From the student newspaper at the University of California, Berkeley: Richard Borcherds, a UC Berkeley professor and Fields medalist, no longer grades his students' homework. After seeing how many of his students used AI to consistently receive top scores, he felt there was "no point" in paying readers to grade for correctness in homework, as AI use is now unavoidable.

Borcherds revealed this in a sit-down interview Tuesday, following OpenAI's announcement that AI agents apparently solved one of mathematics' most difficult unsolved problems, the Navier-Stokes existence and smoothness problem. "If AI continues to improve at present rate, then, in a few years, there'll be nothing human mathematicians can do that AI can't do better," Borcherds said.

Borcherds said he currently grades on completion if a student hands in their homework. He admitted that it is "not a very good system," but at the moment, he has no alternative... Campus has tried several AI detection tools, including Turnitin and Rumi, but have determined that these tools aren't adequate enough to be standardized on campus.
The article points out that at the university, each class's AI policy "is up to the discretion of faculty."

OpenAI says an unreleased internal model has produced 372 results that either resolve or make substantial progress on open problems in mathematics and theoretical computer science. The company says almost all were generated by a single AI agent from a single prompt. "This would be a striking difference from OpenAI's earlier blockbuster solution to the Navier-Stokes problem, which was produced through the collective efforts of a 10,000-strong agentic swarm that cost millions of dollars in computing power," reports Scientific American. From the report: OpenAI revealed the results in a GitHub repository at 6 P.M. EDT. The deluge will take mathematicians months to parse through and understand -- including to determine whether the proofs contain novel and important ideas or are mostly mash-ups of existing techniques. But many of the results have already been verified in Lean -- a programming language that validates a proof's logic -- and are therefore all but certain to be correct.

Among the results claimed are a solution to the four-dimensional Kakeya conjecture, improvements on some of the world's most important computer algorithms and actual progress toward math's scariest problem, the Riemann hypothesis.

"Weird Al" Yankovic's name has just been approved for a new asteroid — (14331) Alyankovic = 1981 EC26 — by the International Astronomical Union, reports Space.com.

Yankovic's asteroid was championed by planetary scientist Allison McGraw joined by "several heavy hitters in the planetary science field, according to the Tucson Star. (Astrophysicist Steve Desch from the School of Earth and Space Exploration at Arizona State University; Tim McCoy, one of the main curators of meteorites at the Smithsonian Institution; and University of Arizona research scientist Melissa Brucker, leader of the Spacewatch program, which has discovered more than 179,000 asteroids.) The scientists also convinced the International Astronomical Union to name an asteroid after one of Yankovic's major influences, famous musical humorist and political satirist Tom Lehrer, who died last year at age 97. Lehrer's work includes "The Elements," a 1959 song in which he recites the entire periodic table to the tune of Gilbert and Sullivan's "Major-General's Song." "He was a mathematician and teacher and also wrote math- and science-themed songs," McGraw said. "We felt that someone who had that kind of science enthusiasm really deserved to have their name up in the sky...." McGraw is hoping that naming space rocks after stars like Lehrer and "Weird Al" will cast some reflected light on two things she's passionate about: asteroid research and science communication.
Six years ago a 92-year-old Tom Lehrer released all his lyrics into the public domain. (Wikipedia notes he'd "largely retired" by the 1970s to become a mathematics teacher at the University of California, Santa Cruz.) Slashdot ran a brief career retrospective when Lehrer died last year at age 97.

And the IAU writes that "Generations of scientists have been inspired" by Weird Al Yankovic's "comedic musical works, including 'It's All About the Pentiums' and 'White and Nerdy'." ("I'm fluent in JavaScript as well as Klingon," Yankovic sings in the latter.) He appears in a song envisioning a rap battle between Bill Nye the Science Guy and Sir Isaac Newton... And in 1999 he recorded a five-minute summation of Star Wars: Phantom Menace, sung to the wistful tune of Don McLean's American Pie. Performing it last month in a NPR Tiny Desk concert, "most of the audience was singing along," remembers an interviewer at NPR. "It felt like something that was very personal to them." Weird Al: It's one of those songs that means a lot to people, particularly "Star Wars" fans, of course. But I mean, I see a lot of people in the audience cosplaying as Jedi Knights and waving their light sabers... I've even heard that, you know, they play that song at "Star Wars" conventions, and people get weepy... [I]t really hits people in a tender place somehow...

"Oh my, my, this here Anakin guy
may be Vader someday later, now he's just a small fry.
And he left his home and kissed his mommy goodbye,
sayin' soon, I'm gonna be a Jedi."
Yankovic has led a geek-friendly career. In the heyday of Napster, he released an anthem-style parody mocking the arguments of the Recording Industry Association of America, titled "Don't Download This Song. ("Even Lars Ulrich knows it's wrong...")

"Once in a while maybe you will feel the urge
To break international copyright law...
you start out stealing songs, then you're robbing liquor stores
And selling crack and running over school kids with your car..."

As a student at Cal Poly, San Luis Obispo, Yankovic bootstrapped a career in 1979 by recording his first novelty song "My Bologna" (a parody of "My Sharona" by the Knack) while playing his accordion in a bathroom for its acoustics. And even the IAU acknowledged the geeky themes in his 1999 song "It's All About the Pentiums" (a filk on Puff Daddy's "It's All About the Benjamins").

"You're usin' a 286? Don't make me laugh
Your Windows boots up in what, a day and a half?
You could back up your whole hard drive on a floppy diskette
You're the biggest joke on the Internet..."

The New York Post wrote that OpenAI "stunned the mathematics world" last week when it announced its AI model "cracked a legendary math problem left unsolved for nearly a century in just 88 hours."

But a New York University professor "has come forward alleging OpenAI may have copied his work after asking him to collaborate — with one of the company's scientists allegedly warning he might 'ruin his career' if he refused." [P]rofessor Tristan Buckmaster, a British-Australian researcher who trained in Germany... had been working with Long Island-raised math prodigy Levent Alpöge, a researcher with OpenAI rival Anthropic, on a different elusive fluid dynamics equation, known as the Euler problem, when he found a solution using AI tools from both Anthropic and ChatGPT. He said scientists from OpenAI approached him and tried to convince him to jointly announce the solution, claiming they had independently solved Navier-Stokes. Buckmaster questioned the timing of their finding the solution in a blog post, noting OpenAI had only done so "in the past few days" — after the company became aware of his work. He asked OpenAI experts whether its AI systems used to solve the equation had been "trained on, or had access to" his research, but said they declined to answer, prompting him to wonder how they had advanced toward the solution so quickly. The puzzle is "not the direction one arrives at in a few days," Buckmaster wrote incredulously...

In OpenAI's post on its website sharing the Navier-Stokes proof, which it credited to Astra, the company's latest AI bot, the firm insisted no user data was incorporated into its model to solve the problem. However, it noted that it "cannot rule out" that anonymized data was used to "improve our models."

"We congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work," OpenAI said in the announcement.

Buckmaster shot back, "Is it ethical to use customer's data to try to scoop their customer?"
Alpöge's affiliation with Anthropic "seems to have been a sore point for OpenAI," writes TechCrunch. Buckmaster's public statement says the OpenAI's Sebastien "twice asserted that he wanted Levent removed from authorship" and also said that "it was so annoying that Levent works at Anthropic" — but that Buckmaster still refused to remove the Anthropic mathematician's credit. I said that if OpenAI released its result in the way proposed I would go public with what happened. The reply was, "Why would you ruin your career?" [OpenAI's Bubeck said later on X that he'd meant unfounded accusations could damage Buckmaster's career.] I replied that I am an academic, and asked why he thought going public would ruin my career. The reply was, "If you don't want me to be nice, then I don't have to be nice."
Buckmaster later told Australia's public broadcaster ABC that the companies building AI "have zero respect for the scientific community. I mean, it's appalling, honestly." But he went on to say AI has made the race to publish mathematical breakthroughs pointless. "I think it's pointless. Like, I think the game is up...." Professor Buckmaster said he and Dr Alpöge had more research they could publish but questioned the point of racing ahead with it when AI could perform work that had previously occupied researchers for years.

He said there needed to be a discussion about how mathematics should operate in this new environment. Professor Buckmaster said some mathematicians had stopped publicly sharing what they were working on to avoid tipping off AI companies about their plans. He has called on academics and people from OpenAI, Anthropic and DeepMind to discuss ground rules for how they worked together. He said the companies' power carried responsibilities that went beyond competing to solve problems before their rivals...

Building on the work of mathematicians Diego Córdoba and Luis Martínez-Zoroa, the pair found a solution to the Euler problem, a stepping stone to the Navier-Stokes problem. Professor Buckmaster said he spent weeks checking and improving the "slop" proofs generated by AI...

For now, Professor Buckmaster said he wanted to finish existing work and support his PhD students and postdoctoral researchers, rather than chase another Millennium Prize. "I think it's more important to re-evaluate what math is," he said.
"The New York University professor also warned Australia about the centralisation of resources into the hands of a small number of privately owned foreign companies..."

An anonymous reader quotes a report from The New York Times: OpenAI said on Tuesday that its newest artificial intelligence technology had solved one of the "Millennium Problems," a collection of important unanswered math questions meant to push the world's leading mathematicians to new heights. The announcement is an another clear sign that A.I. is fundamentally changing the upper reaches of mathematics, which have long been viewed as a pinnacle of human achievement. The change has excited some mathematicians, while stirring concern among others.

Over the past year, A.I. systems successfully solved a wide range of problems that have bedeviled mathematicians for decades. But these problems were not as complex, nor as closely watched, as the one that OpenAI's technology has solved over the past several days. The Millennium Problems are among the most heavily researched in the field. "This is a spectacular culmination of the arc we have seen over the past twelve months," OpenAI researcher Sebastien Bubeck said of the company's new solution.

The company announced that one of its latest models, which has not yet been released to the public, needed just 88 hours to solve what mathematicians call "the Navier-Stokes existence and smoothness problem." This problem involves a series of equations that are often used to predict the weather. The equations describe the movement of water and other fluids. The Navier-Stokes problem, which has no clear practical value, asks whether these equations completely break down in certain situations. OpenAI's proof claims to have defined just such a situation.

This would imply, at least theoretically, that the laws of physics themselves would break down under certain conditions: that, for example, water could be made to spontaneously explode. But mathematicians and physicists do not believe that this mathematical breakdown could really lead to such an outcome in the physical world. The Navier-Stokes problem was one of seven "Millennium Problems" selected by the Clay Mathematics Institute in the year 2000 as a way of tracking the progress of mathematics in the new millennium. The institute, founded by an American businessman named Landon T. Clay, offered a million dollars for the first correct solution to each problem. Before OpenAI's announcement, only one of the problems had been solved. OpenAI says it managed to solve the Navier-Stokes problem by coordinating as many as 10,000 AI agents, an extremely expensive process that may have cost millions of dollars in computing power.

For those curious about the findings, the company has released a paper (PDF) and formal Lean proof.

Teen reading, math, and science scores have fallen to their lowest levels since international PISA testing began in 2000, with 15-year-olds now reading at roughly the level previously expected of students a year younger. The Organization for Economic Co-operation and Development (OECD) linked weaker results to more screen time, less reading for pleasure, and digital distraction. It also found that students who regularly used AI chatbots for tasks like drafting essays and summarizing text scored about 20 points lower in science, roughly equivalent to a year of learning. Reuters reports: A 2012 reading high of 501 on PISA's index had declined to a score of 466 last year. The test is widely regarded as the world's leading gauge of educational performance and its results are closely scrutinized by governments and educators. "In reading, more screen time and less reading for pleasure have gone hand in hand with weaker results," OECD head Mathias Cormann told a press conference. "We're also seeing more hasty reading, students rushing through a text and giving a quick wrong answer."

Reading scores fell more sharply in students from better-off backgrounds, the study added. Hindered by falling reading standards, average teenage science and maths skills were also at their lowest this century. Science fell from a 2009 high of 506 on the index to 486, while maths was down from 502 to 469 in the same period. East Asia was the best-performing region. Chinese cities that participated in the test, Japan, Korea, Singapore and Taiwan had the best educational systems with Britain and Estonia the only countries outside the region that made it into the top 10 in reading, mathematics and science.

The Linux Foundation's director of IT infrastructure says they now spend more CPU cycles "rendering commits for scrapers than we spend on all other kinds of legitimate access." At any one time, across 5 geo-distributed nodes, there are 14 CPU cores doing nothing but rendering git commits as html....

[W]hen a source is guaranteed to be LLM-free, like the entire history of kernel commits, it's worth its weight in gold as a source of training data... At the time of writing, linux.git is about 1.48 million commits. Oh, and we have about 922 forks of it on git.kernel.org — but don't worry, it's actually extremely efficient on the backend, since it's mostly the same objects in every fork. Unless, of course, you're a scraper, in which case you have, oh, several BILLION valid URLs you can scrape, only to get 922 duplicates of the same 1.48 million commits — which is exactly what the scrapers are doing. But wait, it's not just commits itself. You can also ask for patches, plain renders, diffs between arbitrary commits — cgit is happy to let you, which was perfect for the times when the Internet was for humans or crawlers who obeyed robots.txt, and is AWFUL right about now, because we can generate 1.2 METRIC BAJILLION valid URLs just for a single fork of linux.git.

Initially, this was the solution — look through the logs, find out which IPs are obvious scraper bots, and fail2ban them. At first, this was easy, because the bots helpfully told you who they were via their user-agent. Then, they wised up and started pretending that they were random vanilla browsers. So, we started banning them by IP — after all, it's easy to figure out that an IP that is trying to grab every possible commit in a 8-year-old abandoned fork of linux is not really some lone Chrome on Windows user who is just furiously clicking every link that comes across their screen. The bots then started fanning out to entire subnets, but this was still meh, because obviously an IP coming from Google Compute is just pretending to be a Firefox user...

And... that's when things turned really, really ugly. Suddenly, the crawlers were coming from millions of random residential or mobile IPs, all pretending to be random modern browsers. An IP like that would make 4-5 requests and then never show up in the logs again... They descended like swarms of locust, hit hard and fast until the system fell over and then moved on to the next target until you recovered. Then, they returned. Rinse. Repeat. They still do that — welcome to the wonderful world of "proxy SDK monetization." It's big business, and your TV is probably doing it...

Today, git.kernel.org receives about 6M daily requests demanding to see random commits. Of these, 66% are still immediately batted away with the Anubis challenge, but 33% are now solving the math and getting through to the main site — because apparently what we have to offer is worth spending a ton of cycles to calculate the Anubis challenge... With a bunch of generous assumptions, legitimate requests are only about 2% of git.kernel.org traffic — everything else are scrapers...

[W]e're turning off features to reduce the number of crawlable URLs and to gate off actions that are expensive for us to run. Expect to lose some functionality, at least when accessing our resources anonymously. Trust me, we hate it just as much as you, but at this point it's a necessity... [W]e promise to still offer all of our data for download to anyone who asks. You just may have to jump through more hoops to get it.

Sorry.

America's public education system has become "an avenue for multitrillion-dollar companies to enact their agendas," argues a NewYork Times reporter who's covered that tech industry influence for over 10 years. Big tech companies "advocate for new laws to require schools to teach industry-aligned subjects. They sit on committees that help shape learning standards for public school students..."

The reporter concludes that "Silicon Valley's efforts to capture the attention of education professionals and provide student training materials can also double as marketing or indoctrination." They enable some of the most powerful corporations on the planet to spin rosy technology stories for administrators, teachers and students, giving them a sanitized view of industry practices and product risks. Many parents would likely object if, say, Exxon Mobil wrote their children's environmental science curriculum. Or if Purdue Pharma trained their children's biology teachers. Yet, over the last decade, schools have often embraced the technology industry in similar ways with few questions asked.
The article excerpts the soon-to-be-released book " Coding Kids: Big Tech's Battle to Remake Public Schools," with the reporter adding more thoughts on LinkedIn: Every few years, Silicon Valley has urged American public schools to quickly adopt the latest technology or teach the latest tech subject: Big Data analytics; laptops for every child; algorithm-driven math and reading apps; compter programming; virtual reality. And each time the urgent arguments sounded remarkably similar: if schools just adopted the latest tech tool, or taught the latest tech subject, it would democratize learning for every child and equip students with valuable career skills.
Despite good intentions, "today we know some of those classroom tech drives did not exactly pan out as advertised." Now some of the same companies that urged schools to provide laptops and coding lessons are employing similar arguments to urge schools to quickly adopt A.I. tools. I've spent years covering Chromebooks and student chatbot use. And I'm troubled by our collective amnesia around school tech.
Thanks to long-time Slashdot reader theodp for sharing the article.

An anonymous reader quotes a report from CNBC: U.S. President Donald Trump's administration is reportedly considering new tariffs on semiconductors used in the U.S., as tech giants race to beat China in the artificial intelligence infrastructure build-out. The duties will be imposed on an expanded range of tech products made alongside chips, including laptops, data center servers and gaming hardware, eight people familiar with the matter told Politico in a report published Thursday.

The measures, which are still in the early phases and subject to significant changes over the next few months, will be introduced via a staggered rollout period, according to the report. The White House did not immediately respond to a request for comment from CNBC, but told Politico: "Reshoring semiconductor manufacturing is a top priority for President Trump, whose policies have already secured hundreds of billions of dollars of investments in this key sector." The tech industry is pushing back on the tariffs, warning they could raise chip costs, slow data-center construction, and undermine the very AI expansion the administration wants to promote. "This may be the single dumbest way imaginable to pursue American dominance in AI," said one tech official from a major industry group that also served in the first Trump administration. "It's like kneecapping yourself at the starting line."

Lobbyists say they have repeatedly urged officials to "tread carefully" because U.S. demand is surging while domestic chip capacity remains far too limited. The industry argues that factories take years to build and current domestic production cannot come close to satisfying demand.

One tech representative said "the math literally just does not work," noting that proposed duty-free allowances "wouldn't cover the hyperscalers alone." Meanwhile, the Computer and Communications Industry Association warned that higher costs and uncertainty could put the massive data-center buildout "in jeopardy."

"AI went from being very terrible to seemingly genuinely quite good at a professional level in a very short space of time..." says the Verge's AI reporter. But AI systems "are still truly, truly terrible at some areas of math... even the days of the week." If you look at academic math papers, a lot of the time you won't see numbers... So they're still terrible, but they're now also very good at this other part. As to why, at some point you reach a critical mass of what these systems can do. We saw it with writing, we've seen it with programming. They're very good at forging connections between different areas, applying old methods in new ways, those kinds of things. It appears that the newer models they're training have apparently reached that level where it clicks, and now it can do math...

There are areas now where it seems to be producing work that is on par with good mathematicians, alongside other parts where yeah, it can't count. All caught up in that is whether it's going to rewrite employment structures or funding structures... One would hope [math researchers] would branch out into all of these new exciting areas or pose new questions. But AI won't do that, and that's the concern. And then that would leave the field quite sterile, and it will have all of these things that have been done, and maybe nothing left to pursue... [A] lot are scared that it's closing off the field. So by definition, those breakthroughs that lead to something surprising and new that you can say, "Oh, this works here," may not be happening anymore...

There were some bleak responses from graduate students I saw in essays posted online. Where's their place in this as future researchers? Do they have a place in this? Is it as glorified AI proof checkers? That will be quite an unsatisfying career, I imagine. Or maybe not, I don't know. We will see. I think anything used properly will be a net boon.
They also had an interesting response when asked if AI democratizes access to high-level mathematical proofs: A lot of the mathematicians I spoke to were almost quite weary of this, actually. They love the idea, in theory, of democratizing access. They're also quite fed up with AI-generated or -assisted papers that are flooding every publication imaginable, as well as the pre-print servers that they use in these fields. Some of those I spoke to said things like, "Oh, I got three emails this week alone with people being like, 'Hey, is this legit?'" Because they thought they'd solved something with ChatGPT or with Claude, and they also don't have the mathematical skills to check whether they've actually solved something.

On the flip side, there are parts where they said, "Well, we've got a talented undergrad who's done something that a talented undergrad would probably have never managed, and here they are doing grad-level work and they've produced a paper that is legit." And in the bigger scheme of things, a few I spoke to said, "Well yeah, a lot of these are in the ivory tower. Having access to this kind of thing globally could really boost access to the kind of things here." On the flip side, the cost. These things cost a lot to run.

An anonymous reader quotes a report from The Guardian: A math professor at the University of California, Berkeley, criticizing a "severe" math deficiency among students in an op-ed for the San Francisco Standard, admitted to using artificial intelligence to help edit the piece. The Standard published a 2,000-word piece by Zvezdelina Stankova last week, in which the professor said some of her math students were "five to eight years" behind and lacked a "middle school" education on fractions and basic algebra. Stankova said the UC system's test-blind admissions were to blame, suggesting that students who weren't sufficiently prepared for the rigor of Berkeley's mathematics program were admitted because a longstanding benchmark like the SAT had disappeared.

Over the weekend, journalists at Berkeley's student newspaper, the Daily Californian, noticed the op-ed's language sounded like AI. According to Berkeley sophomore Francis Luo, they ran it through AI-detection software Pangram, which claimed 33% of the op-ed had been generated or assisted by AI. In response, Stankova admitted she had used AI software to "help edit the piece" but that the article was "the result of several hundred person-hours of intensive human work, of which about 80 hours are my own."

Stankova said by email she had used AI to locate "numerous documents and articles related to the initiative" but that "all analysis is the result of the team members." When asked about their AI usage policy, the Standard -- which published the op-ed -- shared this statement by email: "While AI may assist, our expectation is that humans are behind every article we publish and take responsibility for every word. Our understanding in working with the author of this op ed was -- and continues to be -- that this piece reflects her and her colleagues' extensive original analysis, research and expertise." Should the op-ed have been entirely written without artificial assistance or is the use of AI a distraction from the argument at hand? That's the question at the heart of the debate playing out online.

According to the op-ed, Stankova and her peers found that the number of students with a "severe deficit" in their readiness for a calculus I class had tripled after the University of California adopted test-blind admissions. Some students were allegedly five to eight years behind and struggling with basic algebra and fractions, among other concepts.

Much of that decline, Stankova argues, is from the removal of standardized tests like the SAT and ACT, which served as important benchmarks for identifying whether applicants were prepared for Berkeley-level coursework.

An anonymous reader quotes a report from Forbes: The cost of producing new results on ten longstanding mathematical problems just fell to $2,000, according to OpenAI, which says its Astra model generated machine-checkable proofs for questions that had resisted human progress for decades. OpenAI published the work on August 1 and used it to give its next major model family a name: Astra. The results run across group theory, high-dimensional geometry, coding theory, quantum complexity, lattice cryptography and extremal combinatorics. They arrived as a 249-page manuscript collection and, alongside it, something the field has not seen attached to an AI claim before at this scale: a machine-checkable certificate for every single result.

The problems were not textbook exercises dressed up as discoveries. Each had been open for at least ten years, most of them far longer, and several sit at the center of their subfields:
- A construction establishing the existence of non-sofic groups, a question that has occupied group theorists for years.
- A disproof of Connes's rigidity conjecture, a long-standing problem in the theory of von Neumann algebras.
- An improvement to the general upper bound on sphere-packing density in high dimensions, a bound that had stood since 1978.
- Three problems come from the catalogue of open questions left behind by Paul Erdos. The announcement follows another result from May, when OpenAI used a similar reasoning model to produce an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946.

Anthropic's Claude Mythos Preview has "found flaws in a weakened version of a digital encryption standard that is in pervasive use throughout the internet," reports The New York Times. Researchers said the model discovered novel attacks against weakened versions of AES and the experimental post-quantum HAWK system, including one that was 200 to 1,000 times faster than previous human-developed methods. From the report: The flaws identified do not concern a cryptographic standard currently in use today, which means that modern banking and communication systems are not subject to immediate potential intrusions from A.I. Instead, Anthropic's technology cracked a watered-down version of an algorithm for Advanced Encryption Standard, or A.E.S., a ubiquitous protocol that safeguards web traffic, wireless networks, data storage and more. It is common to perform tests on weaker versions of encryption algorithms to understand whether more powerful computers could someday crack the actual standards, akin to solving a simpler math problem to identify whether patterns may exist for a more complicated one. In the testing, Mythos was able to break the weaker version of Advanced Encryption Standard in a way that Anthropic said made an assault 200 to 1,000 times faster than what previous human research had managed to do. While the immediate ramifications are minimal, the long-term implications could be significant. In previous tests, large-language models seemingly could not match or best what humans can do in the mathematically dense field of cryptographic research, but their rapid advances could suggest a future in which top models can surmount traditional internet security protections that are foundational to just about everything that takes place on the internet.

[...] In addition to the attack on the encryption standard, Mythos also orchestrated another improved attack against a different digital cryptographic system known as HAWK that is designed to be bulletproof against both traditional and quantum computers. HAWK is not currently in use, but under consideration by the National Institute of Standards and Technology to become a new standard. The HAWK attack was validated by its authors, and independent cryptographers reviewed the Advanced Encryption Standard attack, Anthropic said, adding that it had shared its findings with the U.S. government and industry partners ahead of publication. Mythos devised the cryptographic attack on A.E.S. almost entirely autonomously, Anthropic said, but only after first refusing to contemplate the problem because it believed it was impossible to improve on existing methods of analysis. But after some coaxing, the chatbot sat with the puzzle for about a week before engineering its novel attack. Two human researchers then worked for nearly a month to verify that the method appeared correct. "Given that we are constantly underestimating the power and time of availability of future models, are we really comfortable that two years from now strong encryption won't be threatened?" said Glenn S. Gerstell, the former general counsel at the National Security Agency.

"Mathematicians would tell you that it shouldn't be possible given current computing powers to break strong encryption in any meaningful time," added Mr. Gerstell, who helped write a report on cryptology in 2022. "But I don't think the capabilities of future models in the medium term -- before quantum computing or quantum-proof cryptography -- should be dismissed as trivial in this context."

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."