"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.

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47% of recent graduates say AI has already impacted hiring in their field, according to an April survey from ZipRecruiter. "But is AI really the problem, or is it more complicated?" asks NPR. "Here's what economists have to say." According to the Federal Reserve Bank of New York, the unemployment rate for recent graduates — which it defines as 22-to-27-year-olds with a new bachelor's degree or higher — was 5.7% as of June, more than the rate for all workers, which stands at 4.1%... Stanford University economist Erik Brynjolfsson says AI is impacting the labor market for entry-level roles. "AI is not the whole story, but it's part of the story and the evidence is building," he says. Using payroll data, Brynjolfsson and his co-authors found that since late 2022 — when large language models like ChatGPT started popping up — early-career workers ages 22 to 25 in AI-exposed roles, like software developers and marketing managers, have experienced a 16% relative employment decline. In comparison, employment rates for older workers in AI-exposed fields and for all workers in jobs that aren't easily automated — like home health aides, physical therapists and construction workers — remained stable or have grown over that same time period... [Though he also says often when companies pull back on hiring, they cut junior roles first.] Harvard University economist David Deming isn't convinced that AI is to blame for the challenging early-career job market. "If you look very carefully at the timing, it looks like the decline in junior hiring actually started a bit like six months before ChatGPT was released. And so what that tells me is it's something else," Deming says. "I think it's more like remote work." A recent analysis from the New York Fed found that companies are less likely to hire recent college grads into roles that can be done remotely. As remote jobs increased following the COVID-19 pandemic, so did unemployment among younger college grads, the New York Fed found. The analysis also found that AI didn't explain the rise in unemployment among younger workers and that remote work was more of a driving force... Employers hiring for remote jobs may be less likely to choose an entry-level candidate because it's harder to train them from afar... [And with remote positions, there's many more senior candidates to choose from.] And here's another thing to consider: University of Chicago economist Anders Humlum says if AI were replacing entry-level jobs, you'd expect to see companies that rely heavily on AI to hire fewer workers. But that's not what the data shows. Humlum points to a study done by the financial accounting firm Ramp and the workforce research company Revelio Labs. The study examined AI spending and employee head count across more than 21,000 U.S. firms, from early 2021 to early 2026. It found that at companies making the largest AI investments, entry-level head count grew by 12% over the two years following AI adoption.

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According to a post from chief product officer Hari Srinivasan, "over a million people" have clicked on LinkedIn's new "Seems like AI slop" button, which launched after one analysis found 41% of the platform's longform posts were fully AI-generated. The Verge reports: In Thursday's post, Srinivasan said that users are overall "now experiencing 40% less views on what we classify as AI slop from just a few weeks ago." LinkedIn is also adding a new message that will tell users who make a post if "Some members told us this post seems like AI." "We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan said.

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An anonymous reader quotes a report from The Economist: Students and school children are increasingly using artificial intelligence. A survey last year by Chegg, an ed-tech firm, found that 80% of rich-world undergraduates used it in their studies. More recent polls put the figure at 94% in Britain and 93% in Germany. Such widespread adoption has fueled concerns about the impact the technology might be having on learning. Teachers report marking identikit essays that they suspect are churned out by ChatGPT. Yet firm evidence on AI's educational effects has been lacking. David Stromberg of Stockholm University and Victor Lei and Wu Yanhui of the University of Hong Kong set out to fill the gap. They tracked 27,000 pupils aged 12-18 in China, where AI adoption has been fast. Around 80% reported using models such as Doubao and DeepSeek; the other 20% formed the control group. The results are eye-opening. After six months, pupils using AI saw their average homework score rise by 18% across all subjects. The time they took to complete each assignment fell from an average of 64 minutes to 45. But come exam time, the same students scored 20% below their classmates who had not called on AI's help. Homework scores once predicted exam performance; now those who score highest are, perversely, more likely to do worse in exams. The takeaway: "AI can boost learning productivity, but only for those who use the technology intelligently," reports The Economist. "Students must resist the temptation to reach for an AI-generated answer before thinking things through for themselves."

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Linus Torvalds says AI "enormously helped" him track down a stubborn Intel Xe graphics driver bug that took 24 debugging patches and 18 kernel boots to isolate. "I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it," wrote Torvalds on the commit. "I suspect those things have been trained by people who may not be quite as stubborn as I am..." Phoronix reports: The patch by Linus Torvalds is for the Xe kernel driver and the change is no longer hand out the flat Compute Command Streamer (CCS) storage as usable vRAM. On a Battlemage G21 graphics card, he was hitting a scenario where there was a mismatch where the usable memory ended and hit a case where the GDM display manager would end up being endlessly restarted. [...] Linus Torvalds views AI as a useful tool and in this case reaffirmed he found it "enormously helped" his effort in tracking down this Intel graphics driver bug. The Intel Xe driver fix is merged to Linux 7.3 Git and is also marked to back-porting to the stable kernel branches.

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Google is giving publishers a new way to fight declining referral traffic from AI-powered search by letting them embed a "Preferred Sources" button that readers can use to favor their sites across Search, Discover, and Google News. "The idea is to make it easier for readers to find links from the sites they know and trust when they're searching for content or interacting with Google's AI to learn about a topic or read the latest news," reports TechCrunch. "As of May's launch, the company said that people across the web had already selected over 345,000 unique sources through this method." From the report: Becoming a preferred source can drive more traffic to publishers' websites, Google said. In earlier studies, it found that people are twice as likely to click through to a preferred source when available. By offering publishers these additional tools, Google is trying to assuage the damage that the rapid growth of AI-powered search features has had on traffic-dependent businesses. Alongside the new button, Google said that readers will soon be able to customize their Discover feed in Google's app in their own words. To use this feature, readers will tap any three-dot menu in the feed and then tell Google what topics they'd like to see more or less of, using natural language commands. This helps Google refine the feed in real time.

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Microsoft is expanding Windows Task Manager to show per-process NPU and GPU neural-engine usage, giving users more visibility into which apps are consuming hardware for AI workloads. The Register reports: The Processes tab can show NPU use alongside CPU and GPU activity, while the Performance tab displays overall utilization. [...] Microsoft was keen to point out the metrics that can be monitored. "As AI workloads become more common on Windows devices, visibility into NPU and GPU neural engine utilization can help you make better-informed decisions," the company said. "With the latest Task Manager improvements, you can monitor AI processing activity alongside CPU, memory, storage, and networking data from a familiar interface."

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A growing backlash against AI data centers is spilling into local politics, with residents in more than a dozen communities pushing recall elections against officials who approved projects. "People are standing up and saying, 'Sorry, we don't want these data centers in our communities for a number of reasons,'" Senator Bernie Sanders, the Vermont independent, said in an interview. "'If you can't have the guts to stand up to some large corporation, you should not continue serving in office.'" The New York Times reports: In Festus, Mo., critics of a local data center project gathered enough signatures in May to force a vote to remove the mayor and City Council members who had approved a $6 billion data center, though the effort is tied up in legal wrangling. In Mason, Mich., residents will vote in November over whether to recall a City Council member over a vote to create zoning rules for data centers. Voters in Yukon, Okla., will also decide in November whether to recall their mayor after the town moved to sell land to a data center developer. And residents of Independence, a city of 121,000 that is 10 miles from Kansas City, will vote on whether to remove Mr. Perkins on Sept. 1. Some local officials have already lost their jobs in the face of data center concerns in regularly scheduled elections. Last year, two members of the Georgia Public Service Commission lost their seats amid concern about data centers' driving up electricity prices. A Virginia state delegate, Geary Higgins, also lost his job last year after his opponent branded him "Data Center Geary." [...] Independence would be one of the first municipalities to hold a data-center-motivated recall election. A judge this month rejected a legal challenge by Mr. Perkins and others, who argued the recall violated Missouri's Constitution.

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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.

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