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.

Elon Musk told podcast host Dwarkesh Patel and Stripe co-founder John Collison that space will become the most economically compelling location for AI data centers in less than 36 months, a prediction rooted not in some exotic technical breakthrough but in the basic math of electricity supply: chip output is growing exponentially, and electrical output outside China is essentially flat.

Solar panels in orbit generate roughly five times the power they do on the ground because there is no day-night cycle, no cloud cover, no atmospheric loss, and no atmosphere-related energy reduction. The system economics are even more favorable because space-based operations eliminate the need for batteries entirely, making the effective cost roughly 10 times cheaper than terrestrial solar, Musk said. The terrestrial bottleneck is already real.

Musk said powering 330,000 Nvidia GB300 chips -- once you account for networking hardware, storage, peak cooling on the hottest day of the year, and reserve margin for generator servicing -- requires roughly a gigawatt at the generation level. Gas turbines are sold out through 2030, and the limiting factor is the casting of turbine vanes and blades, a process handled by just three companies worldwide.

Five years from now, Musk predicted, SpaceX will launch and operate more AI compute annually than the cumulative total on Earth, expecting at least a few hundred gigawatts per year in space. Patel estimated that 100 gigawatts alone would require on the order of 10,000 Starship launches per year, a figure Musk affirmed. SpaceX is gearing up for 10,000 launches a year, Musk said, and possibly 20,000 to 30,000.

An anonymous reader quotes a report from Science.org: Some 10,109 doctoral-trained experts in science and related fields left their jobs last year as President Donald Trump dramatically shrank the overall federal workforce. That exodus was only 3% of the 335,192 federal workers who exited last year but represents 14% of the total number of Ph.D.s in science, technology, engineering, and math (STEM) or health fields employed at the end of 2024 as then-President Joe Biden prepared to leave office. The numbers come from employment data posted earlier this month by the White House Office of Personnel Management (OPM). At 14 research agencies Science examined in detail, departures outnumbered new hires last year by a ratio of 11 to one, resulting in a net loss of 4224 STEM Ph.D.s. The graphs that follow show the impact is particularly striking at such scientist-rich agencies as the National Science Foundation (NSF). But across the government, these departing Ph.D.s took with them a wealth of subject matter expertise and knowledge about how the agencies operate.

[...] Science's analysis found that reductions in force, or RIFs, accounted for relatively few departures in 2025. Only at the Centers for Disease Control and Prevention, where 16% of the 519 STEM Ph.D.s who left last year got pink RIF slips, did the percentage exceed 6%, and some agencies reported no STEM Ph.D. RIFs in 2025. At most agencies, the most common reasons for departures were retirements and quitting. Although OPM classifies many of these as voluntary, outside forces including the fear of being fired, the lure of buyout offers, or a profound disagreement with Trump policies, likely influenced many decisions to leave. Many Ph.D.s departed because their position was terminated.

An anonymous reader quotes a report from TechCrunch: Over the weekend, Neel Somani, who is a software engineer, former quant researcher, and a startup founder, was testing the math skills of OpenAI's new model when he made an unexpected discovery. After pasting the problem into ChatGPT and letting it think for 15 minutes, he came back to a full solution. He evaluated the proof and formalized it with a tool called Harmonic -- but it all checked out. "I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they struggle," Somani said. The surprise was that, using the latest model, the frontier started to push forward a bit.

ChatGPT's chain of thought is even more impressive, rattling off mathematical axioms like Legendre's formula, Bertrand's postulate, and the Star of David theorum. Eventually, the model found a Math Overflow post from 2013, where Harvard mathematician Noam Elkies had given an elegant solution to a similar problem. But ChatGPT's final proof differed from Elkies' work in important ways, and gave a more complete solution to a version of the problem posed by legendary mathematician Paul Erdos, whose vast collection of unsolved problems has become a proving ground for AI.

For anyone skeptical of machine intelligence, it's a surprising result -- and it's not the only one. AI tools have become ubiquitous in mathematics, from formalization-oriented LLMs like Harmonic's Aristotle to literature review tools like OpenAI's deep research. But since the release of GPT 5.2 -- which Somani describes as "anecdotally more skilled at mathematical reasoning than previous iterations" -- the sheer volume of solved problems has become difficult to ignore, raising new questions about large language models' ability to push the frontiers of human knowledge. Somani examined the online archive of more than 1,000 Erdos conjectures. Since Christmas, 15 Erdos problems have shifted from "open" to "solved," with 11 solutions explicitly crediting AI involvement.

On GitHub, mathematician Terence Tao identifies eight Erdos problems where AI made meaningful autonomous progress and six more where it advanced work by finding and extending prior research, noting on Mastodon that AI's scalability makes it well suited to tackling the long tail of obscure, often straightforward Erdos problems.

Progress is also being accelerated by a push toward formalization, supported by tools like the open-source "proof assistant" Lean and newer AI systems such as Harmonic's Aristotle.

An R&D lab under America's Energy Department annnounced this week that "Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex mathematical problems that underpin scientific and engineering challenges."

Phys.org publishes the announcement from Sandia National Lab: In a paper published in Nature Machine Intelligence, Sandia National Laboratories computational neuroscientists Brad Theilman and Brad Aimone describe a novel algorithm that enables neuromorphic hardware to tackle partial differential equations, or PDEs — the mathematical foundation for modeling phenomena such as fluid dynamics, electromagnetic fields and structural mechanics. The findings show that neuromorphic computing can not only handle these equations, but do so with remarkable efficiency. The work could pave the way for the world's first neuromorphic supercomputer, potentially revolutionizing energy-efficient computing for national security applications and beyond...

"We're just starting to have computational systems that can exhibit intelligent-like behavior. But they look nothing like the brain, and the amount of resources that they require is ridiculous, frankly," Theilman said.For decades, experts have believed that neuromorphic computers were best suited for tasks like recognizing patterns or accelerating artificial neural networks. These systems weren't expected to excel at solving rigorous mathematical problems like PDEs, which are typically tackled by traditional supercomputers. But for Aimone and Theilman, the results weren't surprising. The researchers believe the brain itself performs complex computations constantly, even if we don't consciously realize it. "Pick any sort of motor control task — like hitting a tennis ball or swinging a bat at a baseball," Aimone said. "These are very sophisticated computations. They are exascale-level problems that our brains are capable of doing very cheaply..."

Their research also raises intriguing questions about the nature of intelligence and computation. The algorithm developed by Theilman and Aimone retains strong similarities to the structure and dynamics of cortical networks in the brain. "We based our circuit on a relatively well-known model in the computational neuroscience world," Theilman said. "We've shown the model has a natural but non-obvious link to PDEs, and that link hasn't been made until now — 12 years after the model was introduced." The researchers believe that neuromorphic computing could help bridge the gap between neuroscience and applied mathematics, offering new insights into how the brain processes information. "Diseases of the brain could be diseases of computation," Aimone said. "But we don't have a solid grasp on how the brain performs computations yet." If their hunch is correct, neuromorphic computing could offer clues to better understand and treat neurological conditions like Alzheimer's and Parkinson's.

An anonymous reader quotes a report from Wired: [P]erhaps AI can, in fact, learn in a more human way -- by figuring out interesting questions to ask itself and attempting to find the right answer. A project from Tsinghua University, the Beijing Institute for General Artificial Intelligence (BIGAI), and Pennsylvania State University shows that AI can learn to reason in this way by playing with computer code. The researchers devised a system called Absolute Zero Reasoner (AZR) that first uses a large language model to generate challenging but solvable Python coding problems. It then uses the same model to solve those problems before checking its work by trying to run the code. And finally, the AZR system uses successes and failures as a signal to refine the original model, augmenting its ability to both pose better problems and solve them.

The team found that their approach significantly improved the coding and reasoning skills of both 7 billion and 14 billion parameter versions of the open source language model Qwen. Impressively, the model even outperformed some models that had received human-curated data. [...] A key challenge is that for now the system only works on problems that can easily be checked, like those that involve math or coding. As the project progresses, it might be possible to use it on agentic AI tasks like browsing the web or doing office chores. This might involve having the AI model try to judge whether an agent's actions are correct. One fascinating possibility of an approach like Absolute Zero is that it could, in theory, allow models to go beyond human teaching. "Once we have that it's kind of a way to reach superintelligence," [said Zilong Zheng, a researcher at BIGAI who worked on the project].

Longtime Slashdot reader MrFreak shares a public radio interview from 2015 discussing artificial intelligence as inference over abstract inputs, along with scaling limits, automation, and governance models, where for-profit engines are constrained by nonprofit oversight: Recorded months before OpenAI was founded, the conversation treats intelligence as math plus incentives rather than something mystical, touching on architectural bottlenecks, why "reasoning" may not simply emerge from brute force, labor displacement, and institutional design for advanced AI systems. Many of the themes align closely with current debates around large language models and AI governance.

The recording was revisited following recent remarks by Sergey Brin at Stanford, where he acknowledged that despite Google's early work on Transformers, institutional hesitation and incentive structures limited how aggressively the technology was pursued. The interview provides an earlier, first-principles perspective on how abstraction, scaling, and organizational design might interact once AI systems begin to compound.

Asus has been showcasing its new 5K 27-inch ROG Strix 27 Pro gaming monitor running at 5,120 x 2,880 resolution and up to 180Hz, but even Nvidia's flagship RTX 5090 struggles to deliver smooth frame rates at this demanding pixel count. In testing conducted by Asus, the RTX 5090D -- a Chinese-exclusive variant with weaker AI performance -- achieved just 51 frames per second in a Cyberpunk 2077 benchmark at ultra ray traced settings. The test system ran an AMD Ryzen 9950X3D processor, had DLSS set to balanced, and kept frame generation disabled. The same configuration running at 4K managed 77 fps, around 50% higher.

The underlying math is simple: 5K resolution requires rendering 78% more pixels than 4K. That 218 PPI pixel density delivers impressive sharpness up close, but Asus chose an IPS panel over OLED technology to reach it, trading away deeper black levels and faster response times. Asus appears to be positioning the monitor as a dual-mode display -- 5K for productivity and video, 1440p at up to 330Hz for gaming. Early Chinese listings have it priced at the equivalent of $800, roughly what you'd pay for a larger 4K OLED panel.