It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology. By taking advantage of this flaw, which concerns how LLMs identify who or what is giving them instructions, the researchers were able to make popular LLMs spit out information they had been trained not to provide, such as how to synthesize cocaine and how to sabotage a commercial aircraft’s navigation system. “There’s a real probability that this is going to be a problem that’s fundamentally unsolvable,” says Charles Ye, an independent researcher and coauthor of the ICML paper. […]

The ICML paper describes attacks against several of OpenAI’s models, but Cui and Ye say that they have since seen similar results with models made by Anthropic, Alibaba, and DeepSeek. Cui and her colleagues wanted to find out why an attack like chain-of-thought forgery was so effective. They suspected it had something to do with the mechanism that LLMs use to keep track of where their instructions are coming from. But what Cui and her colleagues discovered is that LLMs are in fact very bad at keeping track of different roles.

In a series of experiments that looked at what was going on inside a handful of different models, the researchers found that LLMs seem to identify the role of a specific chunk of text not by the tags around it but by the style of that text and the words it contains. The upshot, the researchers claim, is that all an attacker needs to do to hack an LLM is write text that spoofs a certain role. And because roles are a fundamental part of how LLMs work, no amount of training will fully solve the problem.

The nearly $500 billion jump is larger than the market capitalization of roughly 96% of S&P 500 stocks, data compiled by Bloomberg show. It’s also bigger than the combined value of the benchmark’s 44 smallest members, which includes companies like Domino’s Pizza Inc., Clorox Co. and Hasbro Inc.

Microsoft’s one-day add in value also dwarfs many of the world’s other equity markets. South Africa, Turkey, Finland and Vietnam all have total stock market values that are less than what the software maker is set to add on Thursday.

“Across the government, regulatory and contracting carrots and sticks have been trained on other disfavored speakers,” ABC’s lawyers added in the 119-page filing. “The tools vary; the objective does not: a media industry too fearful of official reprisal to report the news freely.”

Carr has repeatedly rejected accusations of censorship while defending his efforts to rein in what he calls abusive, politically motivated behavior by licensed TV broadcasters. “I don’t view the FCC as the speech police,” he told POLITICO this week for an upcoming episode of the podcast “The Conversation.”

In its filing, ABC pointed to an outpouring of support it has received in more than 152,000 comments in the agency’s license review, as well as recent warnings from conservative Supreme Court Justice Neil Gorsuch, Sen. Ted Cruz (R-Texas) and various pro-free-market organizations about the dangers of a politically motivated FCC. Those include letters from a bipartisan group of former FCC leaders, community and advocacy groups and lawmakers of both parties. “The Commission’s attempted censorship of ABC has attracted condemnation across the political aisle,” the network wrote.

Amazon’s Zoox unit has won U.S. approval for limited commercial deployment of its novel steering-wheel-free robotaxis, a first for the autonomous ride industry, the U.S. auto safety agency said on Thursday. Zoox said that the National Highway Traffic Safety Administration’s decision gives the company federal approval to begin charging for rides, and that it will soon begin charging for service, first in Las Vegas, with additional markets to follow as it completes various state requirements.

[…] NHTSA Administrator Jonathan Morrison told Reuters that Zoox had received clearance to commercially deploy up to 2,500 vehicles in each of the next two years. Zoox currently carries passengers in parts of Las Vegas and San Francisco as part of testing. The exemption would allow Zoox to charge them fees, subject to state and local approvals. The agency said it determined the vehicle is as safe as an equivalent vehicle meeting federal motor vehicle safety standards that are being waived. Zoox cannot sell any of the vehicles to the public.

“We can say pretty clearly that the systems in place on the Zoox exceed the equivalent performance requirements of a compliant vehicle,” Morrison said in an interview. “But we still want to make sure that the automated driving system will operate appropriately.” As part of the exemption, NHTSA is placing additional reporting requirements on Zoox for issues such as crashes or stopping inappropriately on roads, and the regulatory agency will adjust the conditions based on how the vehicles behave. “We have the ability to pull the exemption if we see major safety issues,” Morrison said.

All remote operators must be located in the United States, NHTSA said, and Zoox must publish maps of areas indicating where the vehicles are operating. Morrison said NHTSA expects to develop the first federal safety standards for automated driving systems by the end of the Trump administration. It is also proposing to overhaul some existing rules written with human drivers in mind such as requiring brake pedals and rear-view mirrors.

The leak, in February 2022, exposed the details of 18,700 Afghans who said they were in danger from the Taliban because of their links to UK forces and now wanted to escape to Britain. The blunder triggered an unprecedented superinjunction used against the national media, including The Independent, and prompted a secret evacuation program — the cost of which is still unclear but which likely ran into the billions of pounds. Following the revelation by this outlet and others in July last year of the hidden operation, MPs set up an inquiry to scrutinize what had happened. In their report, the defense selection committee concluded that:

- The data breach could have been prevented if Ministry of Defense (MoD) personnel had received basic Excel training
- By August 2023, when the department discovered the leak, thousands of people already knew that a significant data incident had taken place
- The government did not strike “the right balance between operational secrecy and democratic accountability” — and the superinjunction was in place for too long
- Secrecy denied affected Afghans the chance to take steps to protect themselves and their families and caused delays to evacuation program
- Thousands of Afghans eligible to come to Britain are still trapped in Afghanistan with the government failing to explain how they will help get families to safety.

MPs have called on the government to publish periodic reassessments of the risks facing Afghan applicants to UK resettlement schemes, with officials to report findings annually. They also want ministers to publish a clear policy explaining how they will help Afghans who are eligible to come to Britain but who have not yet been evacuated. The defense committee have also called on the MoD to explain who was responsible for data protection risk before the Afghan breach, criticizing the lack of accountability within the civil service.

Gemini Robotics 2 combines several different AI models into a single system. Taken together, they allow a robot to make sense of its surroundings and how to act in it. A vision language model (VLM), which understands images and video, can communicate with humans and reason how to perform different tasks. Two vision language action (VLA) models, trained to understand how to move in physical space, control the robot’s full-body movement as well as the movements of grippers or hands.

In video demonstrations shared ahead of the release, the company showed several different robots performing complex tasks autonomously using the amalgamated model. In one demo, Apptronik’s Apollo 2 robot used hands from a company called Sharpa to tidy shelves. Google DeepMind trained the model to perform these tasks using a mix of human teleoperation, video examples, and simulations — it’s not yet possible for AI models to perform a wide range of complex tasks without specific training.

[…] Parada says Google takes a multi-layered approach to safety, with guardrails applied on each model layer. It’s also introducing ASIMOV-Agentic, a new benchmark for measuring the safety of various AI systems collaborating to control a robot. The benchmark detects whether a command will result in harmful or uncertain outcome.

Google is expanding its answer to Apple’s age-assurance tools with Wednesday’s news that it will bring its Play Signal API to users worldwide by the end of 2026. The technology, already available in Brazil, allows Android developers to identify younger users of their apps in order to provide safer, age-appropriate experiences. The expansion will initially bring the API to Australia and Canada by mid-August, before rolling out globally to all markets by the end of the year.

[…] Like Apple, Google’s technology allows developers to obtain a user’s age range without needing to access personal information, like their date of birth. Instead, it enables parents to share their child’s age range directly with apps. It also lets adults share their age when prompted by app developers as well, allowing for customized experiences. Parents won’t have to manage sharing this information on an app-by-app basis, either. To make it easier, Google centralizes these controls inside its parental controls dashboard, Family Link. Once entered, any developer that chooses to incorporate age-range information can access this signal to customize their apps accordingly.

Google notes, however, that the age ranges are not shared by default — parents must opt in by entering that information. The feature joins other safety tools on Google Play, including those that let developers restrict a child’s ability to discover their apps. Parents, meanwhile, can continue to use Google Play’s Family Link app to manage their child’s screen-time limits, approve app downloads, or set PIN-based content filters for specific apps.

The important point, then, is not whether a developer has used an AI tool at some stage in their work; contributors can use LLMs to discuss ideas, understand existing code, learn about a field they are unfamiliar with, or carry out general research. The limitation lies in the inclusion of copyright-significant material generated by such tools in the code submitted to GCC.

The policy also provides for a few limited exceptions; GCC maintainers are allowed to accept changes that are legally insignificant or trivial and are generated by an LLM, on the condition that they meet the project’s normal contribution requirements and the use of an LLM is clearly disclosed.

Furthermore, copyright-significant AI-generated test cases could still be accepted. Since test cases usually involve small programs which are intended to reproduce compiler bugs or to verify certain behavior, the policy deals with them separately from code that is incorporated into GCC itself.

It does not follow that merely looking at or altering the generated code makes it acceptable. By the rules that have been adopted, a contributor cannot take substantial implementation produced by an LLM, clean it up manually, and then treat the resulting patch as if it had been originally written by them. Once a contribution has been derived from generated content, it is still subject to the policy.

A Comcast store in Plainville, Connecticut, “had a policy that the highest-ranked Retail Sales Consultant for the prior month was instructed by his or her supervisor — Ms. Peterson, the Comcast Store manager — to tie the lowest-ranked sales consultant for the prior month to a chair in the back office and thereafter assault that person by violently smashing a cream pie in their face,” the complaint alleged (PDF).

Plaintiff David Figueroa’s lawsuit said he was hired as a retail sales consultant on February 2, 2026, and was supervised by store manager Sully Fuentes Peterson. Figueroa alleges that Peterson “designed and implemented” the pie-in-face ritual to meet goals related to sales and positive responses in customer surveys.

“Defendant did not inform the Plaintiff prior to his acceptance of Defendant’s offer of employment that the Comcast Store has a policy of subjecting Retail Sales Consultants to public assaults by co-workers — at the direction of Ms. Peterson, the store manager — for the purpose of increasing Defendant’s sales and profitability,” the lawsuit said.

Figueroa resigned on February 27, and he alleges it was a constructive discharge. The lawsuit says the defendant, Comcast, was negligent because it “reasonably should have known” about the store management’s policies and that the policies could harm employees. Comcast “failed to properly supervise the Comcast Store’s management team,” allowing store management to humiliate employees “for the purpose of promoting the Defendant’s revenues and profits,” the lawsuit alleged.

Tuesday’s flight is thought to be the longest ever by a commercial plane, beating the previous record of 22 hours and 42 minutes set by a Boeing 777-200LR in 2005 between Hong Kong and London via the Pacific in 2005.

Flight-tracking provider Flightradar24 said the trip was the second-most-tracked flight ever on its channels — behind a 2022 flight carrying Queen Elizabeth II’s coffin — with more than 3.6 million people following its progress northwards via Canada. Qantas has ordered 12 modified A350-1000ULR aircraft, designed to connect Australia’s east coast with London and New York in about 20 hours.

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.

Such an import ban would apply to some of the most affordable robots primarily produced by Chinese companies, including Unitree’s humanoid robots that are used by robotics labs and researchers for tasks such as experimental robot surgeries. US consumers would also likely lose access to the newest robot vacuum cleaners that are mainly manufactured by Chinese companies such as Roborock. But the ban also broadly applies to foreign-made robots produced by countries nominally allied to the United States, including Japan, South Korea, and Germany. […]

The ban on foreign-made robots could theoretically encourage more US and foreign companies to set up manufacturing facilities in the United States. There are already multiple companies racing to scale up production of humanoid robots in US factories, including Agility Robotics, 1X Technologies, and Figure AI. Tesla has been attempting to shift production away from older electric vehicle models and toward its Optimus humanoid robot. Boston Dynamics has already been making its Atlas humanoid robot, along with its four-legged Spot robot and wheeled Stretch robot, at its main facility in Waltham, Massachusetts. The US robotics company is also planning to massively scale up manufacturing of the Atlas robot under South Korea’s Hyundai Motor Company, which gained full ownership of Boston Dynamics in July 2026.

“This is one of the strongest technology-security actions in modern US history,” wrote Evan Beard, CEO of Standard Bots, in a LinkedIn post. “The message is unambiguous: robotics is a technology America must lead and own — and foreign-subsidized robots will not be allowed to unfairly dominate US robotics as they did solar.” Similar praise came from Rush Doshi, director of the Initiative on China Strategy at the Council on Foreign Relations, who, in a social media post, described the FCC decision as “one of the most significant actions taken so far in support of the US robotics ecosystem.”

However, several robotics researchers and analysts interviewed by The Robot Report expressed skepticism about any potential boost to US competitiveness in robotics. Some even warned that the ban could prove counterproductive for US robotics efforts to develop humanoid robots. “In the near term, the measure could slow US physical AI innovation by cutting startups and researchers off from future low-cost Chinese platforms before comparable Western alternatives exist,” said Georg Stieler, a global robotics advisor and managing director for Asia at Stieler Technology & Market Advisory, in an interview with The Robot Report.

US domestic production of robots lags behind China in terms of mass manufacturing at lower cost, said Rueben Scriven, a senior analyst at Interact Analysis. “This announcement is more likely to inhibit the US humanoid robotics industry, as the presence of low-cost Chinese humanoid robots has been helping educate the US market through promotional and entertainment use cases — an effect this policy risks undermining,” Scriven told The Robot Report.

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.

AlphaFold is an AI program that can accurately predict three-dimensional structures of proteins from their amino acid sequences in minutes instead of years. It’s now being used to accelerate drug discovery, develop vaccines and understand the structural changes in proteins associated with neurodegenerative diseases like Alzheimer’s and Parkinson’s.

DeepMind started developing AlphaFold in 2018. In 2020, it was recognized as a solution to humanity’s 50-year-old “protein folding problem,” which sought to answer how amino acids automatically fold into complex 3D shapes. Those shapes determine the biological role of a protein. Scientists had identified the structures of roughly 170,000 proteins over the past 50 years, using tools and techniques like X-ray and nuclear magnetic resonance. The AlphaFold team took information from those previous work and then fed it to their AI to train AlphaFold.

In 2021, Nature published the papers with AlphaFold’s methodology and the structure predictions of the entire human proteome, or the complete set of proteins expressed by our species. DeepMind then launched the AlphaFold Protein Structure Database, giving researchers free access to over 200 million protein structure predictions. In 2024, DeepMind CEO Demis Hassabis and John Jumper, who was a staff research scientist when the project began and who eventually became a VP and engineering fellow, won the Nobel Prize in Chemistry for their work on AlphaFold.

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.