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Gemini app starts rolling out Personal Intelligence globally
After the US launch, Google is bringing Personal Intelligence to Gemini users around the world (excluding Europe).
Adopting, advancing and adapting to AI
In Penn State's Eberly College of Science, researchers are leveraging AI in a myriad of applications and instructors are finding innovative ways to incorporate AI into their classrooms. But what is artificial intelligence, and are people really talking about the same thing when they say AI?
Diarrhea-causing infection increasing in US, CDC says
Officials called the increase "a public health threat," especially since shigella is spread easily from person to person.
Google brings its Gemini Personal Intelligence feature to India
The feature lets users connect Google accounts like Gmail and Photos to get personalized answers
Q&A with chemist Max Delferro: Unlocking breakthroughs in catalytic science
In this Q&A, chemist Max Delferro explains how his team is combining catalysis, artificial intelligence and robotics to speed chemical discovery — cutting catalyst development from decades to years while advancing more efficient industrial processes.
Q&A with chemist Max Delferro: Unlocking breakthroughs in catalytic science
In this Q&A, chemist Max Delferro explains how his team is combining catalysis, artificial intelligence and robotics to speed chemical discovery — cutting catalyst development from decades to years while advancing more efficient industrial processes.
I Feel So Sorry for My A.I. Sunglasses
Plenty of people hate Mark Zuckerberg’s superintelligent, supercharged spectacles. I was ready to hate them, too.
From War Hero to Politics, Zelenskiy’s Top Aide Steps out of the Shadows
Kyrylo Budanov was taken from military intelligence to run Ukraine’s presidential office. It’s a political post that has Kyiv powerbrokers speculating over his future career path.
UConn guard Azzi Fudd goes No. 1 to the Dallas Wings, reunited with Paige Bueckers
Azzi Fudd became the seventh UConn player to be selected No. 1 in the WNBA draft when she was picked by the Dallas Wings.
Political newcomer running for Rep. Golden's seat in US House
At 29 years old, Paige Loud is the youngest candidate in the Democratic primary race for Maine's 2nd Congressional District.
NFL Draft 2026: A closer look at some Clemson prospects
The Clemson Tigers have a couple of players who have been mocked to the Chicago Bears, so we look at their top prospects here.
The gut health benefits of everyday herbs
Discover how everyday herbs support gut health, digestion, and overall wellness while adding flavor to simple, everyday meals.
Nige and Zia set out plan to send ‘Boriswave’ traitors to the gulag
Farage and sidekick are still sore about how the Tories handled Brexit, although new mates Braverman and Jenrick are forgiven
2026 All-Juice Team: One play from Fernando Mendoza earned him instant entry to this squad
On a team and in a process that has always sought QBs who could lead and elevate players around them, Mendoza's inclusion became a no-brainer. With that in mind, we give you the 12th installment of the All-Juice Team.
Smart Adhesive Is Key to Crease-Free Foldable iPhone Display
Advances in optically clear adhesive (OCA) will be a key factor in achieving a near-invisible crease in Apple's first foldable iPhone expected later this year, according to TrendForce. The supply chain intelligence firm outlined the key technologies in a new report on foldable display innovation, explaining that creases form when layers within the display panel fall out of alignment, concentrating stress at the fold and causing micro-cracks or permanent deformation over time.
Entrepreneurship was never easy, but it doesn’t need to be relentless
Why entrepreneurs and small business owners struggle with mental health, and how intelligent systems can improve that.
US intel officials scramble to keep surveillance law running amid Iran war tensions
With just days until a powerful surveillance law lapses, US national security officials are scrambling to prepare for potential blind spots in intelligence collection amid the US’ delicate ceasefire with Iran, current and former officials told CNN.
Watch: As AI Makes More Health Coverage Decisions, The Risks To Patients Grow
This year, executives from nearly every major health insurance company made the same declaration in calls with Wall Street analysts: Using artificial intelligence to make coverage decisions would help save them money.
What Does Artificial Intelligence Really Mean for Global Politics?
As AI increasingly plays a central role in conflict and war, experts discuss its long-term impact on global politics.
The Masters: Rory McIlroy wins back-to-back green jackets
Rory McIlroy becomes only the fourth golfer to win back-to-back Masters tournaments, joining Jack Nicklaus (1965-1966), Nick Faldo (1989-1990) and Tiger Woods (2001-2002).
Masters 2026: Only one question remains for Rory McIlroy — how high will he climb among golf's all-time greats?
McIlroy may never have the numbers to catch Jack Nicklaus or Tiger Woods, but he certainly has a pathway to No. 3 all-time.
GameThread: Tigers vs. Marlins, 1:40 p.m.
Detroit Tigers (6-9) vs. Miami Marlins (8-7) Time/Place: 1:40 p.m., Comerica ParkOpponent Site: Fish On FirstMedia: Detroit SportsNet, MLB.TV, Tigers Radio NetworkPitching Matchup: LHP Tarik Skubal (1-2, 2.55 ERA) vs. RHP Sandy Alcantara (2-0, 0.74 ERA) Lineups MARLINS TIGERS Austin Slater – RF Kevin McGonigle – SS Agustin Ramirez – C Gleyber Torres – 2B Jakob Marsee – CF Colt Keith […]
Eight years later, Apple quietly shuts the door on AI chief John Giannandrea
Since his retirement was announced in 2025, Apple Intelligence head John Giannandrea has been reduced to the role of an advisor, but is now expected to exit Apple Park shortly.
The Masters 2026 - final round LIVE! Latest updates, highlights, scores as Rory McIlroy chases major victory at Augusta National
Rory McIlroy looks to become first back-to-back winner of The Masters since Tiger Woods; coverage is live from 4.30pm on Sky Sports Golf, with Featured Groups, Featured Holes and extra coverage on Sky Sports+
Pulling Reports, Playing Politics
The CIA’s retraction of intelligence reports should raise concerns about politicization and the Trump administration’s embrace of white supremacist rhetoric.
Marlins-Tigers finale features Cy Young Award matchup
The Detroit Tigers are hoping the third time's the charm early in the season.
Hidden weak spots in HIV and Ebola revealed with breakthrough nanodisc technology
A new nanodisc-based platform lets scientists study viral proteins in a form that closely mimics real viruses, revealing how antibodies truly recognize them. This approach uncovered hidden interactions in viruses like HIV and Ebola that traditional methods missed. By recreating the virus’s membrane environment, researchers can better understand how immune defenses work. The technique could speed up the development of more effective vaccines.
South Carolina sweeps Mizzou in battle of SEC bottom dwellers
The Tigers put up a two-out ninth inning rally, but they fell short in their 6-4 loss to the Gamecocks on Saturday.
“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.
Tigers' Casey Mize hopes sunny skies signal success vs. Marlins
Casey Mize made his most recent start in brutal weather conditions, and the Detroit Tigers right-hander hopes better weather will lead to better results on Saturday when he pitches the middle game of a three-game home series against the Miami Marlins on Saturday afternoon.
Alaska business plan competition awards Native entrepreneurs tackling rural service gaps
Alaska Growth Capital’s Alaska Marketplace competition awarded Indigenous entrepreneurs up to $20,000 to grow businesses addressing service gaps across rural Alaska.
Insights from CLI: Tracking AI's Impact on Offices and Business Parks in APAC
April 11, 2026 -- Artificial intelligence (AI) will be one of the most consequential technological shifts of the current era. It will fundamenta...
Exclusive: US intelligence indicates China is preparing weapons shipment to Iran amid fragile ceasefire, sources say
US intelligence indicates that China is preparing to deliver new air defense systems to Iran within the next few weeks, according to three people familiar with recent intelligence assessments.
Tigers back in the win column, blank Marlins in opener
Behind a sterling start from Keider Montero and just enough offense, the Tigers shut out the Marlins, 2-0, at Comerica Park on Friday.
How OpenAI's reported ad plans could change the AI business model
OpenAI (OPAI.PVT) is reportedly projecting $100 billion in advertising revenue by 2030. Yahoo Finance Technology Editor Dan Howley explains how this could shift the artificial intelligence (AI) business model.
Tigers' Parker Meadows lands on 10-day IL with arm fracture, concussion after collision sent OF to hospital
Cenkusha Launches C1: An AI-Enabled Business Management System that Reveals How Value Flows in Organizations
Cenkusha is a technology company that provides the organizational nervous system and structural backbone that modern enterprises need to operate intelligently in an increasingly complex world.
Goodbye Traditional Bathtubs & Showers – There's A New Trend Taking Over
Technology has made its way into the smallest of spaces in our homes, including the bathroom. There's one smart tech feature in particular that's trending.
Part 1: From science fiction to stark reality: A free film and forum on AI
This is part one of a two-part series that will examine a growing public curiosity and concern about artificial intelligence and offer the community an opportunity to use examples from
Watch: As AI Makes More Health Coverage Decisions, the Risks to Patients Grow
Major health insurers and even Medicare are using artificial intelligence to make coverage decisions. But class action lawsuits have accused insurers of using AI to wrongfully withhold treatment, and new research illuminates the risks.
From The Sports Desk: Everyone’s talking Tiger
Woods’ absence looms large at the Masters. Also: Who will win the green jacket?
North of North Creators to Receive WIE Canada Honors
The ‘North of North’ indigenous comedy creators and showrunners will receive the Breakthrough Award on May 28 in Toronto.
Player tells Tiger to 'get a chauffeur'
Golf legend Gary Player, speaking at the Masters on Thursday, called on Tiger Woods to "get a chauffeur" after his DUI arrest last month.Player, like the Masters and PGA Tour, supported Woods in his fight to recover.
GUEST COLUMN: NC State students turn their campus experiences into a business
SUMMARY: Three SKEMA Business School students used their academic experience at NC State to build Apolia, an artificial intelligence voice assistant designed for small businesses.
Masters 2026 odds, picks, predictions, props: Expert fading Tyrrell Hatton in best bets at Augusta National
SportsLine golf expert Brady Kannon just locked in his best bets for the 2026 Masters at Augusta National Golf Club with Tiger Woods and Phil Mickelson both not playing
Secretly Distribution acquires music data and analytics firms Entertainment Intelligence and Babel Ops
Babel Ops was founded in 2020 by Erik Gilbert and Greg Delaney, who previously co-founded Entertainment Intelligence in 2014.
Gen Alpha drives shift toward AI-powered entertainment discovery
Gracenote has released a new report examining how artificial intelligence is reshaping the way audiences ... Read More
University, UNC Health unveil SHIRE health care innovation platform
The Secure Health Informatics Research Environment will speed up the responsible use of artificial intelligence with clinical data.
If you aren’t losing weight with GLP-1 drugs, this may be one reason why
Two genes involved in gut hormones that influence appetite and digestion may be a factor in how people respond to GLP-1 drugs, according to a new study.
Huck Distinguished Lecture Series brings leaders in AI, biodiversity and cryo‑EM
The Huck Distinguished Lecture Series in April will feature Tanya Berger-Wolf of The Ohio State University and Wah Chiu of Stanford University, highlighting advances in artificial intelligence for biodiversity research and cryogenic electron microscopy for biomedical discovery.
Masters 2026 odds, picks, predictions, favorites: Expert fading Rory McIlroy at Augusta National
SportsLine golf expert David Bearman just locked in his predictions for the 2026 Masters at Augusta National Golf Club with Tiger Woods (personal) not in the field
Cold hard truths on climate change
These data-driven firms are turning atmospheric chaos into actionable intelligence:
What Is Tmrw Longevity Powder? Here's what to know about the blue scoop
Tmrw Longevity Powder combines blue spirulina with a focused blend designed to support energy, clarity, digestion and healthy aging pathways.
Saints Select LSU Tigers Playmaker in 2026 Mock Draft
LSU playmaker Mansoor Delane heads to New Orleans at No. 8, bolstering a depleted secondary and potentially igniting playoff hopes.
Liverpool receive sensational Yan Diomande transfer boost
Barcelona drop out of Diomande raceA report from Spain via Sport Witness now suggests that Liverpool will have one less rival bid to worry about this summer. Barcelona did their due diligence on Dioma...
Viewpoint: FP10 should remain open, excellent and secure
Europe is right to worry about research security. Foreign interference is real. So is the misuse of sensitive knowledge. Universities working in sensitive areas such as AI, quantum, semiconductors and advanced materials cannot afford to ignore the geopolitical environment around them.But Europe is in danger of drawing the wrong conclusion, one that would increase, not reduce, its strategic vulnerabilities.If the EU applies an “EU preference” logic to the next Framework Programme for research and innovation (FP10), it will weaken one of its greatest strategic assets: a research system built on openness, excellence and international collaboration. In the name of security, Europe could end up advancing science more slowly, innovating less effectively and making itself less attractive to global talent and partners in advanced science and technology.That would be a serious mistake.The EU preference included in Article 10 of the proposed European Competitiveness Fund may make sense in some industrial or deployment contexts, but is far harder to justify when applied to collaborative research in advanced science and technology, where excellence and global connectivity are crucial. Research is not procurement. It does not thrive when isolated. It advances through access to the best ideas, the best people, the best facilities and the best data, wherever they are found. This is why FP10 should not simply absorb industrial policy logic by default. Doing so would risk importing instruments that are ill-suited to research and that ultimately increase vulnerability rather than resilience.For universities of science and technology, the issue is especially acute. These institutions sit at the frontier of knowledge and technology creation in the very fields Europe sees as critical for its future competitiveness and resilience. They know better than most that security risks are real. But they also know that openness and security are not opposites. In practice, they have to be made to work together. Research security is not about closing doors. It is about building resilience through collective responsibility, trust, proportionate safeguards and better governance. That is a fundamentally different approach from default restriction.The risks of non-collaborationThe real danger in the current debate is that Europe talks too much about the risks of collaboration and not enough about the risks of non-collaboration. Those risks are strategic too. Cut researchers off from international networks and Europe loses access to knowledge, data, infrastructures and talent. Slow down global collaboration in critical technologies and Europe slows down its own progress on cutting-edge science. Make participation rules unpredictable and Europe weakens its attractiveness to researchers and investors alike.In other words, over-restricting collaboration will create strategic vulnerabilities, particularly in advanced science and technologies.This point is often underestimated in policy discussions that frame openness mainly as a value to be defended. Europe’s scientific strength, technological development and long-term competitiveness depend on remaining connected to the best research ecosystems in the world. For a continent that wants to accelerate deep tech, strengthen industrial capacity and reduce strategic dependencies, weakening the excellence base would be self-defeating. That does not mean ignoring risk. Quite the opposite. Universities are already strengthening due diligence, internal governance, awareness-raising, screening procedures and compliance support. Across Europe, institutions are working to manage risks linked to foreign interference, sanctions, export controls and the misuse of research. The right response is not complacency. It is better risk management. But that means targeted, evidence-based and proportionate measures, not blunt top-down restrictions with wide spillover effects. In the daily reality of universities, openness and restriction often coexist. The real challenge is how to apply safeguards without sliding into securitisation that damages academic freedom, open science and international cooperation. Restrict partners, not nationsMore restrictive treatment of collaborators from European Economic Area members Iceland, Liechtenstein and Norway, or from Switzerland and the UK, is strategically very difficult to justify. These are deeply embedded partners in Europe’s research and innovation ecosystem. Beyond that, Europe should still resist default exclusion and instead rely on case-by-case assessment. The relevant question is not nationality in the abstract. It is the actual risk profile of a collaboration: the institution, the affiliations, the field, the knowledge involved, the intended use and the safeguards available. Screening should focus on institutions and affiliations rather than nationality alone. Related articlesParliament ‘misreads’ link between Horizon Europe and ECF, Commission official saysFrustration grows at governance gap in next Horizon EuropeIf EU preference logic is imported into research without careful calibration, the likely result is a chilling effect. Researchers may step back from legitimate and beneficial collaboration, even where risks are manageable. International partners may see Europe as less predictable. And Europe may end up undermining the very excellence base it needs to compete, scale up and lead in strategic technologies, thereby increasing its dependence on others.FP10 should do the opposite. It should remain open by default, with restrictions used only exceptionally, and then on the basis of clear evidence. It should integrate research security into open science and research integrity frameworks, not set them against each other. And it should recognise that academic freedom, scientific excellence and responsible international collaboration are not obstacles to Europe’s security, they are part of it. Europe needs stronger tools to protect itself. But it also needs the discipline to avoid measures that would weaken it and increase its strategic vulnerabilities in the process. In FP10, that means staying open, excellent and secure.Louise Drogoul is senior advisor for innovation and sustainability at the Cesaer university association.
Last Call for 4.7.26 – A prime-time read of what’s going down in Florida
A digest of the day's politics and policy while the bartender refreshes your drink.
Masters 2026 odds, picks, predictions, favorites: Expert fading Rory McIlroy at Augusta National
SportsLine golf expert David Bearman just locked in his predictions for the 2026 Masters at Augusta National Golf Club with Tiger Woods (personal) not in the field
The Boundless Deep: Young Tennyson, Science, and the Crisis of Belief by Richard Holmes
Pantheon Review by Walter Cummins The image of Alfred Tennyson I’ve carried for decades goes back to the childhood card game of Authors that depicts him with a stately continence sharpened by a jutting black beard. He’s formidable, his aura superior. Holmes opens his biography of the youthful Tennyson by describing the similar formality of the imposing Tennyson statue at Trinty College Cambridge, sharing the honor with other Trinity greats, including Isaac Newton, Francis Bacon, and Thomas Babington Macaulay. Holmes doesn’t want to chip away at that statue but rather reveal its inner youthful core of the man—tall, handsome, and talented but unformed, emerging from a disturbed family of addicted or insane male siblings and four unusual sisters struggling under a violent alcoholic clergyman father. Alfred was certainly statuesque, but would he end up a block of unformed stone like his equally promising brothers? He was youngest of three equally regarded Tennyson brothers at Cambridge, all bright, handsome, and talented, in fact Alfred regarded by some as less promising than his elders who also wrote poetry. Yet Alfred wrote one of his major poems, “The Kraken,” at twenty, revealing both his talent, his interest in geological forces, and the haunting creature that embodied his anxieties. While Holmes focuses on Tennyson of the 1830s and 1840s, he can’t tell his story without exploring his involvement with the Victorian intellectual upheaval. Tennyson was fascinated by new scientific perspectives, following the latest findings as soon as books and articles were published. He was especially taken with Charles Lyell’s deep studies of geology and the discovery of fossils and a timeline of the planet’s development that did much to question fundamental religious assumptions. The impact of these discoveries is reflected in Tennyson’s poetry and led to the crisis in belief he shared with so many contemporaries. He was especially taken by the cycles of destruction and renewal that informed the struggles he confronted in In Memoriam, Maud, and Idylls of the King. Tennyson was only one of the eventual major figures at Cambridge at the same time, young men just beginning to realize their potential. Charles Darwin was at Christ College, although there is no evidence that he and Tennyson even met as students or later in life. Yet evolution became significant in Tennyson’s thinking and the issues of his poetry. One of Tennyson’s best and most important Cambridge friends was Edward FitzGerald, who became the translator of The Rubáiyát of Omar Khayyám and in their on and off relationship served as Tennyson’s harshest critic. FitzGerald was also a friend of the novelist William Makepeace Thackeray, the two of them partying in Paris as undergraduates. That connection led to a friendship between Thackeray and Tennyson. Others who came to play significant roles in Tennyson’s life were Thomas Carlyle, the Brownings, and the Pre-Raphaelites. Tennyson’s deepest friend at college was Arthur Hallam, who at twenty wrote a glowing review of Tennyson’s first poetry collection, travelled on European adventures with him, and fell in love with his sister Emily. Hallam was also regarded as one more very promising Cambridge student. His sudden death at twenty-two in Vienna from a stroke while travelling with his father devastated many friends, but perhaps Tennyson most of all. He did not write immediate poems of grief but took years to compile those collected in In Memoriam A.H.H., published in 1850, almost twenty years after Hallam’s death For all of Tennyson’s early poetic successes and successes, Holmes reveals him as an often troubled young man suffering periods of depression, fearing planetary upheaval, concerned about supporting himself, disappointed in romances, and worried that he would inherit the family’s mental illness tendencies. Yet despite his seeming withdrawals, he was always writing poetry that he kept private and would not let go, constantly revising. 1850 became a transformative year for Tennyson. He finally consented to publishing In Memoriam with a last-minute prologue that acknowledged God and perhaps convinced the religious Emily Sellwood to marry him after years of pursuit.. He opened up to Emily, revealing his struggles with fears of depression and “weird seizures.” The devastating event of that time was the stillborn death of his unnamed first son. It shook him, but Emily’s faith brought them closer and convinced him to believe in an afterlife. Two later sons survived. With the death of William Wordworth in April of 1950, Tennyson was named poet laureate at age forty-one, reluctantly accepting. It’s as if Tennyson’s life split in half in that year. He lived another forty years, now a recognized success, with the prestige of being an official poet writing patriotic verse, wealthy and revered, no longer the insecure young man seeking an outlet for his immense talent. He earned a place in the Authors deck, now dignified and in 1884 elevated to the peerage as Arthur, Lord Tennyson. Richard Holmes is the author of twenty previous books on the Romantics and Victorians, several of which received awards. The Boundless Deep was shortlisted for the 2025 Baillie Gifford Prize for Non-Fiction. He captures the complex vicissitudes of Tennyson’s youth, illustrated by frequent excerpts from the poetry, placing that life in the full context of the shattering discoveries that led to an upheaval of thought in the Victorian age.
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MAGA Is Winning Its War Against U.S. Science
Source: NBER With all the other terrible news right now, you may not have noticed that Donald Trump is in the process of killing American science. OK, that’s an exaggeration — but not that much of an exaggeration. The Trump administration’s latest budget proposal calls for a gigantic increase in military spending combined with severe cuts to social programs. But as the chart above shows, it also calls for debilitating reductions in research funding. Furthermore, Trump appointees have already been strangling science by sharply reducing the rate at which research grants are approved. Here, for example, is the number of new grants approved by the National Science Foundation: Source: Nature Large numbers of existing grants have also been frozen or terminated, especially in the study of infectious diseases. Add to this a sharp drop in visas issued to foreign students, who often play a direct role in research and who help support academic departments that do research: Put all of this together, and much U.S. scientific research is set to come to a screeching halt — not a few years from now, but over the course of the next year or two. This new assault on U.S. science is taking place at a time when the role of American science in the world has already been greatly eroded. The chart below, based on research recently reported by the National Bureau of Economic Research, measures national strength in science by the share of publications in highly ranked journals. In the 1990s the United States had more such publications than the rest of the world combined. Since then we’ve dropped into third place — well behind China and slightly below the European Union. And this was before the Trump administration’s attack on science had time to take its full effect. Source Some erosion in U.S. scientific preeminence was inevitable given China’s growing sophistication and wealth. But we’ve also fallen behind Europe, even though everyone says that Europe is lagging economically and technologically. Claims about Europe’s underperformance are, in fact, dubious if one looks hard at the data. But it’s still striking to see America lagging. What’s going on? There are presumably multiple factors behind America’s scientific lag. But even before Trump II the growing hostility of the U.S. right to science surely had some negative effect. And since the rise of MAGA G.O.P. attitudes toward science in general have become overwhelmingly hostile. This is true even for the Republican rank and file: And while I haven’t been able to find good survey data, it’s obvious that the anti-science turn has been even more pronounced — and began earlier — among the Republican political elite. Chris Mooney published The Republican War on Science in 2005, and even then was describing a longstanding trend. Why have Republicans turned so anti-science? Part of the answer is that they believe that scientists don’t support them. And they’re right! A study of who scientists give money to shows that only a small percentage gave money to Republicans even 20 years ago, and that almost none of them donate to Republicans now: Social scientists have always been strongly pro-Democratic, while there used to be a significant number of physical, “hard” scientists supporting the GOP. But these days physicists are almost as uniformly Democratic, or at least non-Republican, as sociologists. Why are there almost no Republican scientists? It’s not a mystery. GOP political orthodoxy includes positions that are at odds with the scientific consensus on multiple issues, ranging from the validity of the theory of evolution, to the reality of climate change, to the efficacy and safety of vaccines. In each case the scientific consensus is solidly grounded in evidence. But even before the rise of MAGA the U.S. right was increasingly hostile to evidence-based policymaking — especially, of course, where the evidence is unfavorable to fossil fuel interests or quack medicine, both financial mainstays of right-wing politics. So scientists don’t support Republicans, and the feeling is mutual. Today’s Republican Party doesn’t like science or scientists. It doesn’t like having its preconceived views challenged by appeals to evidence. It knows that very few scientists are on its side electorally. In general, it sees scientific research as a threat to its grasp on political power. Add in MAGA’s combination of rabid anti-intellectualism and allergy to any hint of criticism, and one has the makings of a drastic anti-science turn in policy. “Ignorance is strength” might was well be an official MAGA motto. And as I said, we aren’t talking about something that will happen over the course of multiple years: The U.S. scientific enterprise is threatened with severe damage, even collapse, over just the next year. There are many reasons to find this prospect horrifying: Think of all the beneficial advances, affecting almost every part of life, that won’t happen because U.S. science — still crucial to the world — has been eviscerated. But think, also, of America’s international standing. Can a nation that has forfeited its role as a leader, or even a contender, in global science, still be a Great Power? No. MUSICAL CODA
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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:
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We all need to become better science storytellers. Here’s why and how.
Last week, in a lovely New Haven bar, six people gathered in front of a crowd to tell stories that were somehow related to science and public health. It was magical. One person talked about learning how social determinants of health helped them forgive their mother’s alcoholism. Another shared what it’s been like navigating a dementia diagnosis. Another described their last dinner with their mother before they left Nigeria to come to the U.S. and how that moment, plus the diagnosis that followed, shapes their career goals today. As the Executive Director of The Evidence Collective at Your Local Epidemiologist, I was ecstatic to bring this event, “Science and Storytelling,” to the community surrounding the Yale School of Public Health. Stories are not the default way most people communicate about science and public health. In fact, we are taught to take all the emotion out of data, but that makes it pretty darn boring to everyone else. And if people can’t see themselves in it, feel themselves in it, or influence it, how can we ask them to support it? Why do stories stick? How can we all—from community members to public health scientists—do better? And what are some examples?
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