We may earn a commission from links on this page.In 2023, I decided to update my Apple Watch after consistently failing to wear my Series 4 for a number of years. I sold that one on Poshmark and began looking at newer models to find one with enough features to convince me to actually wear it. I opted to get a Series 8, although the Series 9 had just been released, as I was buying two: one for my mom and one for myself. As it turns out, that was a great decision.If you're searching for a new wearable or considering upgrading yours, you might also be wondering which of the older Apple Watch models is still useful today. My Series 8 is holding up beautifully three years after it was introduced, so I'm a big proponent of using older devices as long as possible. But not all Apple Watches will work as well as the Series 8 does in 2025. Don’t buy a watch Apple doesn’t support anymoreWe have to draw the line somewhere: Seven of Apple's watches are no longer supported, meaning they won't receive any software or security updates anymore. In addition, you run the risk that the watch will no longer be compatible with your iPhone or certain apps. In short, you shouldn't buy a watch that Apple doesn't support. That includes the following:Apple Watch Series 0Apple Watch Series 1Apple Watch Series 2Apple Watch Series 3Apple Watch Series 4Apple Watch Series 5Apple Watch SE (first-gen) While the company does currently support the Series 6, it is next in line to join this list. It's not clear when that will happen, but you can be sure it will. We'll see next week—when Apple reveals watchOS 26—whether the watch will be supported another year. If not, it'll be stuck on watchOS 11 for good.Performance and other generational Watch improvementsThere are considerations for older Apple Watch models that extend beyond their ability to simply run the latest operating system. With each generation, improvements are made in some form or another. For instance, the Series 4 introduced the ECG sensor, while the Series 6 introduced the blood oxygen sensor (though Apple had to disable the feature for the Series 9 and Ultra 2 in the U.S. due to a lawsuit). The Series 7 charges faster than its predecessors, and Apple has included fast charging on most watch models since (sorry, Apple Watch SE users).In general, each Apple Watch is faster than the last. Apple tends to put its newest S-Chip—the Apple Watch's processor—in its latest watch series. Simply put, a newer S-chip gives you a faster, more productive product. The Series 6 has an S6 chip, Series 7 has S7, and so on until you hit the Ultras. (The first-generation Ultra has an S8 chip like the Series 8, while the Ultra 2 has an S9 chip like the Series 9.)While there are some core features all currently supported watches share—like workout and swim tracking, sleep tracking, Apple Pay, ECG scanning, and the ability to read and respond to messages—newer models also each have some of their own special advancements and upgrades. Here's a brief list:The Series 7 introduced faster charging, a larger display, and more durable screen.The Series 8 brought temperature sensing, crash detection, and a low-power mode for conserving battery (as did the second-gen Apple Watch SE).The Series 9 debuted new gesture controls, on-device Siri access, more precise location tracking in Find My, and a display with double the brightness of the Series 8.The first-gen Apple Watch Ultra introduced a more durable titanium casing, custom shortcuts to apps and modes via the Action button, a depth gauge and water temperature sensor, more accurate GPS, a 36-hour battery life, and an emergency siren.The Apple Watch Ultra 2 introduced a display with a maximum brightness of 3,000 nits and on-device media playback. The Series 10 introduced the largest display available on a standard Apple Watch and faster charging. If you see a feature you absolutely need in a particular watch model, you'll have to spring for it. But if you just want something for core Apple Watch tasks, you can start to consider older options. Apple's watch comparison site can be a helpful tool for identifying different features among models. Battery degradation All tech degrades to some extent and the Apple Watch is no different—particularly when it comes to the battery. While there are ways to mitigate the problem, over time, the lithium-ion battery powering your wrist computer won't last as long as it used to. That might be a bigger issue than your watch's ability to download and support a new operating system. Apple's warranty doesn't cover batteries that wear down from normal use, and charges $99 for the repair, which you could instead put towards the purchase of a new watch. There is one exception: Battery service is free if you have AppleCare+ and your watch's battery holds less than 80% of its original capacity. You need to take your watch in to an Apple Store or service provider to have it tested. My watch was pre-owned, and while I have no way of knowing if it has its original battery, my battery life has not declined substantially in the two years I've been using it daily. I primarily use mine to track my workouts, vitals, and sleep, which means it's always running. I charge it while I'm in the shower and occasionally for a few minutes before bed, and that's about it. On an average day of constant notifications, mine lasts me a bit longer than the advertised 18-hour mark. Because I have little interest in the small improvements offered by the Series 9 and Series 10—like extra brightness, larger screen size, performance bumps, and advanced cycle tracking—the battery life is what would (or will) compel me to upgrade in the future, but for now, I have not noticed any problems. I asked my mom if she's noticed any battery degradation on hers, since I bought it at the same time and place as mine, and she said no. She uses hers to track walking workouts, talk on the phone, and monitor her sleep and vitals, too.Stick with the Series 7 or newerThoroughly consider which of the features on newer models are actually important to you before making any buying decision and, if you can, stay above a Series 7. The Series 6 is still functional, but, again, it's a matter of time until the company stops acknowledging that one completely. For now, I have been pleasantly surprised by how well my Series 8 has held up for two years. Its touchscreen has never faltered, the external buttons function perfectly, it syncs to all of my apps and devices with no problem, and it does exactly what I need it to do—which is to tell me how many steps I'm taking and how hard I'm exerting myself at the gym. If you're in the market for a smart watch, I see no reason that an older version shouldn't be considered, as long as it still runs the latest operating system. You can save a chunk of change by sourcing an older model from the resale or refurbished markets and put that money away for when Apple drops something super revolutionary in the wearable space. Apple doesn't sell anything below a Series 10 or SE directly anymore, so if you want a 6, 7, 8, or 9, you'll have to check the resale and refurbished markets. You'll definitely save some money that way (a new Series 10 starts at $399, though it can be found on sale, and the refurbished Series 8 I got is selling right now for $219). Apple Watch Series 8 (Renewed) $209.00 at Amazon $220.00 Save $11.00 Learn More Learn More $209.00 at Amazon $220.00 Save $11.00

May 30, 20252 min readLawmakers Form First Extreme Heat Caucus, Citing ‘Deadly Risk’The House of Representatives’ first caucus to address extreme heat is being launched by a Democrat from the Southwest and a Republican from the NortheastBy Ariel Wittenberg & E&E News A construction worker in Folsom, Calif., during a July 2024 heatwave that brought daytime highs of 110 degrees Fahrenheit. David Paul Morris/Bloomberg via Getty ImagesCLIMATEWIRE | An Arizona Democrat and a New York Republican are teaming up to form the Congressional Extreme Heat Caucus in an attempt to find bipartisan solutions for deadly temperatures.“We hope this caucus can make sure the United States is better prepared for the inevitable increase in temperatures, not just in Arizona and the Southwest but all across the country,” Arizona Rep. Greg Stanton (D) said in an interview.He's creating the caucus with New York Rep. Mike Lawler, a moderate Republican who bucked his party last year by expressing support for the nation's first proposed regulation to protect workers from heat by the Occupational Safety and Health Administration.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.“Extreme heat kills more Americans each year than any other weather event — over 1,300 lives lost, including 570 in New York alone — and it’s a growing threat to the Hudson Valley,” Lawler said in a statement. “That’s why I’m co-chairing the Heat Caucus to drive real solutions, raise awareness, and protect our communities from this deadly risk.”Stanton said he was excited to team up with Lawler, who understands that heat jeopardizes health even in northern climates.“He is from New York and I’m proud he recognizes how heat is important for workers,” he said.The caucus will be open to House lawmakers who have bipartisan ideas for addressing extreme heat. Noting that many Republicans have slammed OSHA's proposed heat rule, Stanton said the caucus doesn't have to find consensus on every policy, but members should be willing to search for common ground."It is important to have that conversation on what we can come together and agree on because that's how we get legislation passed in this town, even if we don't agree on how far to go," he said.Lawler and Stanton teamed up earlier this spring to protest workforce reductions at the Department of Health and Human Services that could degrade heat-related programs.In April, the pair wrote a letter to Health Secretary Robert F. Kennedy Jr., protesting layoffs that purged the entire staff of the Centers for Disease Control and Prevention’s Division of Environmental Health Science and Practice as well as the Low-Income Home Energy Assistance Program, which helps families pay for heating and cooling.“As we head into another summer — with projections suggesting 2025 will rank again among the warmest years on record, we cannot afford to limit our ability to counter the impacts of extreme heat,” they wrote in April with nine other lawmakers.Among the caucus' priorities is making LIHEAP funding more evenly distributed to southern states to help pay for cooling assistance. The program was initially created to help low-income families pay their heating bills during winter, and the majority of its funding still goes toward cold-weather states.“We have had too many deaths of people in their homes because they are unable to access programs that would help them access air conditioning,” Stanton said.Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2025. E&E News provides essential news for energy and environment professionals.

Elon Musk’s X experienced an outage on Friday, according to user reports and crowdsourced data from sites like Downdetector. From what TechCrunch staff has witnessed firsthand, the X website and app still loaded, but various features were not functioning properly, and entire feeds — like X’s algorithmic For You feed — didn’t load any content. Others reported that photos weren’t loading, X’s banking service XMoney wasn’t working, the timeline was bare, posts weren’t publishing, and search wasn’t returning results, among other things. Based on the timing of the user reports, the outage and errors seem to have begun just before 4 p.m. ET on Friday and seem to be resolving as of the time of writing. During the outage, thousands of users submitted reports to Downdetector, and others more directly complained in X posts and on other social networks, like Bluesky and Threads. This was the second outage for X in just over a week, as the service also failed on May 22, when X experienced a number of issues over a 24-hour period. During that time, messages wouldn’t load, timelines wouldn’t update, and some posts couldn’t be seen without refreshing the webpage. The current outage may be short-lived. We noticed that some features seemed to either be intermittently working, or worked on X’s mobile app but not on the desktop, for instance. Then, people began reporting that the service seemed to have come back up. The X Developer API v2 is still showing degraded status, however. Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW Just ahead of the outage, X began rolling out its new DM feature, XChat, into beta after pausing encrypted DMs as it worked to make improvements. It’s not clear if any of these changes impacted X’s stability, and the company no longer responds to press inquiries since Musk took ownership.

Scientists Investigate 2.2-Million-Year-Old Tooth Enamel to Unravel the Mysteries of Ancient Human Relatives By studying proteins preserved in teeth, researchers determined the sex of four Paranthropus robustus individuals that lived in southern Africa This skull of a 1.8-million-year-old Paranthropus robustus individual was unearthed in South Africa, but it was not one of the fossils included in the study. José Braga and Didier Descouens via Wikimedia Commons under CC BY-SA 4.0 Paranthropus robustus was a prehistoric, two-legged human relative that lived in southern Africa roughly two million years ago. Scientists have unearthed various P. robustus fossils, but because of the specimens’ age, they haven’t been able to glean much from them. Now, using a novel method, researchers say they’ve determined the sex of four P. robustus individuals by studying their teeth. The new analysis, published Thursday in the journal Science, also reveals insights into the genetic diversity of the broader Paranthropus genus. For the study, the team analyzed teeth discovered in a cave at the Swartkrans paleoanthropological site in South Africa, reports Live Science’s Kristina Killgrove. Because the fossils were so old—dating to between 1.8 million and 2.2 million years ago—researchers could not recover ancient DNA from them. So, instead, they turned to the relatively new field of paleoproteomics, or the study of preserved proteins. Ancient DNA degrades over time, particularly in hot places like southern Africa. So far, scientists studying hominin remains on the continent have only been able to successfully sequence DNA from material that’s less than 20,000 years old. But proteins can survive much longer than DNA, particularly in hard tooth enamel. When they analyzed the fossilized enamel of the P. robustus teeth, the researchers were able to identify specific protein sequences found only in males. This allowed them to determine that two of the P. robustus individuals were male and two were female. They were surprised to learn that one individual, named SK 835, was male. Based on the comparatively small size of that individual’s teeth, researchers had previously thought SK 835 was female, since male hominins tend to be larger than females, on average. This marks an important finding, as it supports the idea that dental measurements are not the most reliable way to determine the sex of ancient hominins. “Paleoanthropologists have long known that our use of tooth size to estimate sex was fraught with uncertainty, but it was the best we had,” says Paul Constantino, a paleoanthropologist at Saint Michael’s College who was not involved with the research, to ScienceNews’ Bruce Bower. “Being able to accurately identify the sex of fossils using proteins will be hugely impactful.” Further analyses of the fossils’ amino acid sequences revealed that SK 835 was less closely related to the other three individuals than they were to each other. That means it’s possible SK 835 represents a different species altogether—maybe the newly proposed Paranthropus capensis. After all, the team writes in the paper, the recent description of that species shows Paranthropus diversity “is currently underestimated and needs to be investigated further.” Or, perhaps the small size of SK 835’s teeth can be explained by microevolution—variations between P. robustus groups living at different sites. Scientists say they will need to get their hands on more Paranthropus fossils from multiple places to know for certain, per Science News. Moving forward, researchers hope they can one day use paleoproteomic methods to map the entire human family tree. Right now, however, their “ability to distinguish between different species is limited by the small number of different proteins present in enamel,” three of the study authors tell Live Science in an email. Scientists are also exploring other protein-sequencing techniques that are less destructive to fossil samples than the current methods. In the meantime, they’re excited about the potential of paleoproteomics to help them learn even more about humans’ ancient ancestors. “It opens entirely new avenues for understanding our evolutionary history [in Africa],” study co-author Marc Dickinson, a chemist at the University of York in England, says in a statement. Get the latest stories in your inbox every weekday.

Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language models (LLMs) attempt to mimic through structured approaches such as chain-of-thought (CoT) prompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks. Attempts to address these issues include methods like GRPO (Group Relative Policy Optimization), length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach. A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model (ARM), which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate. The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuning (SFT) with 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance. ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains. Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models. Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding

screen time Google Is Burying the Web Alive 5:00 A.M. saved Save this article to read it later. Find this story in your account’s ‘Saved for Later’ section. Photo-Illustration: Intelligencer By now, there’s a good chance you’ve encountered Google’s AI Overviews, possibly thousands of times. Appearing as blurbs at the top of search results, they attempt to settle your queries before you scroll — to offer answers, or relevant information, gleaned from websites that you no longer need to click on. The feature was officially rolled out at Google’s developer conference last year and had been in testing for quite some time before that; on the occasion of this year’s conference, the company characterized it as “one of the most successful launches in Search in the past decade,” a strangely narrow claim that is almost certainly true: Google put AI summaries on top of everything else, for everyone, as if to say, “Before you use our main product, see if this works instead.” This year’s conference included another change to search, this one more profound but less aggressively deployed. “AI Mode,” which has similarly been in beta testing for a while, will appear as an option for all users. It’s not like AI Overviews; that is, it’s not an extra module taking up space on a familiar search-results page but rather a complete replacement for conventional search. It’s Google’s “most powerful AI search, with more advanced reasoning and multimodality, and the ability to go deeper through follow-up questions and helpful links to the web,” the company says, “breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf.” It’s available to everyone. It’s a lot like using AI-first chatbots that have search functions, like those from OpenAI, Anthropic, and Perplexity, and Google says it’s destined for greater things than a small tab. “As we get feedback, we’ll graduate many features and capabilities from AI Mode right into the core Search experience,” the company says. I’ve been testing AI Mode for a few months now, and in some ways it’s less radical than it sounds and (at first) feels. It resembles the initial demos of AI search tools, including those by Google, meaning it responds to many questions with clean, ad-free answers. Sometimes it answers in extended plain language, but it also makes a lot of lists and pulls in familiar little gridded modules — especially when you ask about things you can buy — resulting in a product that, despite its chatty interface, feels an awful lot like … search. Again, now you can try it yourself, and your mileage may vary; it hasn’t drawn me away from Google proper for a lot of thoughtless rote tasks, but it’s competitive with ChatGPT for the expanding range of searchish tasks you might attempt with a chatbot. From the very first use, however, AI Mode crystallized something about Google’s priorities and in particular its relationship to the web from which the company has drawn, and returned, many hundreds of billions of dollars of value. AI Overviews demoted links, quite literally pushing content from the web down on the page, and summarizing its contents for digestion without clicking: Photo-Illustration: Intelligencer; Screenshot: Google Meanwhile, AI Mode all but buries them, not just summarizing their content for reading within Google’s product but inviting you to explore and expand on those summaries by asking more questions, rather than clicking out. In many cases, links are retained merely to provide backup and sourcing, included as footnotes and appendices rather than destinations: Photo-Illustration: Intelligencer; Screenshot: Google This is typical with AI search tools and all but inevitable now that such things are possible. In terms of automation, this means companies like OpenAI and Google are mechanizing some of the “work” that goes into using tools like Google search, removing, when possible, the step where users leave their platforms and reducing, in theory, the time and effort it takes to navigate to somewhere else when necessary. In even broader terms — contra Google’s effort to brand this as “going beyond information to intelligence” — this is an example of how LLMs offer different ways to interact with much of the same information: summarization rather than retrieval, regeneration rather than fact-finding, and vibe-y reconstruction over deterministic reproduction. This is interesting to think about and often compelling to use but leaves unresolved one of the first questions posed by chatbots-as-search: Where will they get all the data they need to continue to work well? When Microsoft and Google showed off their first neo-search mockups in 2023, which are pretty close to today’s AI mode, it revealed a dilemma: Search engines still provide the de facto gateway to the broader web, and have a deeply codependent relationship with the people and companies whose content they crawl, index, and rank; a Google that instantly but sometimes unreliably summarizes the websites to which it used to send people would destroy that relationship, and probably a lot of websites, including the ones on which its models were trained. And, well, yep! Now, both AI Overviews and AI Mode, when they aren’t occasionally hallucinating, produce relatively clean answers that benefit in contrast to increasingly degraded regular search results on Google, which are full of hyperoptimized and duplicative spamlike content designed first and foremost with the demands of Google’s ranking algorithms and advertising in mind. AI Mode feels one step further removed from that ecosystem and once again looks good in contrast, a placid textual escape from Google’s own mountain of links that look like ads and ads that look like links (of course, Google is already working on ads for both Overviews and AI Mode). In its drive to embrace AI, Google is further concealing the raw material that fuels it, demoting links as it continues to ingest them for abstraction. Google may still retain plenty of attention to monetize and perhaps keep even more of it for itself, now that it doesn’t need to send people elsewhere; in the process, however, it really is starving the web that supplies it with data on which to train and Two years later, Google has become more explicit about the extent to which it’s moving on from the “you provide us results to rank, and we send you visitors to monetize” bargain, with the head of search telling The Verge, “I think the search results page was a construct.” Which is true, as far as it goes, but also a remarkable thing to hear from a company that’s communicated carefully and voluminously to website operators about small updates to its search algorithms for years. I don’t doubt that Google has been thinking about this stuff for a while and that there are people at the company who deem it strategically irrelevant or at least of secondary importance to winning the AI race — the fate of the web might not sound terribly important when your bosses are talking nonstop about cashing out its accumulated data and expertise for AGI. I also don’t want to be precious about the web as it actually exists in 2025, nor do I suggest that websites working with or near companies like Meta and Google should have expected anything but temporary, incidental alignment with their businesses. If I had to guess, the future of Google search looks more like AI Overviews than AI mode — a jumble of widgets and modules including and united by AI-generated content, rather than a clean break — if only for purposes of sustaining Google’s multi-hundred-billion-dollar advertising business. But I also don’t want to assume Google knows exactly how this stuff will play out for Google, much less what it will actually mean for millions of websites, and their visitors, if Google stops sending as many people beyond its results pages. Google’s push into productizing generative AI is substantially fear-driven, faith-based, and informed by the actions of competitors that are far less invested in and dependent on the vast collection of behaviors — websites full of content authentic and inauthentic, volunteer and commercial, social and antisocial, archival and up-to-date — that make up what’s left of the web and have far less to lose. Maybe, in a few years, a fresh economy will grow around the new behaviors produced by searchlike AI tools; perhaps companies like OpenAI and Google will sign a bunch more licensing deals; conceivably, this style of search automation simply collapses the marketplace supported by search, leveraging training based on years of scraped data to do more with less. In any case, the signals from Google — despite its unconvincing suggestions to the contrary — are clear: It’ll do anything to win the AI race. If that means burying the web, then so be it. Sign Up for the Intelligencer Newsletter Daily news about the politics, business, and technology shaping our world. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. By submitting your email, you agree to our Terms and Privacy Notice and to receive email correspondence from us. Tags: Google Is Burying the Web Alive

Image by BSIP / UIG Via GettyStudiesA nasty bacterial superbug that kills hundreds of thousands of people per year is spreading through hospitals — and it feeds on something surprising, scientists have now found.Researchers from the Brunel University of London have found, per a new study in the journal Cell, that the bacterial Pseudomonas aeruginosa appears to "digest" medical plastic.Associated with more than 559,000 deaths globally per year, this drug-resistant bug is, according to the Center for Disease Control and Prevention, believed to cause other illnesses like pneumonia or urinary tract infections. People often seem to develop it after surgery, but until this study, researchers weren't aware that it might be living or feeding on medical plastics.By analyzing a strain of the bacteria taken from a patient's wound swab, the microbial researchers found that P. aeruginosa seems specifically to survive longer on polycaprolactone, a plastic used in all kinds of medical interventions ranging from sutures, stints, and surgical mesh to wound dressings, drug-delivery patches, and implants.In Brunel University's press release about the "world-first" research, study leader Ronan McCarthy said this finding suggests that medical professionals should rethink how bugs spread throughout hospitals and other healthcare settings."Plastics, including plastic surfaces, could potentially be food for these bacteria," McCarthy said. "Pathogens with this ability could survive for longer in the hospital environment."The study also, as the professor noted, "means that any medical device or treatment that contains plastic" — including the ventilators some pneumonia patients need and catheters necessary for UTIs — "could be susceptible to degradation by bacteria."Beyond its ability to break down such important medical tools, the researchers also found that the enzyme they isolated appears to grow stronger biofilms, or outer layers that help bacteria resist antibiotics and make them harder to treat, after digesting plastic.Though there will obviously need to be more study to figure out how best to head off this plastic-eating menace, there's a 200-year history of pathogenic adaptation behind P. aeruginosa that suggests it may eventually circumvent any such measures.Still, McCarthy pointed out that scientists "need to understand the impact this has on patient safety.""Plastic is everywhere in modern medicine," he said, "and it turns out some pathogens have adapted to degrade it."Share This Article

"Duolingo had been riding high," reports Fast Company, until CEO Luis von Ahn "announced on LinkedIn that the company is phasing out human contractors, looking for AI use in hiring and in performance reviews, and that 'headcount will only be given if a team cannot automate more of their work.'" But then "facing heavy backlash online after unveiling its new AI-first policy", Duolingo's social media presence went dark last weekend. Duolingo even temporarily took down all its posts on TikTok (6.7 million followers) and Instagram (4.1 million followers) "after both accounts were flooded with negative feedback." Duolingo previously faced criticism for quietly laying off 10% of its contractor base and introducing some AI features in late 2023, but it barely went beyond a semi-viral post on Reddit. Now that Duolingo is cutting out all its human contractors whose work can technically be done by AI, and relying on more AI-generated language lessons, the response is far more pronounced. Although earlier TikTok videos are not currently visible, a Fast Company article from May 12 captured a flavor of the reaction: The top comments on virtually every recent post have nothing to do with the video or the company — and everything to do with the company's embrace of AI. For example, a Duolingo TikTok video jumping on board the "Mama, may I have a cookie" trend saw replies like "Mama, may I have real people running the company" (with 69,000 likes) and "How about NO ai, keep your employees...." And then... After days of silence, on Tuesday the company posted a bizarre video message on TikTok and Instagram, the meaning of which is hard to decipher... Duolingo's first video drop in days has the degraded, stuttering feel of a Max Headroom video made by the hackers at Anonymous. In it, a supposed member of the company's social team appears in a three-eyed Duo mask and black hoodie to complain about the corporate overlords ruining the empire the heroic social media crew built. "But this is something Duolingo can't cute-post its way out of," Fast Company wrote on Tuesday, complaining the company "has not yet meaningfully addressed the policies that inspired the backlash against it... " So the next video (Thursday) featured Duolingo CEO Luis von Ahn himself, being confronted by that same hoodie-wearing social media rebel, who says "I'm making the man who caused this mess accountable for his behavior. I'm demanding answers from the CEO..." [Though the video carefully sidesteps the issue of replacing contractors with AI or how "headcount will only be given if a team cannot automate more of their work."] Rebel: First question. So are there going to be any humans left at this company? CEO: Our employees are what make Duolingo so amazing. Our app is so great because our employees made it... So we're going to continue having employees, and not only that, we're actually going to be hiring more employees. Rebel: How do we know that these aren't just empty promises? As long as you're in charge, we could still be shuffled out once the media fire dies down. And we all know that in terms of automation, CEOs should be the first to go. CEO: AI is a fundamental shift. It's going to change how we all do work — including me. And honestly, I don't really know what's going to happen. But I want us, as a company, to have our workforce prepared by really knowing how to use AI so that we can be more efficient with it. Rebel: Learning a foreign language is literally about human connection. How is that even possible with AI-first? CEO: Yes, language is about human connection, and it's about people. And this is the thing about AI. AI will allow us to reach more people, and to teach more people. I mean for example, it took us about 10 years to develop the first 100 courses on Duolingo, and now in under a year, with the help of AI and of course with humans reviewing all the work, we were able to release another 100 courses in less than a year. Rebel: So do you regret posting this memo on LinkedIn. CEO: Honestly, I think I messed up sending that email. What we're trying to do is empower our own employees to be able to achieve more and be able to have way more content to teach better and reach more people all with the help of AI. Returning to where it all started, Duolingo's CEO posted again on LinkedIn Thursday with "more context" for his vision. It still emphasizes the company's employees while sidestepping contractors replaced by AI. But it puts a positive spin on how "headcount will only be given if a team cannot automate more of their work." I've always encouraged our team to embrace new technology (that's why we originally built for mobile instead of desktop), and we are taking that same approach with AI. By understanding the capabilities and limitations of AI now, we can stay ahead of it and remain in control of our own product and our mission. To be clear: I do not see AI as replacing what our employees do (we are in fact continuing to hire at the same speed as before). I see it as a tool to accelerate what we do, at the same or better level of quality. And the sooner we learn how to use it, and use it responsibly, the better off we will be in the long run. My goal is for Duos to feel empowered and prepared to use this technology. No one is expected to navigate this shift alone. We're developing workshops and advisory councils, and carving out dedicated experimentation time to help all our teams learn and adapt. People work at Duolingo because they want to solve big problems to improve education, and the people who work here are what make Duolingo successful. Our mission isn't changing, but the tools we use to build new things will change. I remain committed to leading Duolingo in a way that is consistent with our mission to develop the best education in the world and make it universally available. "The backlash to Duolingo is the latest evidence that 'AI-first' tends to be a concept with much more appeal to investors and managers than most regular people," notes Fortune: And it's not hard to see why. Generative AI is often trained on reams of content that may have been illegally accessed; much of its output is bizarre or incorrect; and some leaders in the field are opposed to regulations on the technology. But outside particular niches in entry-level white-collar work, AI's productivity gains have yet to materialize. Read more of this story at Slashdot.

X is back up for most users after what appeared to be a significant outage that spiked early this morning around 9AM ET. Global internet monitor NetBlocks posted this morning that X “has been experiencing international outages for some users for a second time in a week,” adding that the issue isn’t “related to country-level internet disruptions or filtering.” Downdetector showed complaints of an outage rose sharply after 8AM ET before beginning to fall about 45 minutes later. As of this writing, those reports have dropped from tens of thousands of reports to only hundreds. Elsewhere on social media, people had been complaining about issues with the network for a couple of days — one of the top posts on the r/Twitter Reddit community is a screenshot of an error message on the X login screen, with many replies complaining about login issues. The X developer platform site’s incident history log shows that a sitewide outage started Thursday and lasted roughly a day. At the moment, there’s still an error message on the platform status page that reads, “Login with X (OAuth) and other X platform login flows are experiencing degraded performance.” The issue comes after a fire reportedly broke out in an Oregon data center owned by X on Thursday morning. Wired writes that multiple unnamed sources told it that the fire, which forced “an extended response from emergency crews,” involved batteries in one of the data center’s rooms. Following reports of the fire, there were complaints that X was down, but the outage then seemed comparatively small. X did not immediately respond to The Verge’s request for comment. Update May 24th: Outage reports have mostly subsided.

X, the microblogging platform formerly known as Twitter, is currently down in several regions, including India. The X website and apps on iOS and Android smartphones are displaying an error when attempting to load posts or videos on Saturday. Users have also taken to a popular downtime tracking website to report that X is currently inaccessible. The X developer platform indicates that the company resolved a site-wide outage a couple of days ago, but some login related flows are experiencing degraded performance.X Down for Thousands of Users, Site and App Not WorkingAround 6pm on Saturday, users on downtime tracking website Downdetector.com began to report that the X website and app were not working. At the time of publishing this story, there were nearly 25,950 reports on the website, with a sharp spike at around 6pm. 69 percent of these reports state that the X app is not working, while 23 percent are related to the website being inaccessible and 8 percent of users have reported issues connecting to X servers.Various parts of the X website and mobile apps are not displaying any contentPhoto Credit: Screenshot/ X.comMeanwhile, the India-focused Downdetector.in website shows that users in the country are also reporting issues with accessing the X.com website and mobile apps. Over 2,200 users are currently unable to access the service, as per the website. Forty-six percent of these reports are related to the app being inaccessible, while 38 percent are related to login issues, and 18 percent of users said they were unable to access the website.Gadgets 360 staff members were unable to load posts on X at the time of publishing, and the X website displayed an error while trying to load new posts. Users are presented with the text "Something went wrong. Try reloading." along with a Retry button, which is currently not functional. Users on the app are seeing a message that says "Posts aren't loading right now."[Story is being updated. Please refresh for the latest updates.]