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Carlton Davis' Hamstring Injury Exposes Patriots' Secondary Depth Concerns
The first minor warning sign of New England Patriots training camp 2026 has been illuminated. It is hardly a call for panic but ESPN's Mike Reiss noted on "X," late Friday afternoon that the team had brought in a pair of free agent defensive backs, Eric Butler and Tysheem Johnson, for tryouts. While this practice is commonplace from training camp through the regular season, the fact that a pair of defensive backs were in make the visits extremely timely.
Abdul Carter’s extremely bold goal will catch Giants fans’ attention
New York Giants LB Abdul Carter sets an extremely bold goal that is sure to catch fans' attention ahead of the 2026 NFL season.
‘Extremely violating’: Tinder pauses AI photo enhancement feature
"It was extremely violating," Rubin said. "Those are not my teeth. That's not what my mouth looks like."
Sandra Oh Names 'Extremely Rare' Gift She Got During Her 'Grey's Anatomy' Days
“I was really very fortunate,” she said.
The Science Behind the AI Panic Is Shakier Than You Think
A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
Rabid bat found near zoo restaurant sparks urgent public health warning: 'Extremely serious'
A dead wild bat found near a San Diego Zoo Safari Park restaurant tested positive for rabies, marking the 19th such case in the county in 2026, said experts.
Small business employment growing faster in New England than nationwide
It is no surprise that small businesses are extremely important when it comes to job growth, and a recently released Federal Reserve Bank of Boston report not only confirms that but says that small business job growth in New England is outpacing the rest of the country. In New England, the report says small businesses […]
‘Extremely frustrating:’ Alpharetta businesses fed up with years-long Highway 9 construction project
The project includes widening Highway 9 to four lanes.
Obviously, Bethesda Is Remastering 'Fallout: New Vegas' and 'Fallout 3'
The remasters have been an extremely poorly kept secret for months, but now, they're official.
Reclusive comedian Steven Wright, 70, resurfaces for extremely rare appearance as fans all say the same thing
The reclusive comedian, Steven Wright, made a rare appearance on Howie Mandel's podcast, Howie Mandel Does Stuff on Tuesday, July 14
KFF Health Tracking Poll: Public Views on Fraud in Government Health Programs
As the 2026 midterms approach, health care costs remain voters' top health priority, but fraud in government health programs, including Medicaid and Medicare, is resonating with Republican voters, 55% of whom say it's extremely important for candidates to address. Most voters say there is at least “some” fraud in government health programs, but larger shares say there is fraud in the tax system, defense, and foreign aid, while the smallest share sees fraud in the ACA Marketplaces.
Poll: Costs Are the Top Health Care Issue for Voters in the Midterms, But Fraud Tops Republicans' List As Trump Administration Pushes Crackdown
Health care costs top the list of voters’ health care priorities for the midterm elections, though more than half of Republican voters say it is extremely important for candidates to discuss the issue of fraud in government health programs, according to a new KFF Health Tracking Poll. Most voters say there is at least “some” fraud in government health programs, but larger shares of voters see fraud in the tax system, defense, and foreign aid than in federal health programs. The smallest share of voters sees fraud in the ACA marketplaces.
Canada Wildfire Air Pollution and Heat Wave Combo Pose Heightened Health Risks
Days that are both extremely hot and polluted come with higher risks of respiratory ailments and other health hazards.
The States Try to Topple WarnerMount…Thus Begins the Local Antitrust Era
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) Well, it looks like it’s time for another strategy column. Can I finally avoid writing about M&A? No, it seems I cannot. Admittedly, I’m a week early with this column, since I usually put it out every other week. But this news couldn’t wait. This will make three “Most Important Stories” in a row with a merger or acquisition as its centerpiece. First, Roku-Fox (an acquisition), then Comcast-NBCUniversal (a divestment!), and now the State AGs and the WGA’s lawsuits against Paramount’s pursuit of Warner Bros. Discovery (antitrust). M&A is usually the “most important” topic any given week, simply because a giant company merging or breaking up (or preventing those outcomes) can have the biggest impact on the streaming board. If you think about these companies as players in a game, changing the number of players is one of the biggest changes you can make. I shouldn’t really complain, either, since this lawsuit is very good news for customers and suppliers (econ speak for talent, workers and indie studios in Hollywood). So let me answer some questions about this potential trial, along with (shudder) making some heavily caveated predictions. I’ll also look at whether Hollywood must consolidate, whether or not the states can win, trouble at X-Box, Netflix’s YouTube binge, and more. Plus, I have five images that visualize this merger and the harm, including three brand new data looks you’ve never seen before. Subscribe Most Important Story of the Week - Can The States and the WGA Stop Hollywood’s Consolidation (Crisis)? If every other newsletter and news outlet is writing about the same story, I try to avoid writing about it as well, but I can’t ignore a story this consequential. The story of the last year was the fate of Warner Bros. Discovery, and this development potentially extends that saga into 2027. As I tried to figure out my angle, I realized, like my last column, I’m again faced with a series of questions. These questions, though, are much more forward-looking. Will the states win? Will the WGA win? If so, what happens to Warner Bros. Discovery? Either way, will this chill the market for future mergers? So you know what that means: predictions. About the future, no less, the hardest thing to predict.1 Just remember: nothing here comes with a guarantee. And the more confident someone is in their predictions, the less you should trust them. This situation is incredibly fluid, nuanced, uncertain and tenuous. So let’s try to explain what may happen. Question: Why File this Lawsuit? I’ll admit, a few months back, I was fairly pessimistic about the fate of this lawsuit. Rumors had started bubbling up that the state Attorneys General (in particular California) wanted to settle with either some minor divestments (usually CNN) or behavioral remedies. And that tracked for me: the states usually settle lawsuits, like the recent John Deere right-to-repair suit. Yet, they plan to fight this. (As they did the LiveNation-Ticketmaster monopolization trial and the Nexstar-Tegna merger.) Honestly, that also makes a ton of sense. Frankly, this move is just good politics for these State AGs. Most are still running for higher and higher office in blue states—some folks have emphasized that only Democratic AGs joined this suit, but given the Ellison/Trump ties, and fears of retribution, that tracks—and a high profile case like this will help them move up the electoral ladder, especially since Paramount and its leadership (the Ellisons, father and heir) have sided with the other side of the political aisle. That makes them unsympathetic to Democratic voters and Hollywood workers in particular. Why did this lawsuit happen? In addition to the very real concerns about industry consolidation, this is why. That said, I don’t think we would have seen this lawsuit for Netflix and Warner Bros., simply because they’re more sympathetic and aligned with Democrats. But those consolidation concerns are real. The “town”—meaning Hollywood and such—really is seeing the damage of mergers on this industry. That’s why the WGA also filed its own lawsuit in defense of workers as well. (Wildcard: What about Europe? Will they try to stop the deal, too? There have been some rumors out of the UK they may try to fight the deal, and UK enforcers just stopped a Getty Images-Shutterstock tie up, so there is precedence. This argument could go either way, though. On one hand, the EU doesn’t need to step in, cause they states are handling it. On the other hand, this could make the EU’s case easier. Just imagine if both sides coordinated. I’ll simply say, “We’ll see!”) Question: Will The State AGs or the WGA Win? Can the state AGs play David to Paramount-Skydance’s Goliath here? That’s really the whole thing, isn’t it? If you know this result, you could make a lot of money. When things are uncertain, it’s tempting to shrug and say, “Eh” meaning it’s a 50/50 proposition. I wouldn’t, though. When in doubt, you should expect that the merging parties succeed. AT&T did successfully buy Warner Bros., after all, and Disney bought Fox without any challenges. Many judges are extremely sympathetic to mergers, and I’d bet the state AGs get a judge who will allow the merger. (Even Obama-appointed judges often allow mergers.) As I understand it, we’ll see two main attacks. First, theatrical films. The AGs are arguing that, when it comes to share of box office and the number of both “wide releases” and blockbuster (or “tentpole”) films, the merging parties will have over 27% market share. Now, the Supreme Court once ruled from on high that any merger that exceeds 30% is presumptively illegal, but that’s guidance, not an ironclad bar. Still, the states will have to argue that this is close to having too much power, and with that power, this will likely further worsen the theater/studio splits in particular. Indeed, as Matthew Stoller pointed out, “just five studios are responsible for 95% of all blockbusters; if this merger goes through, then Paramount and Disney will control 60% of that.” So yes, movie studios will be very concentrated after this merger. Want me to visualize some of that? Well, here’s the share of films that grossed over $200 million at the domestic box office since 2022. (Note: this isn’t all films released in over 3,000 screens, but it is a good look at market power.) First, here are the 43 films by studio: Notice all the blue (Disney), orange (Universal), and purple (Warner Bros)? Yeah, those studios are powerful, and this combines Paramount with one of them. Here’s the look by studio and share of the films that grossed over $200 million: Now, if you look at that and also think, “Huh, should we break up Disney?” I’m with you. I’ve seen some folks poo-poo this idea that this is a real category, but it is! For an upcoming article, I’ve been pulling data on the share of top 100 films at the box office as a share of total box office, and it’s massive. Here’s the chart: (This analysis looks at The-Number’s top 100 films and total domestic box office each year.) To be clear, tentpoles make up 80.7% of the box office each year, so I do think it’s a genuine market. (To be clear, films grossing over $50 million make up 80% of the domestic box office, and films grossing over $100 million make up 64.1%.) I mean, the top 100 films at the box office make up 95% of each year’s grosses. Worse, the combined studios will likely cut output to flex that market power. Everyone can just look at what happened to Disney and Fox to see the precedent. (With fewer films, the remaining theaters have to give more for those few films that remain.) Here’s an image I’ve used a lot, the share of wide-release films from Disney and Fox over time: The AGs will also argue that this consolidation will also impact the cable industry. In this case, the consolidation is actually worse, but that environment is…complicated. Paramount will argue that the cable industry is shrinking. That said, predictions that cable will “go to zero” also don’t look very well supported by the data, as the declines are shrinking, meaning “cable” or “vMVPDs” will likely stabilize at some point. Can the state Attorneys General win on those two main arguments? I honestly don’t know. Again, I’d bet no, since history in general supports that, but it depends on the judge/jury and how they interpret the arguments. Notably, Paramount won’t face accusations about monopoly power in other areas, like streaming, news, sports rights, or vertical integration. I tend to disagree with leaving out sports rights, but I also see the appeal for a simpler case. The states also aren’t really pushing novel theories about labor harm either, which should help. (By the way, Sean McNulty at The Ankler wrote a great run through of potential options, and Capitol Forum also wrote a great paper on theatrical consolidation. Both these pieces predated the lawsuit. Matthew Stoller is also out with his analysis of the pros and cons of the case too.) In some ways, the WGA’s case is even stronger. Quoting Sean McNulty’s summary: “The WGA is citing WARNAMOUNT as crossing a 30% market share in multiple sectors, a number that the Supreme Court has precedent for deeming “presumptively anticompetitive” in a market. According to the WGA numbers, WARNAMOUNT would have represented: - 35% of film writing jobs between 2021 and 2024 - 36% of TV writing projects between 2022 and 2025 - 38% of overall deals between 2021 and 2024” Notably, antitrust enforcers did win recently in the Penguin-Simon & Schuster merger by focusing on labor impact, so don’t count this thinking out. (Wildcard: Will the states try to get a jury trial? Most likely no, they’ll let a judge handle it. If I’m them, though, I’d try to put this in front of a jury. Juries tend to understand monopoly power in a more realistic way than judges faced with legions of economists.) Question: What if They Win? What if They Lose?
'House of the Dragon' Season 3, episode 4: The season's dogs are here
Extremely good Vale pups.
Obsidian game director reacts to people "spouting off" about the studio and how it's not what it "used to be"
A director at Obsidian shared their thoughts about the "extremely difficult week" at the studio.
Mary Steenburgen says ‘Book Club’ cast are her closest friends, they miss Diane Keaton
‘Book Club’ stars Mary Steenburgen, Candice Bergen, and Jane Fonda are extremely close and will be ‘missing’ Diane Keaton when they soon reunite.
Nick Fuentes to Hasan Piker: click by click, antisemitism goes mainstream
As attacks on Jews rise, USA TODAY's Extremely Normal explores how mainstream politicians and commentators are embracing antisemitism.
Quantum computing wielded to create extremely rare material critical to nuclear fusion
Nuclear fusion inches closer after scientists combine supercomputing, AI and quantum computing to blueprint a way to create more tritium.
Up to 100 gigawatts: China unveils its high-power microwave weapon arsenal
Defence scientists say the technology could threaten low Earth orbit satellite networks such as Starlink at an extremely low cost.
NJBIZ Honors Two Outstanding Programs and Three Healthcare Heroes from RWJBarnabas Health
Two extremely impactful programs and three dedicated health care team members from RWJBarnabas Health were recognized by NJBIZ as 2026 Healthcare Heroes, and were honored at a formal event on June 23 at The Marigold in Somerset.
Midjourney founder says AI coding leaves friends 'extremely drained'
Midjourney founder David Holz said the phenomenon "makes me feel like something is wrong, and also that there might be a big opportunity."
Monmouth Cancels Second Straight Card Due To Heat
Monmouth Park has canceled live racing for Saturday, July 4, due to excessive heat. The day's scheduled races will appear as extras for the card on Friday, July 10. The Oceanport, N.J. track also canceled its Friday, July 3 card because of the extremely hot weather. Temperatures for Saturday are ...
Bowles Extremely Impressed With 1 Quality of Rueben Bain Jr.
Defying concerns over his physical reach, the rookie edge rusher is wielding veteran-level intelligence to ignite Tampa Bay’s pass rush and solve a long-standing defensive drought.
GTA 6 Could Skip 60 FPS Entirely, as Rockstar’s Insane Detail Forces a 30 FPS Lock Even on PS5 Pro
With GTA 6, Rockstar Games is set to raise the bar for what is possible to achieve on PlayStation 5 and Xbox Series X|S much like the studio did with Red Dead Redemption 2, but this is likely coming at a price. Although the highly anticipated next entry in the series is rumored to feature a performance mode running at 60 FPS, it is extremely unlikely this is real, as, according to the tech experts at Digital Foundry, the level of detail shown in the latest footage would make it very difficult to scale everything down to allow for 60 […]
A $1000 PlayStation 6 No Longer Sounds Crazy, As Sony Doesn’t Intend To Sell Hardware At Significant Losses
The PlayStation 6 is set to launch at one of the most challenging times for consumer tech, if not the most challenging. With the rising prices of components, RAM and SSDs above all, manufacturers are having an extremely hard time keeping costs down, and with a bill of materials that is now close to $1000, there's a very high chance the system will cost way more than most anticipated. Adding fuel to the fires of speculation is Sony itself, as the company heavily suggested in a recent investor Q&A session that it won't heavily subsidize the system. "We regard hardware as the base […]
Platner Has Slight Edge Over Collins in Tight Maine Senate Race, Poll Finds
The Maine race, which both parties see as key to winning control of the Senate, is extremely close as the sprint to fall begins, a Times/Portland Press Herald/Siena poll found.
Scientists Publish Extremely Serious Research About Whether Tickling Apes Makes Them Giggle
Scientists tickled apes and examined their laughs to gain some very serious insights into primate evolution.
New York Knicks Champion Mitchell Robinson Makes Heartfelt Post
Mitchell Robinson is coming off his eighth season in the NBA.The 28-year-old has become an extremely valuable player for the New York Knicks in that span.He is coming off a season where he averaged 5.7 points, 8.8 rebounds and 1.2 blocks per contest while shooting 72.3% from the field in 60 games.Knicks Champion Robinson Makes […] The post New York Knicks Champion Mitchell Robinson Makes Heartfelt Post appeared first on HEAVY.
Colby Parkinson is extremely valuable to the Rams offense
Analysts around the league are clamoring for him, but they’re almost certainly not going to pry him away from L.A.
The Extremely Online Senate Race Testing Democrats’ Midterm Strategy
In 2026, winning an election might mean winning the most attention online.
This Democratic congresswoman almost helped elect a Republican speaker
Rep. Marcy Kaptur (D-Ohio) explains why she contemplated supporting Rep. Kevin McCarthy (R-California) in 2023 with an extremely rare crossover vote.
Serious statin side effects on muscles are extremely rare, new research confirms
Scientists created a new tool to help doctors predict a patient’s risk of severe muscle problems from taking a statin.
Trump describes Burnham as 'the mayor of a town' and 'extremely liberal'
Donald Trump gives his first public reaction to the prospect of Andy Burnham becoming prime minister.
🏀 Y! Sports AM: Draft night
The Wizards took AJ Dybantsa at the top of an extremely loaded NBA Draft.
Scheffler career Slam chances blowing in Shinnecock winds
Scottie Scheffler's chances of winning the US Open to complete a career Grand Slam might just come down to how the fickle winds of Shinnecock blow this weekend.Playing in the wind is extremely difficult and judging speed on putts when the wind is blowing that hard can be challenging," Scheffler said.
Border Report Live: New World screwworm adding to overburdened, under-funded US health system
Although the chance of New World screwworm infecting humans is extremely rare, Dr. Tyler Evans, CEO and co-founder of the Wellness and Equity Alliance tells Border Report Live that the threat is ad…
Latvia pilots citizen engagement in research grant evaluations
Latvia has introduced an element of citizen engagement to the evaluation of nationally funded research grants. The aim is to both create more connections between science and society, and to improve the quality of its evaluations. The move comes as research funders across Europe are experimenting with ways to improve evaluation processes in the face of a sharpened focus on science’s impact on society. Evaluators of grant applications submitted to Latvia’s Fundamental and Applied Research Programme (FLPP), the country’s main research funder, will this year have the option of consulting citizen feedback on challenges the public thinks science should focus on to inform their decisions. “In recent years, both public discussions and policy-level debates in Latvia have highlighted the importance of demonstrating how publicly funded research contributes to society, the economy and the resolution of real-world challenges,” said a source at the Latvian Council of Science (LCS), which manages the FLPP. “At the same time, research institutions themselves expressed interest in improving the project evaluation framework while continuing to ensure that funding is awarded to the highest-quality projects,” the source added. The citizen input comes from a survey that was conducted between 25 February and 16 March 2025, to which 1,737 people responded. It gathered information on what the public views as problem areas for Latvia, and the role of science and technology in providing solutions to these problems. A summary of these responses has been included as an annex to the FLPP 2026 call for proposals that evaluators can refer to, purely in an advisory manner, when judging proposals. Results from the survey show that the main problem areas identified by the public were in healthcare and public health, followed by the development of new treatment methods and medicines, and then digital technology, data security and cyber security. At the bottom of the list was research aimed at acquiring new knowledge about the universe, matter and the laws of nature. Further analysis looked at the public view of the possible role of science in solving these problems. The survey charted 4,167 individual responses based on open-ended questions that were derived from the participants’ answers. The analysis again found that medical and health sciences topped the scoring, with 788 responses referring to the field. Humanities and the arts were bottom of the list, with 625. Digging further into the details, the survey suggests citizens are aware of and concerned about a number of research areas. This includes technologies and innovation to improve the environment or reduce pollution, the development of modern military technologies and cybersecurity tools, and the development and application of artificial intelligence, among others. “We hope this initiative will help strengthen public trust in science and create a more meaningful dialogue between researchers and society,” the LCS source said, adding that it offers benefits to researchers, evaluators and to the general public. More space for citizen engagement in evaluationsThere is a gradual but increasing use of citizen engagement in research evaluation processes around Europe, especially in healthcare, but it remains relatively rare, said Henry Sauermann, a professor at the European School of Management and Technology in Berlin and an expert in citizen science.“Given that we are at a point where citizen involvement is extremely limited, there is presumably space for more, but it's not that we should do it just because we want to do it,” he told Science|Business. He said some research lends itself better to having citizen input, and this usually tends to be on the applied end of the science spectrum where the public can offer useful feedback based on their experiences. He added that advising scientists on certain topics to study is also a useful approach, in the way that Latvia is doing. “The public does fund a lot of science, there's a lot of tax money going into this. We of course do want to preserve academic freedom, but it's also not unreasonable to incorporate more feedback or input from citizens where possible,” he said. Related articlesCitizen science: where, when, and how to let the public contribute to researchHow can Europe cope with a flood of AI-assisted research grant proposals?Data Corner: Horizon Europe’s AI, digital and data call success rate is 5.2%There are several ways of involving citizens in science funding. The most minimal way is having a few representatives on a panel, such as members of a patient group or union. The most direct way is crowdsourcing funding for specific projects. There is an increasing number of online platforms that allow the public to chip in to support scientific proposals.Sauermann studied this in a recent paper and found that it can have its uses, but it still is not clear if there are benefits to it. There are various assumptions linked to citizen engagement, for example that it increases public trust in science, or that it provides a representative view of what the public thinks about science. These are not necessarily true, Sauermann found. People who participate in engagement activities tend to already be very trusting of science, and, on the whole, it is generally the well-off and well educated who contribute, giving a narrow view of public opinion. On whether citizen input can help manage the increasing number of proposals that are being submitted due to assistance from AI, Sauermann is more sceptical. “The obvious answer to the flood of AI-written proposals is AI-driven evaluation,” he said, adding that it doesn’t mean citizen engagement shouldn’t be a part of evaluations in a well thought-out manner. Southern Denmark’s successful approachOne successful example of citizen engagement in science evaluations was implemented in southern Denmark. Between 2019 and 2025, citizens in the region were invited to vote to fund one of five pre-selected projects in the field of healthcare. The project, called A Healthier Southern Denmark, was a collaboration between regional authorities, the University of Southern Denmark, and two media partners. Over the seven years, there were 75,000 votes and significant public engagement online and on social media. “It was a bit of a wild project, we’d never done something like that before and we were a bit nervous about what would be said in the media about it. But actually, people were very positive,” said Anne Kathrine Overgaard, one of the main organisers of the project from the University of Southern Denmark. The university and its researchers have learned a lot from the experience, particularly about engaging with society and shaping their research to match real-world needs. This is important with Europe’s increasing focus on competitiveness and bringing innovations to market faster, Overgaard said. She hopes to see more citizen engagement in evaluation processes and in science more broadly. “For many years, citizen engagement has been a bit of an add-on to proposals, just to check a box. But I think things are changing,” she said. It’s less about citizens just gathering data and more “about the dialogue between the public and the scientists,” she said. The initiative ended last year as the team wanted to regroup to find fresh momentum. The development of AI and AI-written proposals has made Overgaard reflect on how the project could be reworked to help evaluators. She hopes to bring it back in the future. “It could be a good idea to try this initiative again in a slightly re-worked format,” she said.
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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
California’s sluggish vote counting ripped across the political spectrum: 'Extremely embarrassing'
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How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
Intel warns of "crazy" price: Arc G3 Extreme tramples AMD Ryzen Z2 Extreme for expensive price
Intel has detailed the performance and power efficiency of its upcoming Arc G3 Extreme SoC. Designed for handheld gaming consoles, the Intel Arc G3 and Arc G3 Extreme rely on Intel's latest Panther Lake CPU architecture and feature 10 and 12 Xe-core iGPUs, respectively. However, the consoles powered by these chips could be extremely expensive.
Towards a responsive health system and equitable access to quality RMNCAH services
Indonesia’s vast geography and sociocultural diversity shape the delivery of reproductive, maternal, newborn, child and adolescent health (RMNCAH) services in ways that are both enabling and challenging. Across provinces, significant variation has been observed in how services are planned, resourced, and delivered. These variations in service standards and quality are felt daily in the lives of women, newborns and adolescents. Despite these challenges, progress is visible. National estimates show a drop in maternal mortality ratio, from 331 to 140 per 100 000 live births since 2000, signaling improvements in care quality and access. Fertility has stabilised and contraceptive use has grown, yet unmet reproductive health needs persist, and adolescent pregnancies continue to reflect early marriage and limited access to youth-friendly services.To better understand how Indonesia’s health system has evolved under decentralisation, the Ministry of Health, supported by World Health Organization (WHO), United Nations Population Fund (UNFPA), the United Nations Children’s Fund (UNICEF), IPAS and the Asian Development Bank (ADB), conducted a joint external review of RMNCAH programmes. This review draws on multiple sources of evidence, including existing literature, policy documents, national health system data, as well as field visits to six selected provinces to represent Indonesia’s geographic and administrative diversity. During the field visits, review teams observed service delivery in health facilities, spoke with health workers and local leaders and met mothers and families to understand how RMNCAH policies are operationalised. One of the challenges identified was the management of low birthweight (LBW) infants, a major contributor to neonatal deaths, under resource constraints. One midwife described a severe case of extremely LBW twins, one stillborn and the other died after three days of care. She recalled how the surviving infant struggled to swallow and may not have received breast milk, while simultaneous emergencies required the team’s attention, affecting the care provided to the infant.A subsequent review of the case by the district health office revealed insufficient antenatal care, fragmented maternal medical history due to population mobility, referral delays, limited provider capacity in neonatal intravenous access and a heavy workload that constrained proper newborn monitoring. These insights informed recommendations to strengthen both system-wide and service-level responses. Since the early 2000s, Indonesia’s decentralisation initiative has given much of the responsibility for funding, staffing and managing health services to regional governments. While the Ministry of Health sets national clinical standards, daily management of public health facilities falls under regional authorities through the Ministry of Internal Affairs. While this arrangement allows services to be tailored to local needs, it also depends heavily on local capacity for planning, budgeting and management. Health facilities and district health offices are better positioned to translate national priorities into local action when they have access to consistent technical support, helping health workers manage resource constraints and infrastructure gaps.The complexity of Indonesia’s health system governance is no easy feat to navigate. Understanding these dynamics is essential to strengthening policy, improving coordination and ensuring more equitable access to services across Indonesia. Under the leadership of Ministry of Health, recommendations from this exercise will be key in guiding the nation’s roadmap to an equitable and quality health system for RMNCAH.Written by Edit Oktavia Manuama, National Professional Officer (Maternal, Newborn, and Child Health), and Jessie Yunus, National Professional Officer (Gender, Rights, Equity, and Disability Inclusion)
MSU Benefitted From Recent Legislative Session
The recently-completed 2026 regular session of the Mississippi Legislature saw encouraging gains for Mississippi State University, the state’s leading research university, as lawmakers invested in the land-grant institution’s core missions and in the university’s world-class faculty and staff. “While the bulk of our operations saw level funding, I’m extremely pleased that the Mississippi Legislature and […]
Blue Moon 2026: An extremely rare micromoon rises tonight
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Stunning 150-million-year-old stegosaur skull rewrites dinosaur evolution
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Locking up key contributor named Eagles' most underrated offseason move
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Trump's Truth Social lays bare narrow obsessions of an extremely online president
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The night sky could get three times brighter as new satellites launch — all but ruining the Vera C. Rubin Observatory's survey of the universe
Extremely bright satellites and megaconstellations could make the night sky up to three times brighter than it is now, a new study warns. This would seriously hinder astronomical imaging devices, like the enormous camera at the Vera C. Rubin Observatory.
Caleb Downs Is the Cowboys’ Best Pick in Years
Caleb Downs very clearly has “it.” It’s extremely premature to make a claim as bold as “best in a decade” before a draft pick touches the field in training camp, let alone at game speed. However, some people have “it”—an aura people can see on film and in real game time. Every draft has a […] The post Caleb Downs Is the Cowboys’ Best Pick in Years appeared first on The Lead.
Trump keeps saying the quiet part out loud on changes to the 2026 election
President Donald Trump’s latest gerrymandering push is an extremely desperate partisan power grab that would invalidate votes already cast.
A New Jersey Republican hasn’t voted in weeks, sparking questions over lengthy absence on Capitol Hill
Republican Rep. Tom Kean Jr. of New Jersey has not voted in Congress in weeks, sparking questions over when he will return to Capitol Hill as he faces a competitive race for reelection back home and Speaker Mike Johnson oversees an extremely narrow House majority in Washington.
Viewpoint: A career in science is possible, and worth it
Starting a career in science is exciting, but also demanding, with researchers often faced with intense competition, long working hours and high levels of stress. A pioneer in the field of mass spectrometry, Carol Robinson has been there. She speaks with Science|Business on the importance of creating a supportive environment, particularly for women.Robinson did not wake up one day thinking that she would become a scientist. It was only late in school that she developed an interest in chemistry. “The more you work at something, the more you think, actually, this is fascinating. So, I went much further and deeper than I ever needed to pass the exam. And then I loved it,” she says. “I thought, I’m going to give it my all. I’m going to enjoy it while I’m here. But I didn’t think this would be my career for 50 odd years.”She left school at 16 to become a lab technician with the drug company Pfizer, which is where she first worked with mass spectrometry. Fascinated, she was encouraged to take a part-time degree and then a PhD. She went on to do pioneering work in native mass spectrometry, a technique that is used to study protein structures in their natural state, paving the way for breakthroughs in drug discovery. She also broke new ground by becoming the first female professor of chemistry at the University of Cambridge, and then the first female professor of chemistry at the University of Oxford, her current home.She now wants to show young researchers that a career in science is worth it. “Sometimes, I believe the culture isn’t very encouraging, and that’s something I have worked on,” Robinson says.In 2021, she launched the Kavli Institute for Nanoscience Discovery at Oxford, whose acronym gives a sense of what she hopes it will achieve. “Maybe it is corny,” she says. “It’s just a reminder that I would like it if we could all be kind to each other and supportive, especially for early-career researchers of either gender, because there’s quite a big attrition rate in science, and maybe it just doesn’t feel like a very friendly place to work.”And yet, a career in science comes with a lot of flexibility. “There are few careers where you have that amount of freedom, to go to conferences, to the library. Just because you didn’t see me didn’t mean I wasn’t working,” she says. “And I’m excited to come in every day. So, I don’t want people to think it’s a terrible place.”Gender equality is also important when it comes to the quality of science as a career. “The more women we have, the better it becomes,” Robinson adds.Imposter syndromeAccording to EU data, women outnumber men in university education but account for just about a third of graduates in science, technology, engineering and mathematics (Stem). And while the number of women working as scientists and engineers in the 27-member bloc is on the rise, their representation remains below that of men, at 41%. This discrepancy is even more pronounced in top-tier functions across these fields, where women fill just 20% of positions.Having supervised many young scientists, Robinson sees that women continue to have a greater propensity than men to doubt their abilities, a feeling she once shared. “When I was doing my PhD at the University at Cambridge, I really felt like an imposter,” she says. “I don’t think I ever felt I deserved to be there or owned it and was proud. I just thought I was lucky.” Later on, she adds, “I was always that person who said, I can’t apply yet because my CV is not strong enough.”She has now made it her job to encourage women to break with this imposter syndrome. For example, rather than calling for volunteers to give a lecture in the groups that she mentors or teaches, which nearly always results in men stepping forward, she goes straight to the women, “because I know they won’t push themselves forward.”But the gender gap in science is not just down to imposter syndrome. The “obvious” reason, Robinson says, and the reason that is on everybody’s mind when discussing workplace gender equality, is the balance between work and raising a family. Her own path was to choose between the two, taking an eight-year break after completing a postdoctoral training fellowship to bring up three children. And she continues to advise women not to work part-time while they raise a family. “You’ll just get half the pay and continue to work full-time because you are not able to fit everything in,” she says. “You should be able to have a family and come back to have the career that you want.”The solution is for men to also take career breaks. “That kind of evens it up, so that it’s not such a big thing for women,” Robinson says. “I always encourage men in my groups to do that. I think it’s important to take your share of the duties.”Support produces resultsAccording to the Women in Stem Network, a UK advocacy group, countries with strong childcare systems and equal pay policies retain more women in technical careers. In Europe, these are primarily found in the Nordic region and parts of central Europe.At the EU level, the European Commission asks all Horizon Europe applicant organisations to have a gender action plan in place. Since this criterion was introduced in 2021, the share of women leading consortia has increased to 31%, against 23% in Horizon 2020. Their overall participation, however, has risen just 1%, to 38%. After trying to weaken the requirement for gender action plans in 2025, the Commission now intends to adopt a plan for supporting women in research and innovation.Related articlesCommission scraps plan to water down gender requirements in HorizonHow gender balance can swing who wins Horizon Europe fundingRobinson herself has benefited from three Advanced Grants from the European Research Council. “I was extremely honoured and privileged to get those, because they really allowed me to fuel my passion,” she says. “Without that, it would have been a much harder slog.”Beyond applying gender rules, she says that more work is needed on translational enterprise, where women are, once again, largely underrepresented. The European Patent Office found that only 13.8% of applications were filed by women in 2022. “We have so many more women doing amazing science; why do they feel it’s not a thing to have a company?” she asks.She founded her own spin-out company, OMass Technologies, in 2016, and remains chair of its scientific advisory board now that it trades as OMass Therapeutics. As for research, Robinson is now studying how changes in the brain may be linked to depression, using port-mortem tissue. “We’re hoping to publish soon,” she says. “We had to consult with a lot of people who knew a lot more about the brain than we did. So, we’ve learnt a lot, and that’s always a lot of fun to learn a new thing. That’s what keeps you going.”
James Harden says Cavs 'extremely confident' after losses to Raptors
James Harden considers the Cavaliers' problems in Game 3 and 4 losses to the Toronto Raptors as "self-inflicted and things that we can control."
Texas' Top Criminal Court Has Stymied the State's 'Junk Science' Law. Will SCOTUS Intervene?
If the U.S. Supreme Court renders a decision in favor of Charles Flores and others, it would likely force the Court of Criminal Appeals to relax its extremely strict handling of appeals based on shoddy and outdated science.
Why Doesn't Star Trek Ever Leave The Milky Way Galaxy?
Throughout the many adventures of Star Trek, it's extremely rare that the Enterprise (or any other ship) leaves the Milky Way galaxy. But why is that?
Pokemon Z development build found, footage released
Brand new footage of Pokemon Z – which was never released – has now been shared. It goes without saying, but this was an extremely early development build. MDTravisYT posted about the discovery on social media. It’s noted that the source code has an “upper_fake” branch that compiles as VERSION_Z. This has a “unique” title screen alongside some “new” mega...
Diablo 4: Lord of Hatred Review
An extremely satisfying conclusion to Diablo's latest demonic saga.
'Everyone's nervous': Wells Fargo CEO on how businesses and consumers feel about the economy
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2025 major champion puts 30-gram driver shaft into play
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95% of people carry this virus and scientists may have just found how to stop it
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Asking Eric: ‘Extremely online’ girlfriend shares too much
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Astronomers detect mega-laser beam signal from 8 billion light-years away
Science News: One of the most remarkable discoveries made by astronomers in recent years is the identification of an extremely strong \"mega-laser\" signal that has t.
Nintendo Prime: Nintendo is spreading fake information internally to patch leaks
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
This superconductivity dies then comes back to life
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Rare, violent rift discovered in Ngogo chimpanzee group from 'Chimp Empire'
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Apple very cautious about iPhone Ultra sales; Samsung wins concession
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Immunic COO highlights the importance of science-led innovation to mark World Health Day
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Scientists just uncovered the secret behind nature’s “proton highway”
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Foldable iPhone hits engineering snags, shipment delays possible: sources
April-May 'extremely critical' period for Apple to resolve issues in time
Dear Abby | Brother's beliefs are incompatible with sister's reality
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Charlie Day recalls in horror taking all his clothes off in front of Jennifer Aniston: 'Never felt less attractive'
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
Trump tells US that Iran war victory near, but vows big strikes
In a speech from the White House, the US president vowed two to three weeks of "extremely hard" strikes against Iran
Who could the Chiefs potentially draft with the ninth pick?
- The 2026 NFL Draft is less than a month away and it's an extremely important one for the Kansas City Chiefs, who have plenty of holes to fill on the roster.
Melania Trump’s very robotic White House guest
The storied White House East Room has welcomed politicians, foreign dignitaries, war heroes, celebrities and elite athletes, but on Wednesday, it was the setting for an extremely robotic speech introducing first lady Melania Trump.
Airport chaos is adding urgency to the DHS shutdown debate. But who has the leverage?
DHS has been shut down for more than a month. But extremely long and growing TSA wait times have created newfound political urgency to reach a deal.
Toto Wolff: Mercedes team principal wary of 'politics' as Charles Leclerc makes 'extremely strong' Silver Arrows claim
Scientists Trap Light in a Layer 1,000x Thinner Than a Hair
Scientists have found a way to trap light in an extremely thin layer, potentially reshaping how future photonic devices are built.
New dispute emerges around Subnautica 2’s early access release
Lawyers for the ousted—and now partially reinstated—leadership team of Unknown Worlds, the studio behind Subnautica 2, are crying foul over news that the game will be released into early access in May. The release announcement earlier this week was made improperly, potentially “damaging the game and sowing additional confusion among the Subnautica community,” the lawyers said in a letter sent on Tuesday to Vice Chancellor Lori Will of Delaware’s Court of Chancery. In their letter, the lawyers also floated the idea of asking Will to find Krafton in contempt of the court’s ruling. The letter was sent just one day after Will’s bombshell ruling that publisher Krafton had breached its contract with Unknown Worlds’ executive team, when it fired them last June. The new issue isn’t necessarily the May early access timeframe, but how the news was announced. The letter and Krafton’s response have not been previously reported. A quick recap of prior events: Subnautica 2 is an unreleased, underwater survival adventure and the most-wishlisted game on Steam. Last year, longtime Unknown Worlds CEO Ted Gill, along with studio co-founders Charlie Cleveland and Max McGuire, were fired by Krafton. They swiftly sued, saying the publisher had terminated them to avoid paying out up to $250 million in potential performance-based bonuses. The studio leaders had hoped to earn that with an early access release of the game, which Krafton had blocked. The publisher argued in court that Subnautica 2 wasn’t ready, which the ousted execs disputed. On Monday, Will ruled that Krafton had violated its contract, firing the execs to avoid the performance payouts, and that it had to reinstate Gill and give him full authority over any early access release for Subnautica 2. On Tuesday, IGN reported on a new memo issued that day to Unknown Worlds staff by Steve Papoutsis, the executive Krafton had put in charge of Unknown Worlds after the 2025 firings. In the note, Papoutsis said that Subnautica 2 had passed a key development milestone “last week” and that he and Krafton had determined the game “is ready for Early Access release in May.” This news appeared to catch the lawyers for Gill, Cleveland and McGuire by surprise. On Tuesday night, they sent a letter to Will expressing “serious concerns” about whether it would be possible to forge an agreement with Krafton consistent with the judge’s order. Papoutsis’ memo was a problem, they wrote. He didn’t have the authority to declare anything about Subnautica 2’s release. As of 9am ET on Monday, March 16, the effective date of Will’s order, Papoutsis wasn’t CEO of Unknown Worlds. Gill was. And, per Will’s order, only Gill could make Subnautica 2 release decisions. They continued: Krafton self-servingly announced the launch without any regard to its impact on the game, the team, or the community—let alone this Court’s Opinion. Announcing the release of a game is momentous, and it is typically accompanied by significant marketing activity, fanfare, and community coordination. And most importantly, the announcement is carefully designed to maximize excitement for the game. That entire process was supposed to be driven by Mr. Gill. However, in defiance of the Court’s Opinion, Krafton has now taken that away, further damaging the game and sowing additional confusion among the Subnautica community. The lawyers speculated that Krafton had “intentionally leaked” Papoutsis’ memo and then issued an official statement to confirm the leak. Game File is a reader-supported publication. My reporting is only possible through support from readers like you. To support my work, consider becoming a free or paid subscriber. Subscribe On Wednesday, lawyers for Krafton defended their client in their own letter to vice chancellor Will, saying nothing they did violated her order nor showed contempt: Papoutsis’s message was simply celebrating the UW employees’ efforts toward a past event: Krafton’s pre-Opinion determination that Subnautica 2 was ready for Early Access release. There was nothing improper about conveying the results of the milestone review or thanking the development team for their dedication and talent. They added that Gill could still make his own call about Subnautica 2’s release: In his role as CEO, Gill will be able to assess independently his views on the state of Subnautica 2 and the appropriate release schedule; nothing in Papoutsis’s message alters Gill’s authority or discretion. Vice chancellor Will has not responded to the letters and doesn’t necessarily have to. Both parties were previously ordered by Will to confer this week and submit a proposed judgment consistent with her ruling. The letter from Gill’s attorney’s this week doesn’t indicate whether he would or would not support a May release for Subnautica 2. But it could limit his options. And, if Gill’s lawyers are right, the lack of marketing fanfare around the leak of the May date could dull the launch of the game. Any harm to Subnautica 2’s launch is an extra sensitive issue given the context of the legal dispute: millions of dollars of payouts are on the line, tethered to the scale of the game’s initial performance. In her ruling on Monday, Will noted that there was obvious “bad blood” between Gill’s side and Krafton but that the two would need to work together: “They can—and must—act in good faith to navigate their remaining contractual relationship.” Item 2: A new kind of couch co-op game A highlight of GDC each year is the Alt.Ctrl showcase that features games played with extremely experimental controllers. Perusing this year’s offerings last Friday, I found a game controlled with giant scissors, another with big toothbrushes and one that amounted to dumpster diving. My favorite was Pivot, a co-op game about collectively maneuvering a couch. It was developed by students at the University of Utah’s masters of arts and engineering games program. Players lift and tilt the physical couch in order to control the position of a virtual one. That virtual one is moving through an obstacle course on-screen, while being pursued by a giant skull. Developer Nishant Verma told me at GDC that the couch is made of styrofoam (this became more evident as I watched!) and that the name was a riff on an episode of the sitcom Friends. Item 3: In brief… 🚫 Ubisoft is laying off more than 100 workers at its Red Storm studio, as the North Carolina location known for decades for its Tom Clancy games, ceases in-house game development, VGC reports. 👀 On Monday, Nvidia’s revealed its DLSS 5 AI-driven graphics technology that purports to improve game graphics… and it has been roasted about it by the media and the online gaming public for the remainder of the week. The tech changed the faces of characters in footage from Resident Evil Requiem, Hogwarts Legacy and EA Sports FC, drawing accusations of giving carefully art-directed games the generic look of AI “slop.” Nvidia CEO Jensen Huang had said on Monday that “DLSS 5 is the GPT moment for graphics—blending handcrafted rendering with generative AI to deliver a dramatic leap in visual realism while preserving the control artists need for creative expression.” By mid-week, Huang was telling Tom’s Hardware that the new tech’s critics were “completely wrong” because game-makers could still “fine-tune the generative AI” to suit their game’s aesthetics.
Something extremely weird is happening to our galactic neighbor. Scientists think they know why
The stars in the Small Magellanic Cloud aren’t behaving the way they should. A cataclysmic collision with another nearby galaxy may be the culprit
Scientists just discovered a tiny signal that volcanoes send before they erupt
A new detection method called “Jerk” could dramatically improve how scientists forecast volcanic eruptions. By using a single broadband seismometer, the system can detect extremely subtle ground movements caused by magma pushing underground—often hours before an eruption begins. Tested for more than a decade at the Piton de la Fournaise volcano on La Réunion, the tool successfully predicted 92% of eruptions between 2014 and 2023, sometimes giving up to eight hours of warning.
Windows 11 gets extreme display refresh rate support and new features in KB5079387
A preview of this month's non-security update for Windows 11 is here, and it brings support for extremely high display refresh rates, improved File Explorer, and more.
Astronomers witness magnetar birth for first time
Scientists observed for more than 200 days the death of star that resulted in a dense, spinning remnant threaded with an extremely strong magnetic field
Apple's entry-level M5 SoC in the MacBook Air 13 only consumes 7-8 Watts in games like Cyberpunk 2077 or Baldur's Gate 3
Apple's new MacBook Air with the small M5 SoC is extremely efficient while gaming. During our tests, we recorded a SoC consumption of just 7-8 Watts, which means the gaming performance is also rock solid despite the passive cooling solution.
Workplace violence rising in health care as hospital shooting renews security concerns
While shootings inside hospitals remain extremely rare, violence in health care settings more broadly is a growing national concern.A patient and a visitor are
Five Berkeley Lab Scientists Receive DOE Early Career Research Awards
Five scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the Department of Energy. The ECRP program, now in its 16th year, was established to bolster the nation’s scientific workforce by supporting exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities. To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years. This year’s Berkeley Lab awardees and their projects are listed below: Timon Heim: Better detectors for more powerful particle colliders Timon Heim is a staff scientist in the Physics Division. His ECRP project will develop a new type of detector that can track the particles generated by collisions in particle colliders with high spatial and temporal resolution. Current detectors in use at high-energy colliders, like the ATLAS detector at the Large Hadron Collider (LHC) at CERN, can track the trajectory and measure momentum of particles, but they can’t track a particle’s time of arrival with sufficient precision to discern from which of the many particle interactions they originate. Heim’s proposed technology — called a 4D tracking detector, because it captures data about particles’ positions in space and time — aims to solve this challenge, allowing researchers to glean more precise data from collision experiments to uncover new insights into fundamental forces and potentially discover new particles. The enhanced capabilities of 4D detectors will be a necessity for experiments at future colliders, such as the Muon Collider or Future Circular Collider. The project will develop custom microelectronics that process the signal of a pixelated silicon sensor that can register charged particles passing through it in an extreme radiation environment that these colliders produce during operation. The goal is to measure individual particles’ position with 50-micro-meter spatial and 50 pico-second (one trillionth of a second) temporal precision, for an incoming flux of around 1 billion particles per second per square centimeter. Harrison Lisabeth: Studying underground processes to unleash abundant energy Harrison Lisabeth is a research scientist in the Energy Geoscience Division focused on combining real-world experiments and AI to guide development of geothermal energy systems. His project will explore how deep underground rock formations – where geothermal wells are dug – are stressed by chemical reactions of the minerals and mechanical strain from movements in the Earth. A better understanding of subsurface stresses is essential to engineer safe and reliable geothermal wells that access the heat of deep rocks to produce electricity. It is estimated that the U.S. could generate ten times the amount of energy needed to power the grid using geothermal sources. Lisabeth plans to simulate how minerals grow, dissolve, and influence the behavior of surrounding rock in geothermal reservoirs by tracking chemical changes in rock samples under pressure in the lab. The project will also develop machine learning models to predict geologic strain based on field measurements and refine existing models that describe how common rock types deform under stress. Combining these approaches, Lisabeth hopes to develop a framework to predict stress to improve stability and longevity of geothermal wells. Daniel Carney: Pushing the quantum boundaries of particle measurement Daniel Carney is a staff scientist in the Physics Division who uses quantum information science to study particle physics and gravity. At very small scales, “quantum noise” — random fluctuations from the act of measurement — can drown out faint signals from rare processes. Carney’s ECRP project, “Fundamental Physics at the Quantum Limits of Measurement,” aims to build a new mechanical quantum sensor that can measure extremely small particle interactions below the limits of that quantum noise. The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
NASA Captures an Extremely Rare Atmospheric Phenomenon Above the Pacific - Futura-Sciences
However, these aren’t actually rainbows but a phenomenon known as “glory.” It’s an optical event often spotted from airplanes, caused by the refraction of sunlight on the water droplets that make up the clouds. In reality, there is a single glory, stretching about 480 kilometers, consisting of two multicolored edges....
Scientists want to put a super laser on the moon
An ultrastable laser could enable extremely precise timing and navigation on the moon, and the cold, dark craters near the lunar poles would be the ideal location for it