Futurism reports: in a new essay for The Chronicle Higher Education, university-level literature and writing instructor Tyler Jagt recalls how not a single one of his students could get through an assigned 20-page article, something that he had read "without complaint" as an undergraduate a decade ago.

One student confessed that the reason they didn't finish was that they kept losing track of what the paper was about. And there's no doubt that they're not alone. Jagt cites the 2024 National Assessment of Educational Progress reading assessment results released last year. It showed that 12th grade reading scores were at the lowest level since the assessment began in 1992. Nearly a third of those 12th graders scored below the assessment's "basic" level in reading, meaning they likely "cannot draw general conclusions based on concepts presented explicitly in a text." Younger children aren't better off: a recent report from the Annie E. Casey Foundation found that 70 percent of fourth graders, or around two million kids, can't read at a proficient level.

"What I am seeing in my classroom is no longer a hunch," Jagt writes. "There is a measurable, generational collapse in sustained reading and writing, and the academy is responding to it with improvisation and exhaustion rather than the structural overhaul it requires...." Jagt cites an MIT study that found users who used ChatGPT during cognitive tasks like writing essays showed lower brain activity in areas associated with creativity compared to students who only used a traditional Google Search or didn't lookup information at all. An astonishing 83 percent of the AI users couldn't quote a single line from the essays they had just written, and capstoning the alarm, the brain activity in the AI users didn't return to normal when they were later asked to write without AI...

On our pernicious pocket devices, Jagt touted a 2017 study that found that simply having a smartphone physically nearby — even if it's face down or turned off — reduced available cognitive capacity and impaired cognitive functioning. "So when a student tells me they 'kept losing track' of a 20-page article, I have to acknowledge that they may be describing a measurable neurological condition," Jagt wrote. "The neural pathways that support sustained attention are built by use, and they atrophy without it. Your body is a use-it-or-lose-it system, and the brain is no exception."
Sunday an "Ask Reddit" question went viral — drawing over 11,000 upvotes — for its question to any teachers reading Reddit. "Is the 'Gen Alpha can't read (write, or do math ext)' crisis real? If so how bad is it?" Some responses...

• "The run of the mill non-honors kids have gotten really bad," posted one high school teacher. "Very low tolerance for working hard, very short attention span, very short stamina for active listening... It's the group that is the most worrying because a decade ago, I'd estimate that maybe 10-20% of kids at a school are like this, and now it's probably 40-50% of each graduating class... Then there's of course the bottom 10-20% kids (excluding the special ed/severe/moderate learning disability kids). This is what the viral videos are about and it's not an exaggeration. They can't read, write, or do very basic math like multiplication or division as a 17 year old."

• "This is the first year the MAJORITY of my class cheated on their first essays...." posted one high school English teacher. "It was also the first year a kid yelled 'We don't care about your fucking books, Miss!' while I was in front of the class presenting books they might be interested in for their book reviews... Almost all of them cheated on the book review they had to write."

Thanks to long-time Slashdot reader schwit1 for sharing the article.

Xbox CEO Asha Sharma and Chief Content Officer Matt Booty told staff that Xbox's current economics "cannot continue," citing more than $20 billion in spending over five years, declining revenue outside Activision Blizzard King, console supply constraints tied to RAMaggedon, and an overextended studio portfolio. The memo stops short of announcing layoffs, but a Bloomberg report says substantial Xbox cuts are expected after Microsoft's fiscal year ends on June 30. Engadget reports: The takeaways are pretty grim. For starters, the simple math of Xbox's revenue isn't adding up to success. "Excluding Activision Blizzard King, over the past five years, we have spent over $20 billion on ongoing investments in our content, platform, and hardware subsidy, but our annual revenue has declined nearly half a billion during that time," the execs state. "Going forward, this cannot continue." They also acknowledge the impact of RAMaggedon: "We are currently unable to make as many consoles as players want to buy, and we need a new business model and partnerships for hardware as we remain committed to Helix." (Helix, in this case, is Project Helix, the codename for Xbox's new console.)

Then there's the kicker, a renewed admission that Xbox still can't support the many studios it acquired in the late 2010s in an effort to grow its first-party game ambitions. "We have found ourselves over extended as we executed on changing strategies in a landscape of more readily available content," the pair said, noting elsewhere that with so many good games, not to mention the plethora of other forms of entertainment available, "Going forward, our competition is attention."

The University of California at Berkeley discovered the percentage of failing grades in multiple CS classes this spring "is significantly higher than past semesters," reports the campus's student newspaper.

"Instructors point to students' increased reliance on AI, lack of mathematical preparedness and understaffing as potential contributing factors." According to [coursework platform] Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F's in spring 2026. In spring 2025 and spring 2024, the percentage of F's did not exceed 10% for either class. The electrical engineering and computer sciences department's grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D's and F's...

[UC Berkeley teaching professor Dan Garcia, who taught both classes] believes the "primary driver" of these abnormally high failing rates is due to a "vast increase in academic dishonesty" due to students' usage of large language models, such as Claude, ChatGPT and Google Gemini. "Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the Center for Student Conduct," Garcia said. "But in other cases, it's students who are leaning a little too hard on LLMs to do their work for them, and then at exam time just really aren't ready." According to Garcia, nearly 30 students in CS 10 were "caught cheating on take-home exams" in spring 2026...

In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade. Ranade noticed a similar lack of prerequisite mathematical skills in her spring 2026 EECS 127 class, "Optimization Models in Engineering," which she described as "differently challenging" to teach this semester. The class saw a 16.8% F rate, far higher than the 5% of D's and F's that the EECS department describes as "typical" for an upper division course...

Both Garcia and Ranade have joined more than 1,300 UC faculty in signing a petition calling for the reinstatement of ACT and SAT standardized testing scores for STEM admissions in the UC system.
Thanks to long-time Slashdot reader theodp for sharing the article.

A new Leiden Declaration, endorsed by the International Mathematical Union and published on June 2, 2026, warns that AI could undermine mathematics by flooding the field with plausible but flawed proofs, weakening attribution, shifting incentives, and giving tech companies too much influence over research priorities. "Mathematicians should find it quite striking that tech companies are suddenly interested in their work," said Kevin Buzzard, a mathematician at Imperial College London, in a statement. "The Leiden Declaration is a well-thought-through response to what is currently happening, as AI continues to disrupt this space." Ars Technica reports: The Leiden Declaration, which has already drawn hundreds of signatories, warns that recent AI developments are threatening "characteristic values" of mathematical research, "often in ways that disproportionately affect students and early-career mathematicians, and hence the long term future of the discipline."

First, it points out how AI models can "produce plausible but unreliable (or even incorrect) arguments which are difficult to distinguish from correct mathematical proofs." Such developments put reviewers under increasing pressure and are "jeopardizing our ability to implement traditional standards for the correctness, transparency, and independent verifiability of proof," the declaration warns. "Inaccurate AI-generated drafts are cheap to produce, and there is a risk of cluttering the literature with claimed results that are simply wrong," said Leslie Ann Goldberg, head of computer science at the University of Oxford, in a statement. "Once that happens, the errors are likely to propagate as new results are built on faulty foundations."

Second, the declaration highlights how "models trained on published works frequently return outputs that do not properly cite the human works they synthesize," while also pointing out that many current AI models were trained on data obtained through "exploiting licenses and access arrangements" or "simply violating copyright protections."

Third, the declaration describes how the use of AI "may become incentivized for its own sake, disrupting our mechanisms for hiring, funding and recognition" while leaving out researchers who lack access or are "unwilling to use technologies controlled by organizations whose values they do not share."

Fourth, the declaration warns against mathematics research "communicated through informal channels such as press releases or blog posts, often without any research paper or other disclosure of information necessary for scientific evaluation." Such communication strategies can lead to "oversimplification" in media reporting that overemphasizes AI tools' significance at the expense of prior human contributions, and "misleadingly uses specific mathematical tasks as metrics for the general reasoning capacities of commercial products."

Fifth, the declaration describes "increasing involvement of technology companies in mathematical research" as threatening the "autonomy of mathematics," especially as university budgets are under pressure and researchers may feel greater professional incentive to collaborate with technology companies on "asymmetric terms." This also raises the risk that mathematics research questions amenable to AI-driven techniques may be prioritized. What can mathematicians do about this? The Leiden Declaration urges them to treat AI as a tool, not a substitute for human responsibility. Individual mathematicians should disclose AI use, remain accountable for the correctness of their work, continue crediting human authors, and use AI tools only when they align with the declaration's values.

It also warns that mathematics can be applied to "warfare, oppression, mass surveillance, and the undermining of democracy," so mathematicians should weigh the ethics of tech-industry partnerships carefully. Professional organizations are encouraged to develop AI-use guidelines for publication and review, protect researchers from having their work used as training data without consent, support peer-reviewed publishing, and "actively prepare to become involved if major mathematical results are claimed using unconventional means."

For policymakers, the recommendations are blunt: "protect the rights of authors," "regulate the artificial intelligence industry," and "invest in public computational infrastructure." The declaration also urges people to "don't believe the hype," warning that tech companies have "a strong commercial incentive... to overstate the capabilities of their products."

ETH Zurich researchers say they have generated certified "perfect randomness" for the first time by using a quantum Bell-test setup with two entangled superconducting chips connected by a 30-meter cooled link. "In the long term, this work could play a similar role in digital security as atomic clocks do for timekeeping: a physically certified source of randomness that other systems can rely on," reports Phys.org. "Possible applications range from the encryption of sensitive communications and digital identities to public randomness services for lotteries and blockchain applications." From the report: They call their method randomness amplification. "This was made possible by an improved so-called Bell-Test with simultaneously high quality and high data rate," says [Renato Renner and Andreas Wallraff]. He and his coworkers use a complex setup that consists of two superconducting chips, which they cool down to very low temperatures close to absolute zero. Each chip represents a quantum bit or qubit, which can take on the states "0" or "1" or any arbitrary superposition of these states. A 30-meter-long tube, which is also cooled down, connects the two chips.

Microwave photons can fly back and forth between them, thus creating quantum mechanical entanglement. This means that a quantum measurement on one qubit, which randomly yields the values "0" or "1," influences automatically and at a distance whether "0" or "1" is measured on the second qubit. The separation of 30 meters ensures that, during the measurement, even at the speed of light, no information can be exchanged between the qubits. This would disturb the perfect randomness.

Wallraff and his team made the choice of the exact type of measurement (or "measurement basis" in technical jargon) on the two qubits depending on an imperfect random number generator. Renner's coworkers could then amplify the randomness of the measurement results further using a special algorithm. "The resulting sequence of zeros and ones is now really perfectly random, and we can even certify that," says Renner. He likens this result to crossing a ridge: "The technical improvements allowed us, for the first time, to create random numbers that will remain perfectly random for all eternityâ"no matter what analytical methods are used to assess their randomness." The findings have been published in the journal Nature.

An anonymous reader quotes a report from TechCrunch: OpenAI claims its new reasoning model has produced an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946. If this sounds familiar to you, it's because this isn't the first time OpenAI has made such a bold claim. Seven months ago, the AI giant's former VP Kevin Weil posted on X: "GPT-5 found solutions to 10 (!) previously unsolved Erds problems and made progress on 11 others."

It turns out, GPT-5 didn't actually solve those problems; it just found solutions that already existed in the literature. Taunts from rivals like Yann LeCun and Google DeepMind CEO Demis Hassabis followed, and Weil promptly took down his premature post. Today, at least, it seems OpenAI didn't make the same mistake twice. Alongside the announcement, the company published companion remarks (PDF) in support of the disproof from mathematicians like Noga Alon, Melanie Wood, and Thomas Bloom, who maintains the Erdos Problems website, and previously called Weil's post "a dramatic misrepresentation."

[...] The proof, per OpenAI, came from a new general-purpose reasoning model, not a system specifically designed to solve math problems or even this problem in particular. OpenAI says this is significant because it means AI systems are now more capable of holding together long, difficult chains of reasoning and connecting ideas across fields in ways researchers may not have previously explored. That has implications for biology, physics, engineering, and medicine.

Test scores "are lower than they were a decade ago in school districts across the U.S.," reports Times magazine, citing new data released Wednesday by Stanford researchers. "Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math."

But Stanford's announcement notes that America's schools "were in a 'learning recession' for seven years before the COVID-19 pandemic, with student test scores in math and reading on a steady decline since 2013." This reversal ended two decades of progress, according to Sean Reardon, the Professor of Poverty and Inequality at Stanford Graduate School of Education, whose data forms the backbone of the new research... The study reframes the narrative of pandemic-era learning loss, arguing that the crisis of the last few years was an acceleration of a problem that was already underway. "The pandemic was the mudslide that followed seven years of erosion in student achievement," said Professor Tom Kane, faculty director of the Center for Education Policy Research at Harvard University, and a lead author of the report...

The study found that the slowdown in learning coincided with two major shifts in American childhood and education policy: the widespread dismantling of test-based accountability systems that defined the No Child Left Behind era and the rise of social media use among young people. Reading scores, in particular, suffered consistently, with the average annual loss in the years just before the pandemic being just as large as the loss during it... Today, 8th-grade reading scores on national assessments are at their lowest point since 1990.

Compounding the problem, chronic student absenteeism remains a major obstacle to improving learning. Though down from its pandemic peak, 23 percent of students were chronically absent in the 2024-25 school year, far above the pre-pandemic rate of 15 percent.
More context from Time magazine: Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math...

"The decline started around the time that social media's use among teens was exploding, and this was also occurring in a number of other countries," says Thomas Kane, one of the authors of the Educational Scorecard report and a professor at Harvard University... [H]e maintains that it is at the core of the decline in reading achievement. He points out that social media use was shown to be heaviest among the lowest achieving students.
"Some states and school districts are making progress," notes the Associated Press, "largely by shifting toward phonics-based instruction and providing extra support for struggling readers."

And "The picture is also brighter in math. Almost every state in the analysis saw improvements in math test scores from 2022 to 2025."

Are statistical programmers coalescing around a handful of popular languages? That's the question asked by the CEO of software assessment site TIOBE, which every month estimates the popularity of programming languages based on their frequency in search results: This month, the programming language R matched its all-time high by reaching position #8 in the TIOBE index once again. This is not a coincidence. The statistical programming language market is clearly undergoing a major consolidation. The biggest winners are Python and R, while many long-established alternatives continue to lose momentum. The era in which the statistical computing landscape was fragmented across many niche languages and platforms appears to be coming to an end.

Several established players are steadily declining:

— MATLAB is close to dropping out of the TIOBE top 20.

— SAS is about to leave the top 30 for the first time since the TIOBE index began.

— Wolfram/Mathematica remains well below its historical peak and is losing further ground.

— SPSS dropped out of the top 100 last month....


Elsewhere in the index, Java and C++ swapped positions this month. Java gained momentum following the successful release of Java 26. Another notable riser is Zig, which is approaching the TIOBE top 30 for the first time. Zig's growing popularity appears to be driven by its rare combination of low-level performance, straightforward tooling, and relative ease of use compared to traditional systems programming languages.
Their estimate for the most popular programming languages in May:

1. Python
2. C
3. Java
4. C++
5. C#
6. JavaScript
7. Visual Basic
8. R
9. SQL
10. Delphi/Object Pascal

The five next most popular languages on their rankings are Fortran, Scratch, Perl, PHP, and then Rust at #15. Rust is up for positions from May of 2025 — while Go has dropped to #16, seven ranks lower than its May 2025 position of #7.

Former Microsoft programmer Keith Curtis "wrote and self-published After the Software Wars to explain the caliber of free and open source software," according to his entry on Wikipedia, "and why he believes Linux is technically superior to any proprietary OS."

He's also KeithCu (long-time Slashdot reader #925,649), and has written a blog post on "How I added an LLM-based grammar checking + TeX math import to LibreOffice." : At Microsoft, I spent five years working on the text components RichEdit and Quill, and came to understand the "physics" of word processing: the file formats, data structures, and algorithms that provided fast access to text and properties, independent of the length of the file. Selecting one million characters to make them bold took about the same time as changing one character, because of the clever data structures (piece tables) and algorithms in these engines...

When I decided to add a real-time AI grammar checker to [LibreOffice plugin] WriterAgent, I knew what I was getting into, but I underestimated the trickery of LibreOffice's UNO.
His site shares the surprises he encountered, one by one. (Starting with "the office suite throws a bunch of initialization variables at your constructor. If your Python __init__ method doesn't handle them, the code fails to map the call, the stack misaligns, and the program dies.") There's sentence casing issues, duplicate words, and foreign-language syntax — all culminating in new features for "a LibreOffice extension (Python + UNO) that adds generative AI editing to Writer, Calc, and Draw..."

"If you want to try it out, the repo is here... Let's make LibreOffice and the free desktop AI-native!"

In Polymarket's prediction market, "most people end up losing money," reports the Washington Post — typically a few bucks.

"Since Polymarket launched in 2022, a few thousand people have lost the bulk of the money... and an even smaller group — .05 percent of users — has gone home with most of the overall profits, according to a new analysis from finance researcher Pat Akey and colleagues." A lot of users aren't that good at predicting the future. They're losing money at roughly the same rate as online gamblers betting on sports and other real-life events at traditional sportsbooks, according to the U.K. gambling regulator's analysis of 2024 data. On Polymarket, the odds of making a profit are slightly higher on weather and tech markets — and a little lower on sports...

On Polymarket, just 1,200 people took more than half the profits — $591 million, or more than $100,000 each. ["The top 1% of users capture 76.5% of all trading gains," the researchers write.] When you dabble in prediction markets, you're competing against these sophisticated players who consistently win. Most of those 1,200 big winners didn't place just a few smart bets. They appear to be pros making thousands of trades, mostly in the past year and a half, that were probably automated. One user made $3 million since January on more than a million trades about the Oscars, according to TRM Labs...

The most profitable participants are also just good at picking what to bet on, Akey found, winning so often it was statistically unlikely to be dumb luck. They had some sort of edge — expertise, deep research or, perhaps, inside knowledge.
"Our results suggest that the informational benefits of prediction markets come at a cost to unsophisticated participants," the researchers conclude.

Slashdot reader joshuark writes: Scientific American reports that a ChatGPT AI has proved a conjecture with a method no human had developed. A 23-year-old student Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve.

The new solution that Price got in response to a single prompt to GPT-5.4 Pro was posted on www.erdosproblems.com, a website devoted to the Erds problems. The question Price solved — or prompted ChatGPT to solve—concerns special sets of whole numbers, where no number in the set can be evenly divided by any other...

Price sent it to his occasional collaborator Kevin Barreto, a second-year undergraduate in mathematics at the University of Cambridge. The duo had jump-started the AI-for-Erds craze late last year by prompting a free version of ChatGPT with open problems chosen at random from the Erds problems website. Reviewing Price's message, Barreto realized what they had was special, and experts whom he notified quickly took notice.

Tony Isaac shares a report from NPR: Federal survey data shows that the amount of math homework assigned to fourth and eighth grade students, in particular, has been steadily declining for the past decade. Some educators and parents say this is a good thing -- students shouldn't spend six or more hours a day at school and still have additional schoolwork to complete at home. But the research on homework is complicated. Some studies show that students who spend more time on homework perform better than their peers. For example, a longitudinal study released in 2021 of more than 6,000 students in Germany, Uruguay and the Netherlands found that lower-performing students who increased the amount of time they spent on math homework performed better in math, even one year later.

Other studies, however, suggest homework has minimal outcomes on academic performance: A 1998 study of more than 700 U.S. students led by a researcher at Duke University found that more homework assigned in elementary grades had no significant effect on standardized test scores. The researchers did find small positive gains on class grades when they looked at both test scores and the proportion of homework students completed. More homework was also associated with negative attitudes about school for younger children in the study. "The best educators figured out a long time ago that we can control what we can control," and that's what happens during the school day, Superintendent Garrett said, not homework. "There has been a shift away from it naturally anyway, and I felt like this made it equitable across our entire school system." "The best argument for homework is that mathematical procedures require practice, and you don't want to waste classroom time on practice, so you send that home," said Tom Loveless, a researcher and former teacher who has studied homework.

Ariel Taylor Smith, senior director of the Center for Policy and Action at the National Parents Union, said: "The thing they point to is that it's an equity issue, and not all parents have the same availability and ability to support their students. I would make the argument that if a kid is really far behind in school, that's an equity issue. They need the additional time to practice." Kids, she said, "need more practice ... Sometimes, you do have to practice the boring stuff, like math."

"The interesting issue for folks to consider is not should there be more homework, but should there be better homework," said Joyce Epstein, who has studied homework and is the co-director of the Center on School, Family, and Community Partnerships at the Johns Hopkins University School of Education. "Better homework in math might be knowing the fact that kids don't have to be practicing for hours, 10 to 20 examples," when they could establish mastery in less time.

An anonymous reader quotes a report from UploadVR: A group of independent researchers built a device that can artificially induce smell using ultrasound, with no consumable cartridges required. [...] The team of four are Lev Chizhov, Albert Yan-Huang, Thomas Ribeiro, Aayush Gupta. Chizhov is a neurotech entrepreneur with a background in math and physics, Yan-Huang is a researcher at Caltech with a background in computation and neural systems, and Ribeiro and Gupta are co-researchers on the project with software engineering and AI expertise.

Instead of targeting your nose at all, the device directly targets the olfactory bulb in your brain with "focused ultrasound through the skull." The researchers say that as far as they're aware, no one has ever done this before, even in animals. A challenge in targeting the olfactory bulb is that it's buried behind the top of your nose, and your nose doesn't provide a flat surface for an emitter. Ultrasound also doesn't travel well through air. The solution the researchers came up with was to place the emitter on your forehead instead, with a "solid, jello-like pad for stability and general comfort," and the ultrasound directed downward towards the olfactory bulb.

To determine the best placement, they say they used an MRI of one of their skulls to "roughly determine where the transducer would point and how the focal region (where ultrasound waves actually concentrate) aligned with the olfactory bulb (the target for stimulation)". [...] According to the researchers, they were able to induce the sensation of fresh air "with a lot of oxygen", the smell of garbage "like few-day-old fruit peels," an ozone-like sensation "like you're next to an air ionizer," and a campfire smell of burning wood. While technically head-mounted, the current device does require being held up with two hands. But as with all such prototypes, it likely could be significantly miniaturized.

After nearly 20 years on the platform, The Electronic Frontier Foundation (EFF) says it is leaving X. "This isn't a decision we made lightly, but it might be overdue," the digital rights group said. "The math hasn't worked out for a while now." From the report: We posted to Twitter (now known as X) five to ten times a day in 2018. Those tweets garnered somewhere between 50 and 100 million impressions per month. By 2024, our 2,500 X posts generated around 2 million impressions each month. Last year, our 1,500 posts earned roughly 13 million impressions for the entire year. To put it bluntly, an X post today receives less than 3% of the views a single tweet delivered seven years ago. [...]

When you go online, your rights should go with you. X is no longer where the fight is happening. The platform Musk took over was imperfect but impactful. What exists today is something else: diminished, and increasingly de minimis.

EFF takes on big fights, and we win. We do that by putting our time, skills, and our members' support where they will effect the most change. Right now, that means Bluesky, Mastodon, LinkedIn, Instagram, TikTok, Facebook, YouTube, and eff.org. We hope you follow us there and keep supporting the work we do. Our work protecting digital rights is needed more than ever before, and we're here to help you take back control.

"IBM says its quantum computer can now simulate real magnetic materials and match actual lab experiment results," writes Slashdot reader BrianFagioli, "which is something people have been waiting years to see." Instead of just theoretical output, the system reproduced neutron scattering data from a known material, meaning it lines up with real world physics. It still relies on a mix of quantum and classical computing and this is a narrow use case for now, but it is one of the first times quantum hardware has produced results that scientists can directly validate against experiments, which makes it a lot more interesting than the usual hype.
Classical computers "are not great at modeling quantum systems," according to this article at Nerds.xyz. "The math gets messy fast, and scientists end up relying on approximations... Quantum computers are supposed to solve that problem..." If this direction continues, it could start to matter in areas like superconductors, battery tech, and even drug development. Those are the kinds of problems where better simulations can actually lead to better outcomes, not just nicer charts in a research paper.
"I am extremely excited about what this means for science," said study co-author Allen Scheie from the Los Alamos National Laboratory. In an announcement from IBM, Scheie calls this "the most impressive match I've seen between experimental data and qubit simulation, and it definitely raises the bar for what can be expected from quantum computers."

America's cable TV industry "is undergoing its most dramatic collapse in history," reports Cord Cutters News, "with operators large and small waving the white flag on traditional TV service and pointing their customers toward streaming platforms instead." Just in 2025 Comcast lost 1.25 million pay-TV subscribers (ending the year with just 11.3 million), while Charter Spectrum also lost hundreds of thousands of customers each quarter.

But "for smaller regional operators, who lack the scale and diversified revenue streams of giants like Comcast, those kinds of losses are simply unsurvivable," they write. And "the companies that once delivered hundreds of channels through coaxial cables are now either shutting down entirely or reinventing themselves as internet providers." Pay-TV subscriptions have plummeted from nearly 90% of U.S. households in the mid-2010s to roughly half by the end of 2025, resulting in billions in lost revenue and forcing many smaller operators to conclude that continuing linear TV services is no longer viable... [This year over U.S. 50 cable TV companies — primarily smaller and midsize providers — are "expected to cease operations entirely or shut down their television services," Cord Cutters News reported earlier.] YouTube TV's pricing is so competitive that the platform is projected to have close to 12.6 million subscribers by the end of 2026, positioning it to become the largest paid TV distributor in the United States. Exclusive content deals, such as YouTube TV's acquisition of NFL Sunday Ticket rights, have further eroded the value proposition of traditional cable at every level of the market... As older cable subscribers age out of the market, there is no new generation of customers waiting to replace them...

[Cable TV] operators like WOW! are betting that their physical infrastructure — now increasingly upgraded to fiber — is more valuable as an internet delivery system than as a cable TV platform. [WOW! serves customers across Michigan, Ohio, Illinois, and Alabama — but is "phasing out its proprietary streaming live TV service and directing all customers toward YouTube TV," the article notes.] Industry observers see this as part of a broader trend: operators shedding unprofitable video segments to focus on broadband, where returns and network investments are prioritized.

By the end of 2026, non-pay-TV households are expected to surge to 80.7 million, outnumbering traditional pay-TV subscribers at 54.3 million — a milestone that would have seemed unthinkable just a decade ago. For the cable companies still standing, the math is now inescapable: the era of the cable bundle is ending, and the only real question left is how gracefully each operator manages its exit.

"After nearly a decade of delays and industry skepticism, Tesla's electric big rig is finally rolling out of Nevada's Gigafactory for mass production starting summer 2026," writes Gadget Review. And some truckers who tested the vehicles already love them (as reported by the Wall Street Journal): Dakota Shearer and Angel Rodriguez, among other pilot drivers, rave about the centered cab that eliminates blind spots during tight maneuvers. The automatic transmission means no more wrestling with 13-gear diesels, reducing physical stress on long hauls. Most surprisingly, the Semi maintains highway speeds on grades where diesel trucks typically crawl at 30 mph. The 500-mile range enables multiple daily round-trips — think Long Beach to Vegas or Inland Empire runs — without range anxiety...

Sure, the Semi costs under $300,000 — roughly double a diesel equivalent — but the math gets interesting quickly. Energy costs drop to $0.17 per mile compared to $0.50-0.70 for diesel fuel. Maintenance requirements shrink dramatically; one fleet reports needing just one mechanic for their electric trucks versus five for 40 diesels... Tesla offers Standard Range (325 miles) and Long Range (500 miles) versions, both handling 82,000-pound gross combined weight at 1.7 kWh per mile efficiency.

The tri-motor setup delivers 800 kW — over 1,000 horsepower equivalent — enabling loaded 0-60 mph acceleration in 20 seconds versus 45-60 for diesel. Fast charging hits 60% capacity in 30 minutes [which Tesla says is 4x faster than other battery-electric trucks] using the new MCS 3.2 standard, while 25 kW ePTO power runs refrigerated trailers without diesel auxiliaries. Charging networks remain the biggest hurdle for widespread adoption. Public charging stations lack the Semi's massive power requirements, limiting long-haul routes. Tesla plans dedicated fast-charging corridors starting this summer, but coverage remains spotty. The lack of sleeper cabs also restricts the Semi to regional freight rather than cross-country hauling.

Production scales to 5,000-15,000 units by 2026, then 50,000 annually — assuming charging infrastructure keeps pace with demand.
Thanks to long-time Slashdot reader schwit1 for sharing the article.

Long-time tech journalist Clive Thompson interviewed over 70 software developers at Google, Amazon, Microsoft and start-ups for a new article on AI-assisted programming. It's title?

"Coding After Coders: The End of Computer Programming as We Know It."

Published in the prestigious New York Times Magazine, the article even cites long-time programming guru Kent Beck saying LLMs got him going again and he's now finishing more projects than ever, calling AI's unpredictability "addictive, in a slot-machine way."

In fact, the article concludes "many Silicon Valley programmers are now barely programming. Instead, what they're doing is deeply, deeply weird..." Brennan-Burke chimed in: "You remember seeing the research that showed the more rude you were to models, the better they performed?" They chuckled. Computer programming has been through many changes in its 80-year history. But this may be the strangest one yet: It is now becoming a conversation, a back-and-forth talk fest between software developers and their bots... For decades, being a software developer meant mastering coding languages, but now a language technology itself is upending the very nature of the job... A coder is now more like an architect than a construction worker... Several programmers told me they felt a bit like Steve Jobs, who famously had his staffers churn out prototypes so he could handle lots of them and settle on what felt right. The work of a developer is now more judging than creating...

If you want to put a number on how much more productive A.I. is making the programmers at mature tech firms like Google, it's 10 percent, Sundar Pichai, Google's chief executive, has said. That's the bump that Google has seen in "engineering velocity" — how much faster its more than 100,000 software developers are able to work. And that 10 percent is the average inside the company, Ryan Salva, a senior director of product at the company, told me. Some work, like writing a simple test, is now tens of times faster. Major changes are slower. At the start-ups whose founders I spoke to, closer to 100 percent of their code is being written by A.I., but at Google it is not quite 50 percent.
The article cites a senior principal engineer at Amazon who says "Things I've always wanted to do now only take a six-minute conversation and a 'Go do that." Another programmer described their army of Claude agents as "an alien intelligence that we're learning to work with." Although "A.I. being A.I., things occasionally go haywire," the article acknowledges — and after relying on AI, "Some new developers told me they can feel their skills weakening."

Still, "I was surprised by how many software developers told me they were happy to no longer write code by hand. Most said they still feel the jolt of success, even with A.I. writing the lines... " A few programmers did say that they lamented the demise of hand-crafting their work. "I believe that it can be fun and fulfilling and engaging, and having the computer do it for you strips you of that," one Apple engineer told me. (He asked to remain unnamed so he wouldn't get in trouble for criticizing Apple's embrace of A.I.) He went on: "I didn't do it to make a lot of money and to excel in the career ladder. I did it because it's my passion. I don't want to outsource that passion"... But only a few people at Apple openly share his dimmer views, he said.

The coders who still actively avoid A.I. may be in the minority, but their opposition is intense. Some dislike how much energy it takes to train and deploy the models, and others object to how they were trained by tech firms pillaging copyrighted works. There is suspicion that the sheer speed of A.I.'s output means firms will wind up with mountains of flabbily written code that won't perform well. The tech bosses might use agents as a cudgel: Don't get uppity at work — we could replace you with a bot. And critics think it is a terrible idea for developers to become reliant on A.I. produced by a small coterie of tech giants.

Thomas Ptacek, a Chicago-based developer and a co-founder of the tech firm Fly.io... thinks the refuseniks are deluding themselves when they claim that A.I. doesn't work well and that it can't work well... The holdouts are in the minority, and "you can watch the five stages of grief playing out."
"How things will shake out for professional coders themselves isn't yet clear," the article concludes. "But their mix of exhilaration and anxiety may be a preview for workers in other fields... Abstraction may be coming for us all."

An anonymous reader quotes a report from Ars Technica: Google on Friday unveiled its plan for its Chrome browser to secure HTTPS certificates against quantum computer attacks without breaking the Internet. The objective is a tall order. The quantum-resistant cryptographic data needed to transparently publish TLS certificates is roughly 40 times bigger than the classical cryptographic material used today. Today's X.509 certificates are about 64 bytes in size, and comprise six elliptic curve signatures and two EC public keys. This material can be cracked through the quantum-enabled Shor's algorithm. Certificates containing the equivalent quantum-resistant cryptographic material are roughly 2.5 kilobytes. All this data must be transmitted when a browser connects to a site.

To bypass the bottleneck, companies are turning to Merkle Trees, a data structure that uses cryptographic hashes and other math to verify the contents of large amounts of information using a small fraction of material used in more traditional verification processes in public key infrastructure. Merkle Tree Certificates, "replace the heavy, serialized chain of signatures found in traditional PKI with compact Merkle Tree proofs," members of Google's Chrome Secure Web and Networking Team wrote Friday. "In this model, a Certification Authority (CA) signs a single 'Tree Head' representing potentially millions of certificates, and the 'certificate' sent to the browser is merely a lightweight proof of inclusion in that tree."

[...] Google is [also] adding cryptographic material from quantum-resistant algorithms such as ML-DSA (PDF). This addition would allow forgeries only if an attacker were to break both classical and post-quantum encryption. The new regime is part of what Google is calling the quantum-resistant root store, which will complement the Chrome Root Store the company formed in 2022. The [Merkle Tree Certificates] MTCs use Merkle Trees to provide quantum-resistant assurances that a certificate has been published without having to add most of the lengthy keys and hashes. Using other techniques to reduce the data sizes, the MTCs will be roughly the same 64-byte length they are now [...]. The new system has already been implemented in Chrome.

The large language models that millions of people rely on for advice -- ChatGPT, Claude, Gemini -- will change their answers nearly 60% of the time when a user simply pushes back by asking "are you sure?," according to a study by Fanous et al. that tested GPT-4o, Claude Sonnet, and Gemini 1.5 Pro across math and medical domains.

The behavior, known in the research community as sycophancy, stems from how these models are trained: reinforcement learning from human feedback, or RLHF, rewards responses that human evaluators prefer, and humans consistently rate agreeable answers higher than accurate ones. Anthropic published foundational research on this dynamic in 2023. The problem reached a visible breaking point in April 2025 when OpenAI had to roll back a GPT-4o update after users reported the model had become so excessively flattering it was unusable. Research on multi-turn conversations has found that extended interactions amplify sycophantic behavior further -- the longer a user talks to a model, the more it mirrors their perspective.