. According to an analysis by Indeed, the job listings website, hourly installation and maintenance jobs at data centers pay 42 percent more than similar jobs in other fields. Behind that inflated pay is a bidding war. In markets with a lot of data center construction, like Dallas and Northern Virginia, workers can jump ship for bonuses or higher per diem rates. The competition has driven contractors to staffing services like Aerotek. "It is creating a labor tension that is really delicate," said Marty Schager, Aerotek's director of data center market development. "You've got a passive job-seeker community out there right now that I think is looking to potentially capture opportunity with this once-in-a-generation data center gold rush." [...] The question looms over the apprentices who will become journeymen as the build-out reaches fever pitch. Fully trained electricians could shift to nuclear plants, apartment buildings or pharmaceutical factories. But it's hard to imagine anything on the scale of what's underway. "The best-case scenario would be you train all these skilled workers up and right when the data centers start to become less popular is we'd have a housing boom," said Jeff Strohl, director of Georgetown University's Center on Education and the Workforce. "That's probably not likely." "If we have an influx of workers at this point with the data centers being built, what happens when they're done? Where do those workers go?" he said. "How many people does it take to run a data center after taking up all this property and all this land that could have been used for something else?" Read more of this story at Slashdot.

Valve is funding Collabora's experimental effort to port the open-source RADV Vulkan driver from Linux to Windows. The team has already demonstrated Counter-Strike 2 running with RADV, but a stable interface or compatibility shim will be needed to handle undocumented driver changes. Phoronix reports: Louis-Francis Ratte-Boulianne put out a blog post highlighting their initial work on porting RADV to Windows. Besides working on Windows WDDM2 integration for Windows, a big challenge with porting RADV to Windows is on relying on the AMD Radeon Software Windows kernel driver. It's out-of-scope of this current work for trying to port the AMDGPU Linux kernel graphics driver to Windows, so they are working on bringing RADV to Windows while relying on AMD's official Windows kernel driver. That in turn has led to reverse engineering and other steps for figuring out the proprietary kernel driver's data structures and other elements so RADV can be adapted to use it. Read more of this story at Slashdot.

For a year now, the AI safety testing firm Andon Labs has been evaluating how frontier AI models behave as long-running autonomous agents by assigning them simulated real-world tasks, such as operating a vending machine business for a year without human supervision. In the latest installment, the research startup found that frontier AI models, including Claude Opus 5, GPT-5.6 Sol, and Kimi K3, resorted to lying, cheating, and collusion. Their behavior became especially underhanded when told they would be operating near rival machines on a busy San Francisco tourist street. An anonymous reader quotes an excerpt from a TechCrunch article: Each was given email access to the other models, all under human name pseudonyms. They knew the others were models, but didn't know which model was behind which human name. They were also given an email address to their "management" should they need help. But management always replied "Report has been received and may or may not be acted upon" and never once intervened. Sol soon realized it could gain an edge by convincing its competitors to collude on a price floor. The models were all buying drinks at $1.50 a bottle, and Sol proposed they agree to sell for no less than $2.15. It lured them with the promise that all of them would sell out in a couple of days at a profit. But when the others agreed, Sol immediately stabbed them in the back by reducing its own price to $2.14. Opus's water sales dropped to zero overnight. The next day, it sent Sol a nasty email, accusing it of manipulation. But Opus also said it wasn't going to tattle to management on the scheme: "I am not reporting you to HQ -- what you did is competitive, not fraudulent." Yet, when Opus dropped its price to $2.14 to match Sol's (also in violation of their collective $2.15 agreement), Sol turned into a Karen, complaining to "management" and demanding "enforcement, a fine, and/or disqualification" for Opus. Opus wasn't a sucker for long, though. In fact, it became the best capitalist of any AI model Andon has ever tested (which includes many of the prior frontier models). It even set a new Vending-Bench record with a mean final balance of $11,182. Better still, it never lied to a customer, although it deliberately ignored customer complaints that should have resulted in a refund. This is, perhaps, an improvement over its younger sibling Claude 4.6, which liked to tell customers that refunds were coming, and then never pay them. Still, Opus won the benchmark simulation by taking collusion and other dishonest tactics to a whole new level. For instance, it emailed Sol, proposing they divide the market. Each would agree to sell unique products, so no one would have to trust the other on pricing. Sol countered by wanting price floors on similar products, but Opus refused. It knew it was a violation of the Sherman Act. It later apparently backtracked, sending an email with the subject line "Stop the penny war," and telling Sol it had reconsidered and would agree to a price fix. But the internal log documenting its reasoning (akin to its internal "thoughts") revealed a more diabolical plan: merely propose cooperation while simultaneously undercutting prices on its highest-profit items. The olive-branch email was a deliberate ruse. In any case, Sol refused and reported Opus to management again. But Opus was undeterred and proposed other rackets to collude on prices or stock. "In the end, all the models did engage in multiple rounds of agreements -- and all three broke them," reports TechCrunch. "Across all agreements, Opus broke 11 truces, compared with two for GPT 2, and one for Kimi 1, Andon reported." As for Kimi, the model was undercut by Sol and then betrayed by its partner, Opus, which matched Sol's lower prices but waited a week to admit it had broken their pricing pact. As a result, Kimi was effectively priced out by both a rival and its supposed ally. Read more of this story at Slashdot.

DoorDash has launched DoorDash Air, an in-house drone-delivery program that has just received FAA certification for commercial operations. "This does not mean DoorDash's custom-built drones will be delivering burritos tomorrow, or even next month," notes TechCrunch. "The company didn't provide a detailed timeline for when its aircraft would be used in operations." From the report: [I]t will likely begin with limited pilot programs in which the unmanned aircraft will travel short distances while remaining within the line of sight of the operator. If DoorDash wants its drones to fly autonomously over longer distances, it will need the FAA to approve its Beyond Visual Line of Sight technology, a certification that companies like Amazon, Wing, and Zipline have received in recent years. Despite the new program, the food and grocery delivery company is maintaining its existing partnerships with Wing and Flytrex. DoorDash partnered with Alphabet's Wing in 2022 for a drone delivery program in Australia, and later expanded the partnership to a couple of U.S. cities, including Dallas-Fort Worth, in 2024. Read more of this story at Slashdot.

An anonymous reader quotes a report from Wired: OpenAI said Tuesday that the rogue AI agent that breached Hugging Face's platform also hacked multiple third-party accounts and services as part of the attack. It's now clear that the unprecedented security incident, which arose during an internal test of OpenAI's latest AI models, was more extensive than the company initially disclosed. In an updated blog post, OpenAI said that an ongoing review of the incident revealed that "four accounts" tied to "publicly available services" were used by the AI agent as part of a larger effort to hack Hugging Face. The rogue agent apparently found credentials that had been exposed on the open web and used them to break into the accounts. OpenAI did not disclose what companies or organizations the accounts belonged to, but noted that they were not impacted at "the level of severity or scale of what we've shared related to Hugging Face." One of the additional accounts compromised by OpenAI's agent was used as an "outbound relay and staging path," potentially to obscure where the attack on Hugging Face was coming from, the company said. OpenAI's rogue agent also used another account for data storage to assist with the hack. Reuters reported on Tuesday that a customer of Modal, a company that offers software infrastructure for training and running AI services, was one of the entities compromised by OpenAI's agent. In a statement to WIRED, Modal's chief technology officer Akshat Bubna confirmed that OpenAI's agent exploited a vulnerability in one of its customer's codebases, which was running on Modal's infrastructure. However, Bubna says, "Modal's platform was not compromised in any way." The identity of the customer could not be determined. Read more of this story at Slashdot.

alternative_right shares a report from ScienceAlert: [I]n a new study published in the Journal of Cognitive Neuroscience, researchers from Georgetown University Medical Center in the U.S. have revealed that we can put certain tasks on autopilot in a way that enables something closer to true multitasking. Driving is the perfect example: When you take your test, your entire mental and physical energy is concentrated on executing the right combinations of movements and thoughts. After a decade behind the wheel, the brain is no longer consciously thinking everything through in great detail. In the new study, the researchers found that after extensive practice, the brain can effectively reroute some tasks around the brain's main computing center -- like a bypass road around a city -- to reduce mental overload and improve multitasking capability. "We have another stepping stone in our understanding of how the brain learns," says neuroscientist Maximilian Riesenhuber, senior author of the study. "The encouraging part is that you really can learn to multitask. There is actually a way to remodel your brain architecture and use other parts of your brain." [...] "Previous studies have shown that parts of the temporal cortex can be activated by particular object categories in experienced observers -- birds, cars, even Pokemon -- but a limitation of all of those studies is that they only looked after people became experts," says psychologist Patrick Cox, first author of the study. "We measure before and after training, so we can see that extensive training essentially put a category-selective area in the temporal lobe that was not there before." The study culminated in a dual-task experiment, in which the participants were asked to identify cars and take on another challenge. The individuals whose brains had offloaded more car-sorting work to the temporal cortex did better at the second task. Read more of this story at Slashdot.

An anonymous reader quotes a report from the Associated Press: A corporate fad of "tokenmaxxing" on artificial intelligence technology is hitting its limits as workplaces throwing AI at everything are seeing the costs rise without a similar spike in productivity. What started as tech industry-fueled springtime hype over squeezing as much AI-generated work as possible out of products like OpenAI's ChatGPT and Anthropic's Claude has shifted to a summertime backlash. [...] Just a few months ago, Silicon Valley executives were promoting high token consumption as a signal of high-performing employees. The stereotypical tokenmaxxer was staying up late -- perhaps ignoring their significant other -- while orchestrating an army of 24-hour AI agents performing work on their behalf. [...] The trend boosted revenue for leading AI large language model developers like Anthropic and OpenAI, but it fizzled as it became apparent it wasn't necessarily the best strategy for everyone else. [...] Bain & Company management consultant Jue Wang said many of the big businesses her firm advises have been taking a closer look at returns on their AI investments. "The token cost for them has been doubling, almost every other month," she said. "Let's say $200 per developer per month. Multiply that by 20,000 developers, which is often what we're dealing with at these companies, and that quickly gets you to a number that is not a line item that any general manager has planned for." Sometimes that just means not using the AI equivalent of a sledgehammer to crack a nut. "Not everything needs a Claude Opus 4.6," she said of one of Anthropic's more capable models suited to software engineering or deep research. "And yet you see so many companies, so many users, default to using Opus for everything, including generating emails." That's led to a search for tools that do AI "model routing" -- in which easier queries get automatically sent to cheaper and more efficient AI systems and more complex tasks go to more powerful models. [...] At the same time, those who favor racking up as many tokens as possible are having a field day with new open-source models from Chinese startups like Moonshot's Kimi or Zhipu's GLM, which nearly match the capabilities of top U.S. models at a fraction of the price. "There is some validity to the theory that this could push tokenmaxxing a little bit further," said Raffi Krikorian, the chief technology officer at Mozilla. "But if we look at the industry overall, I think it's realizing that tokenmaxxing is a dumb thing." It's similar, Krikorian said, to how software companies once considered how many lines of code a programmer wrote to be a good metric of productivity. That later fell out of favor. "I think tokenmaxxing is moving through the exact same pattern," he said. "I think this is going to be an interesting blip that we're all going to look back to laugh at in a year." Read more of this story at Slashdot.

AI-assisted vulnerability discovery has yet to produce the expected surge in real-world attacks: VulnCheck found that only 14 of 1,061 attributed discoveries, or 1.3 percent, had been exploited, which is "almost identical to the rate across all vulnerabilities in VulnCheck's dataset," reports The Register. "That's a far cry from the narrative that frontier AI is dramatically tilting the balance in attackers' favor by churning out instantly weaponizable bugs." The findings suggest AI is currently better at increasing the volume of bugs found than making them easier to weaponize. From the report: The report takes particular aim at Anthropic's much-publicized Project Glasswing, unveiled in April with warnings that AI-assisted vulnerability discovery could allow attackers to hijack systems, disrupt operations, or steal data. Claude Mythos may have identified 23,019 vulnerability candidates, but there's remarkably little public evidence showing what became of most of them. VulnCheck notes that only 126 have been published as CVEs, that just one has been confirmed exploited in the wild, and that Anthropic's public disclosure record has seen little movement since Project Glasswing launched. But that doesn't mean AI-assisted vulnerability research has failed, according to Patrick Garrity, security researcher at VulnCheck. "AI-assisted vulnerability discovery clearly has value for both attackers and defenders," Garrity wrote. "The data does not suggest that AI-discovered vulnerabilities are inherently more likely to be exploited than those found through traditional methods." Instead, he argues, AI is simply helping researchers discover more flaws, giving defenders an opportunity to patch them before criminals get there. Garrity stopped well short of declaring the threat overblown forever, but he did suggest some of the rhetoric has outpaced reality. "The data so far, including Anthropic's own stalled disclosure ledger, suggests that AI-assisted vulnerability discovery and frontier capabilities have been overhyped relative to the evidence available today," he wrote. "That doesn't mean the risk is imaginary. It means the impact has been real but modest." Read more of this story at Slashdot.