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Artificial intelligence is reshaping environmental science into a predictive and precision-driven discipline
Artificial intelligence is rapidly transforming how scientists study and protect the environment, shifting the field from traditional observation toward predictive, data-driven discovery. A new perspective article highlights how emerging AI technologies are enabling a fundamental change in environmental research, opening the door to faster, smarter, and more integrated solutions to global challenges.
Ohio readies $5M fine against Kalshi for unlicensed sports betting: Capitol Letter
The prediction market operator is waging a multistate battle over whether its sports offerings should be treated as state-regulated gambling or federally regulated financial swaps.
Markets are correctly perceiving that politics will steer rate policy, says CIO
Rich Weiss, CIO of Multi-Asset Strategies at American Century Investments, says that markets are correctly predicting that politics will triumph over economics in pressuring the Fed to cut rates.
Warriors vs. Clippers odds, prediction, time: 2026 NBA Play-In Tournament picks, April 15 bets by proven model
SportsLine's proven model has revealed its Golden State Warriors vs. Los Angeles Clippers play-in best bets
How to Turn Peak Energy Demand Into Cost Control & Business Advantage
Learn practical ways to manage usage, reduce risk, and improve cost predictability during peak demand periods.
This Round 1 is built for chaos: 10 bold predictions that could hit
Let's get nuts and predict the first-round twists that could shake up the draft
JPMorganChase partners with sports legends to help athletes master their money
Athletes’ careers are unpredictable. Fewer than 2% of NCAA college athletes turn professional and many professional athletes retire before they’re 35. Yet nearly 65% of athletes say they never had financial education in school. To help address the unique challenges athletes face, JPMorganChase announced a new initiative to help athletes navigate every aspect of their financial lives, from early career stages […]
Rockets vs. Lakers: Can LeBron James shoulder the load if Luka Dončić can't go? Series keys, schedule and prediction
Prediction markets on track for $1 trillion by 2030, but sports betting stirs legal storm
Bernstein sees prediction market trading volume hitting $1 trillion by 2030, but an ongoing legal fight over sports event contracts could test that outlook.
Alabama Predicted by CBS Sports to Exceed Expectations
CBS Sports predicts a surprising 10- or 11-win season for Alabama. Can new offensive coordinator Ryan Grubb unlock a championship run?
Ohio regulators seek to fine Kalshi $5M for unlicensed sports gambling
COLUMBUS — Online prediction market platform Kalshi is facing a $5 million fine for operating an unlicensed sports gambling business in Ohio. The Ohio ...
Trump predicts oil prices will drop ‘tremendously’ despite recent surge
President Donald Trump sits down with FOX Business' Maria Bartiromo to discuss escalating Middle East tensions, his warning to Iran on nuclear weapons and why he believes gas prices will drop ahead of the midterms.
Iran latest: Trump predicts Iran war 'very close to over' in FOX Business interview
President Donald Trump suggested the end of the Iran war may be in sight, telling FOX Business' Maria Bartiromo, "I think it’s close to over, yeah. I view it as very close to being over."
Arsenal XI vs Sporting CP – Predicted lineup and team news
Arsenal host Sporting CP tonight, aiming to clinch a place in the Champions League semi-finals.The Gunners hold a slender 1–0 aggregate lead after a resilient display in Lisbon last week. Mikel Arte...
A new approach to brain health, one neuron at a time
Neuroscientist Paul Nuyujukian likens the brain to a stadium full of people. To eavesdrop on the crowd you could put a microphone in the middle of the stadium. But to understand the conversations you need to record individual people. He thinks about the brain the same way. To understand brain disease, he studies neurons—one at a time. And his insights are shedding light on a big global issue—stroke. The World Health Organization predicts one in four adults will have a stroke in their lifetime. Strokes can cause death, or lead to paralysis or speech problems. But there’s still a lot researchers don’t know about how the brain recovers from an event like a stroke. Nuyujukian directs a lab at Stanford University that studies how the brain controls movement, including after neurological events like stroke. We get into how he does this, and why he hopes his research could eventually help people who’ve been paralyzed. Email us your questions about the brain – or anything else to do with science at shortwave@npr.org. We may turn it into an episode in the future!Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.
Gary Gensler Paved the Way for Prediction Markets. Sports Betting Wasn’t Part of the Plan.
Kalshi and Polymarket say their sports bets are allowed under federal law. States argue otherwise. The designer of the law is weighing in.
NBA play-in predictions: Magic-76ers? Warriors-Clippers? Our takes on who will advance
2026 RBC Heritage odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 17 majors
Mike McClure's model has simulated the RBC Heritage 2026 10,000 times and revealed PGA picks for Hilton Head
Bengals stop Jeremiyah Love's fall, Chiefs double down at CB in NFL mock draft based on what teams should do
Most mock drafts try to predict what teams will do. This one flips the script. Here's how I'd attack Round 1 as each team's GM.
Ohio Casino Control Commission fines Kalshi prediction market
Prediction market Kalshi is facing a fine from the Ohio Casino Control Commission for unlicensed sports gaming.
WATCH LIVE: CFTC chairman testifies before House panel amid scrutiny of prediction markets
NBA Play-In Tournament predictions: Expert picks, odds for Hornets vs. Heat, Suns vs. Blazers and more
The 2026 NBA postseason gets started on Tuesday night with the Play-In Tournament
2026 RBC Heritage odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 17 majors
Mike McClure's model has simulated the RBC Heritage 2026 10,000 times and revealed golf picks for Hilton Head
Inside Gaming: Strip room rates gradually increasing, analyst says
Plus, a temporary restraining order blocking the enforcement of Arizona gaming laws against prediction markets has been ordered in U.S. District Court in Arizona.
NFL mock draft roundup: Experts predict first round before draft
The 2026 NFL Draft begins on April 23. Here are the latest expert projections for the first round from around the web.
This simple metal tube helps scientists predict drought before it happens
We’re in a perilous moment for water. But the Church Sampler is one of the many devices scientists can use help us make better decisions.
Tech businesses express optimism as report predicts continued New Mexico growth
The State of the Tech Workforce 2026 report ranks New Mexico 38th nationally with a tech labor force of more than 97,000 workers.
OTB Staff: 3 Bold Predictions for 2026-2027 Rutgers Sports
As the 2025-2026 athletic year nears its end, with just the spring sports left to play, it is time to look ahead to the 2026-2027 athletic year. We here at On The Banks will each make three bold predictions about what the next year of Rutgers sports might have in store for us. Let us […]
UFC White House adds historic $1 million fighter bonus
If the stakes at the UFC White House weren't already high enough, fighters will now get the chance to collect a $1 million bonus when the event rolls around on June 14. Upon the announcement of the event, there was excitement in the air with many predicting who they believed would feature on the ...
Masters action shows ongoing sports-betting growth of prediction markets
Sports books are in danger of taking a backseat to prediction markets.
Cubs vs. Phillies prediction, odds, line, time: 2026 MLB picks for Monday, April 13 from advanced model
SportsLine's model reveals its Chicago Cubs vs. Philadelphia Phillies best bets and MLB picks today
Prediction markets have exploded. What can they tell us about Oregon’s hottest primary race?
The state of the Republican gubernatorial primary has been hard to divine, but gamblers have thoughts.
Iowa State Fair hints at expanded entertainment lineup ahead of reveal
DES MOINES, Iowa — The Iowa State Fair is not just another summer tradition this year. It is shaping up to feel bigger, louder, and more unpredictable as it ties into the nationwide America 2…
Iowa State Fair hints at expanded entertainment lineup ahead of reveal
DES MOINES, Iowa — The Iowa State Fair is not just another summer tradition this year. It is shaping up to feel bigger, louder, and more unpredictable as it ties into the nationwide America 250 celebr...
Braves vs. Guardians prediction, odds, time: 2026 MLB picks for Sunday Night Baseball from advanced model
SportsLine's model reveals Atlanta Braves vs. Cleveland Guardians picks, MLB best bets for Sunday Night Baseball
Sutton's predictions: Sunderland v Tottenham
What's Roberto de Zerbi going to do with Tottenham and how will he set them up? The whole situation is just odd, with De Zerbi securing a five-year contract despite only hanging around for more than two years at one of his previous eight clubs. Sunderland's form at the Stadium of Light has dipped a bit since they lost their unbeaten home record against Liverpool in February, but the Black Cats will be buoyant after beating Newcastle last time out.
Hungarians head to polls in crucial election
Pollsters predict a record turnout as EU future is likely at stake.
Politics: Iowa gubernatorial race shifts to 'toss-up'
The nonpartisan political prediction source Cook Political Report has classified Iowa’s 2026 gubernatorial race as a “toss-up” ahead of this November’s midterms.
2026 Masters odds: Surprising PGA picks, predictions into Round 4 by golf model that nailed 16 majors
Mike McClure's model has simulated the final round of the Masters 10,000 times and revealed golf picks for Augusta National
How aggressive breast cancer turns off the immune system
Researchers are launching a new project to crack the mystery of aggressive breast cancer, where predicting disease progression remains a major hurdle. By studying how tumors interact with and suppress the immune system, scientists aim to identify new biomarkers that reveal how the cancer evolves. Using real patient samples, the team hopes to turn earlier discoveries into practical clinical tools. The goal: more precise, personalized treatments that can outsmart even the most dangerous tumors.
The Universe is expanding too fast and scientists still can’t explain it
A major international effort has produced an ultra-precise measurement of the Universe’s expansion rate, confirming it’s faster than early-Universe models predict. By linking multiple distance-measuring techniques, scientists ruled out simple errors as the cause of the discrepancy. The persistent “Hubble tension” now looks more real than ever. It could mean our current model of the cosmos is incomplete.
Prediction: A Cheaper EV Could Be a Game Changer for Tesla's Business
The company hasn't said its last word.
“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.
Tyson Fury vs. Arslanbek Makhmudov odds, prediction: Picks for April 11 fight from proven boxing expert
Expert Josh Nagel reveals his boxing best bets for the April 11 Tyson Fury vs Arslanbek Makhmudov bout
Burnley XI vs Brighton – Predicted lineup and team news
Burnley host in-form Brighton today, aiming to narrow a ten-point gap to Premier League safety.The Clarets have moved closer to relegation after winning only once in 22 league games. Manager Scott Par...
Barcelona vs Espanyol – Predicted lineup and team news
Barcelona will look to make it seven La Liga wins in a row when they host Espanyol at the Camp Nou on Saturday.The Catalan giants have dominated this fixture in recent years — Espanyol haven’t bea...
Juventus predicted lineup and team news vs Atalanta
Juventus make the trip to Bergamo this Saturday night to take on Atalanta in Serie A.Only four points separate the two sides — Juventus sit fifth with 57 points from 31 games, while Atalanta are sev...
UT Dallas researchers using body movement to predict mental health crises
Microsoft makes it easier for Windows Insider testers to get new features
Windows Insider builds remain confusing, but they should be more predictable.
White House warns staff against insider trading on prediction markets
The White House recently warned staff not to engage in insider trading on prediction markets and other platforms, according to a White House official.
Senator contacts leagues over 'gambling's ugly takeover'
As prediction markets expand partnerships with major U.S. sports leagues, a U.S. senator sent letters to six league commissioners Thursday raising concerns about "gambling's ugly takeover of sports."
NBA player prop picks, odds, predictions, free best bets: Model includes Deni Avdija on Friday, April 10
The SportsLine model has simulated every NBA game 10,000 times and revealed its best player prop bets
Interview: Rob McClure on the Unsettling Reality of SPARE PARTS – “This Isn’t Science Fiction. It’s Just Science.”
“Experimenting on yourself is insane. An experiment on a single person, with no controls, tells you nothing. It is incapable of predicting anything.” Or… is it? Such is the premise of Spare Parts, the darkly comedic and deeply twisted new drama currently shaking up Theatre Row through April 30th — and subverting our standards of what …Read more
White House Warned Staff Not to Engage in Insider Trading Amid War With Iran
The directive came amid a surge of suspiciously well-timed trades on oil and prediction markets just ahead of crucial moments in the conflict.
Is the Options Market Predicting a Spike in Tencent Music Entertainment Stock?
Investors need to pay close attention to TME stock based on the movements in the options market lately.
When and why political leaders polarise rhetoric: Evidence from Twitter/X
Standard theories of electoral competition predict that politicians should converge toward the centre as elections approach. This column challenges this prediction, using 3.4 million tweets from 367 political leaders across 21 Western democracies. Rhetorical polarisation between populist and non-populist leaders rises sharply in the two quarters before an election and reverts to average levels once it is over. Both groups adopt a more emotionally direct communication style as elections near, but they increasingly talk about different topics. The findings suggest that social media turns electoral campaigns into an engine of political polarisation.
Recruit of the Week: A 300-pound limbo champion? Meet college football's next elite LT, Mark Matthews
Miami currently holds a 247Sports crystal ball prediction for the elite offensive line prospect, with LSU, Texas A&M and Georgia all in Matthews' final four as well
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
2026 Masters odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 16 majors
Mike McClure's model has simulated the Masters 2026 10,000 times and revealed golf picks for Augusta National
2026 Masters odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 16 majors
Mike McClure's model has simulated the Masters 2026 10,000 times and revealed golf picks for Augusta National
From The Sports Desk: MLB predictions
The NBC News sports team forecasts the MVP, the Cy Young and Rookie of the Year races for the 2026 season.
Physicists zero in on the mass of the fundamental W boson particle
When fundamental particles are heavier or lighter than expected, physicists' understanding of the universe can tip into the unknown. A particle that is just beyond its predicted mass can unravel scientists' ...
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
College basketball transfer portal opens; Cade Horton to miss season; Masters player rankings, predictions
Plus stay-or-go NBA Draft decisions that will define college basketball's offseason
Polygon Labs in Talks to Raise Up to $100 Million for Payments Business
Polygon Labs, developer of the blockchain that underpins prediction market Polymarket and other crypto platforms, is in early talks with investors to raise as much as $100 million to build a new stablecoin payments business, according to people familiar with the talks. The funding discussions ...
2026 Masters odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 16 majors
Mike McClure's model has simulated the Masters 2026 10,000 times and revealed golf picks for Augusta National
2026 Masters odds, picks: Scottie Scheffler, Rory McIlroy predictions by model that nailed 4 straight winners
Picks for 2026 Masters golfers and the Masters favorite to win ahead of the year's first major at Augusta National
2026 Masters odds, picks, field, date: Surprising PGA predictions from golf model that's nailed 16 majors
Mike McClure's model has simulated the Masters 2026 10,000 times and revealed golf picks for Augusta National
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.
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