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Bears 53-man roster predictions: Week 2 preseason edition
With one more preseason game to go, @jacobinfante24 predicts what the Bears’ 53-man roster will look like in 2026.
The State of Politics: Wikler Predicts Democratic Wave in November
But was some of that wave in August election Republican crossovers voting for Hong?
ESPN predicts Texas A&M's CFP and bowl game destinations
ESPN has released its early postseason predictions for Texas A&M, including a first round trip to the CFP, and an appearance in the Citrus Bowl.
Larry Fitzgerald predicts ‘Broncos beatdown’ vs. Cardinals in October
Newly-minted Hall of Famer Larry Fitzgerald made a bold Week 7 prediction for the 2026 NFL season.
Jets Predicted to Cut Former Cowboys’ 1st-Round Pick
Bleacher Report's Moe Moton predicts former Dallas Cowboys DT and 2023 1st-round pick Mazi Smith will be cut by the New York Jets. The post Jets Predicted to Cut Former Cowboys’ 1st-Round Pick appeared first on HEAVY.
Conor McGregor predicts new UFC champion at UFC 330
Conor McGregor picks Ian Machado Garry to beat Islam Makhachev at UFC 330
Joe Rogan predicts retirement for Islam Makhachev after UFC 330
One of MMA's biggest voices sees Islam Makhachev hanging up the gloves after UFC 330
Your address holds clues to knowing your child's hospitalization risk
Researchers at Cincinnati Children's used local data to create a framework that predicts a child's hospitalization risk based on their address.
'Football 301' predicts win totals for the AFC West, where the Broncos look to maintain, Chargers look to take a big step and Patrick Mahomes gets help
CBS Sports predicts Florida's record in Jon Sumrall's first season
CBS Sports anticipates a three-win improvement for Florida football during Jon Sumrall's first season in Gainesville.
Israel Adesanya Predicts Jan Blachowicz vs. Navajo Stirling
UFC legend Israel Adesanya shared his prediction for the UFC Belgrade co-main event between Jan Blachowicz and Navajo Stirling.Adesanya fought Blachowicz for the UFC light heavyweight title at UFC 259 in March 2021, losing by unanimous decision. He was also a former longtime training partner of Stirling at City Kickboxing, the gym he trained at […] The post Israel Adesanya Predicts Jan Blachowicz vs. Navajo Stirling appeared first on HEAVY.
‘Not out of the woods’: Delaware doctor predicts more measles cases as the state’s outbreak rises to 5
The CDC and Delaware public health officials urge unvaccinated residents exposed to the measles virus to get the shots within three days.
Scientists look to shark DNA for the genetic signature of time
Aging leaves telltale marks on DNA, and for the first time, scientists have used those molecular signatures to create a model that predicts the age of sharks.
Zuckerberg predicts more jobs and entrepreneurship if superintelligence is widely distributed
Mark Zuckerberg pushes back against AI doom rhetoric, writing that distributed superintelligence will create more jobs and make starting businesses easier.
Scientists look to shark DNA for the genetic signature of time
Aging leaves telltale marks on DNA, and for the first time, scientists have used those molecular signatures to create a model that predicts the age of sharks. This tool, known as an epigenetic clock, was developed for the zebra shark (Stegostoma tigrinum) and could soon be expanded to more species,
This value predicts future quality of life. In Australia, it’s tanking
More than 600 scientists are sounding the alarm that our standard of living is under threat.
20 Lions on the defensive roster bubble entering training camp
Our staff predicts the fate of 20 Detroit Lions players on defense who enter training camp on the bubble.
ESPN super computer predicts every Texas Longhorns game in 2026
The Longhorns have one of the toughest schedules in the nation. But the ESPN FPI computers are very bullish on Texas.
Troubled UDFA Cowboys Rookie Predicted to Make 53-Man Roster
Bleacher Report's Gary Davenport predicts UDFA rookie tight end Michael Trigg will make the 53-man roster for the Dallas Cowboys. The post Troubled UDFA Cowboys Rookie Predicted to Make 53-Man Roster appeared first on HEAVY.
When It Comes to Fusion, Materials Matter
Fusion at high temperatures powers the sun and, if harnessed, could provide a potential source of energy here on Earth. But controlling fusion reactions has other benefits. The process also generates subatomic particles called neutrons that are used in a range of applications spanning medicine, research, and national security. Now, scientists at the University of California, Davis (UC Davis), and the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have found that the materials surrounding a fusion reaction can dramatically increase how often it occurs, particularly at low energies where fusion is rare. The study was published July 18 in Nature Communications. Their approach sets up a way to study and engineer nuclear reactions within solid materials, opening a new field of “materials-driven fusion.” Instead of designing materials just to survive the harsh conditions of fusion, researchers might be able to design materials that boost the reaction in specific conditions, similar to the way catalysts speed up chemical processes. “It gives you a new knob to turn that you didn’t have before,” said Arun Persaud, head of the Fusion Science & Ion Beam Technology group in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division. “If we understand this effect better, it opens the door to engineering new materials that would affect the fusion rate under certain conditions. Someday future progress might enable more compact and efficient neutron generators, which have all kinds of applications, like cargo screening, planetary science, and medical therapy and imaging.” In the experiment, researchers used two different methods to pack deuterium, a heavy form of hydrogen atoms that is often used in fusion, into thin foils of palladium and titanium. They then fired a beam of deuterium ions at the foils at different energies and measured how often fusion happened. Then they compared the rates from the different materials and methods with the “bare” fusion reaction (not in a material). The team found that fusion rates depended on how the deuterium was loaded into the metal foils. The biggest effect was at the lowest energies, below 2.5 kiloelectronvolts (keV), where theory predicts fusion rates drop off sharply. Instead, researchers found a surprising plateau: Some samples showed elevated fusion rates roughly a quintillion times higher than bare fusion reactions. (A quintillion is a 1 followed by 18 zeroes.) Researchers are not exactly sure why that’s happening, though they have some ideas and ways to test them. The electrons and defects within the material might partially shield repulsive electrostatic forces between deuterium nuclei, making it easier for them to get close together and fuse. Tuning the electronic structure, defects, and composition of materials could help make nuclear reactions happen more readily. “It comes down to better understanding the mechanism so that we can try to enhance it,” said corresponding author Jeremy Munday, a professor at UC Davis. “We’ve seen that we can increase fusion rates, but what is the limit? Can we bring it to lower temperatures or energies? There’s obviously a lot of interest and excitement about fusion, so if there’s something we can learn about the physics at these lower energy scales, maybe there’s something we can translate to other areas of nuclear science.” The team plans to explore a wider range of materials and continue probing the unexpected fusion plateau at lower energies. Their work establishes a reproducible experimental platform to study how solid materials influence nuclear reactions, creating a new area of research that links fusion science with materials science and chemistry. “The work shows conclusively that the material environment where fusion occurs at low temperatures is an active participant rather than a passive container,” said Cameron Geddes, director of ATAP. “That adds a new dimension to fusion research.” This work was funded by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E). ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Yu Deng Predicts the Actions of the Whole From Its Tiniest Parts
Dr. Deng, a mathematics professor at the University of Chicago, received a Fields Medal for his work on an equation describing fluid mechanics.
This Lightning Storm Never Ends. Scientists Finally Figured Out Why.
Venezuela's Lake Maracaibo flashes up to nine hours a night, 160 nights a year. A new model predicts strikes three months in advance.
Oil rises above $90 as US-Iran strikes escalate; Ryanair predicts lower fares this summer
Rolling coverage of the latest economic and financial news
New AI blood test predicts heart disease 15 years early
A new AI-powered blood test could give people a remarkably early warning of serious heart and circulation problems. Developed by researchers at the University of Hong Kong, CardiOmicScore analyzes thousands of proteins and metabolites to estimate the risk of six major cardiovascular diseases, including heart attack, stroke, heart failure, and atrial fibrillation. Unlike genetic risk scores, which remain fixed throughout life, the system captures biological changes linked to a person’s current health, lifestyle, and environment.
ESPN predicts Georgia football's most likely defeat
ESPN predicts the most likely loss on Georgia's 2026 schedule, which is ranked as the easiest in the SEC
Giants Trade Pitch Deals Disappointing Former 1st-Round Pick
Bleacher Report's Moe Moton predicts the New York Giants will trade cornerback and former 1st-round pick Deonte Banks. The post Giants Trade Pitch Deals Disappointing Former 1st-Round Pick appeared first on HEAVY.
‘Underwhelming’ 49ers’ 1st-Round Pick Called NFL’s Biggest Bust
Bleacher Report's Moe Moton predicts San Francisco 49ers WR and 2024 1st-round pick Ricky Pearsall will be NFL's "Biggest Bust" in 2026. The post ‘Underwhelming’ 49ers’ 1st-Round Pick Called NFL’s Biggest Bust appeared first on HEAVY.
UFC champion predicts quick KO in UFC 329 main event
Former two-division champion Conor McGregor plans to put on a 'masterpiece' performance in his UFC 329 return on Saturday. "The Notorious" takes on former featherweight champion and BMF titleholder Max Holloway in a rematch in the fight card's main event. It's been five years since the 37 year-old ...
Analyst Predicts Oklahoma's Best and Worst-Case Record for 2026
One analyst sees Oklahoma's ceiling at 10 wins, but warns offensive issues could lead to 6-6.
Virginia Tech football: CBS predicts every Hokies’ game for 2026
How many games do you believe the Hokies will win in 2026?
Max Holloway predicts 'fireworks' against Conor McGregor
Thirteen years ago, at UFC Fight Night 26 in Boston, future legends Conor McGregor and Max Holloway fought in a featherweight bout on the preliminary card. McGregor defeated "Blessed" via unanimous decision. It was the Irishman's second octagon appearance. The two will rematch in the UFC 329 main ...
Goldman Sachs economist predicts AI displacing 15 million jobs
Joseph Briggs of Goldman Sachs foresees AI displacing 15 million US jobs, but suggests new roles will emerge as adoption progresses.
ESPN boldly predicts Dak Prescott to join exclusive NFL club
Do you think Dak Prescott can throw for 5K?
PwC predicts: Providers, payers, pharma, medtech—all will have new to-do lists after the midterm elections. And that’s regardless of results.
With Election Day only four months away, healthcare is again emerging as a major source of idealistic campaign promises. Many of these are driven by widespread public angst. What sorts of changes can U.S. healthcare stakeholders realistically expect once the results are in?
UFC champ Sean Strickland predicts unlikely future title matchup
UFC middleweight champion Sean Strickland believes a fight between him and a training partner at Xtreme Couture is inevitable.
Steelers reporter predicts Pittsburgh will cut Will Howard
Will the Steelers cut Will Howard?
Former Eagles WR A.J. Brown Predicted to Threaten NFL Record
NFL.com's Bucky Brooks predicts former Eagles WR A.J. Brown could break the 2,000-yard receiving mark with the Patriots in 2026. The post Former Eagles WR A.J. Brown Predicted to Threaten NFL Record appeared first on HEAVY.
‘Under the Radar’ Cowboys Free Agent Predicted for Major Payday
Bleacher Report predicts Dallas Cowboys CB Cobie Durant could land a major payday in 2027 with a standout season. The post ‘Under the Radar’ Cowboys Free Agent Predicted for Major Payday appeared first on HEAVY.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
Dolphins dead last in recent power rankings
Frank Schwab of Yahoo Sports predicts a difficult 2026 for Miami.
Dalton and Tristi's NBA mock draft 4.0: Who Warriors, Kings pick in first round
NBC Sports Bay Area and California predicts who the Golden State Warriors and Sacramento Kings will pick in the 2026 NBA Draft's first round.
Reader wants to know what happened to Tennessee's football museum
David says why my football/track plan won't work. Randy revives memories of Peyton Manning wax statue. Jimmy predicts end of Josh Heupel era.
Analyst predicts 49ers star to break long-standing NFL record
Christian McCaffrey has been in the NFL long enough where he is now staring at some NFL records.
New Steelers safety shares bold prediction for 2026 season
New Pittsburgh Steelers safety Jaquan Brisker has high hopes for the 2026 season, as he predicts the defense could become a top-three unit in the NFL.
Dalton and Tristi's NBA mock draft 3.0: Who Warriors, Kings pick in first round
NBC Sports Bay Area and California predicts who the Golden State Warriors and Sacramento Kings will pick in the 2026 NBA Draft's first round.
Clemson doubt continues? Athlon Sports predicts Tigers’ 2026 finish
Clemson is seemingly outside the Top 3 in every ACC rankings yet. Are the Tigers being undervalued?
Xbox Memo Predicts over 5x Increase in Storage and Memory Costs by Holiday 2027
"While the entire industry is facing a components crisis, we believe we have been impacted more greatly than many of our peers".
Mega-bull Dan Ives shares 5 stocks to buy ahead of a coming shift in AI
Dan Ives of Wedbush Securities predicts a coming shift in the AI market that will favor a handful of stocks including Palantir and MongoDB.
Ben Roethlisberger Predicts Steelers Record Under Mike McCarthy
Pittsburgh's former quarterback expects a solid season.
Exclusive: SBA's Kelly Loeffler predicts AI will fuel Main Street growth
Despite economic fears, Americans are filing to start new businesses at near-record rates.
Tokenomics will lead to opportunities in 'edge AI,' Citrini says
Citrini Research predicts that the economics of tokenmaxxing will prompt a shift to "edge AI" that will have implications for investors.
CFB analyst provides intriguing prediction for Texas A&M's 2026 season
Popular X account CFB Home predicts Texas A&M to finish 10-2 in 2026, with both losses coming at the end of the year.
NBA writer predicts Draymond Green will decline his player option
Draymond Green has a player option for next season with the Warriors, but the 14-year veteran has not yet decided to exercise it.
BofA Says Foreign Buyers Lured by UK Yields Despite Politics
Bank of America predicts a streak of gilt purchases by foreign buyers extended through May, as some of the highest government yields in major developed nations offset political turbulence and inflation fears.
OECD predicts spate of recessions globally if Iran conflict drags into 2027
Policy forum lays out ‘prolonged disruption’ scenario in which world’s GDP falls to 2.1% this year from 3.4% in 2025
Bleacher Report's Kristopher Knox predicts another rough Browns season
Despite a remade offense one analyst believes not much will change for Cleveland in 2026.
Greene predicts ‘political revolution in America’ if US troops sent to Iran
Former Rep. Marjorie Taylor Greene (R-Ga.), a former ally of President Trump who has since sparred with him over multiple issues, on Sunday predicted “political revolution in” the U.S. if there are…
ESPN predicts Bengals will make AFC Championship
A bold claim, but one we all love.
Hayden Panettiere reflects on dating 30-year-old Milo Ventimiglia when she was 18, predicts her daughter will do the same
Hayden Panettiere is preparing for her 11-year-old daughter to date an older man after the actress dated her 30-year-old 'Heroes' costar Milo Ventimiglia when she was just 18.
Stephen A. Smith Predicts LeBron James Could 'Average 20 Assists' With One Free Agent Move
Following a sweep in the 2026 NBA playoffs, the four-time MVP LeBron James faces a massive decision.
Analyst predicts Commanders' rookie for key offensive role
Is one Washington rookie set to play a big role in 2026?
New AI tool developed by Stowers Institute and Helmholtz Munich scientists predicts how cells choose their future — helping uncover hidden drivers of development
Scientists at the Stowers Institute for Medical Research and Helmholtz Munich have developed RegVelo, a new AI framework that predicts how cells acquire their identities and identifies the genetic regulators guiding those changes. Published in Cell, the study used zebrafish neural crest development to show RegVelo can uncover early drivers of cell fate, including regulators of pigment cell formation, and then support those predictions experimentally. The researchers also applied the framework across multiple biological systems, suggesting its value extends beyond neural crest cells as a broadly useful tool for studying how cells change over time. The team says the new model could pave the way for future cell therapy treatments.
One Critical Factor Predicts Longevity Better Than Diet or Exercise, Study Finds
It's vital for almost every aspect of our well-being.
PS5 sales expected to decline due to generative AI memory drought and rising prices
Sony has released its financial results for the past fiscal year. Inside, it predicts a decrease in PS5 hardware sales in the next 12 months.
Lunchbreak: ‘Sports Illustrated’ Predicts Best Year 1 Players for All 32 Teams
Minnesota Vikings Latest News
Mamdani's latest tax pledge 'makes no sense' and will only exacerbate Florida 'exodus': former mayor
Republican congressional candidate Scott Singer predicts continued business exodus from NYC to Florida amid Mayor Mamdani's tax-the-rich policies.
A Science Policy Framework for the Climate Determinants of Health
In 2023, the World Health Organization (WHO) called climate change “the single biggest health threat facing humanity.” With warming at approximately 1.14°C above preindustrial levels and set to increase, the Intergovernmental Panel on Climate Change (IPCC) predicts with very high confidence that climate-related illness will also rise (IPCC, 2022). Under high-emissions scenarios, this science-policy body […]
A Science Policy Framework for the Climate Determinants of Health
In 2023, the World Health Organization (WHO) called climate change “the single biggest health threat facing humanity.” With warming at approximately 1.14°C above preindustrial levels and set to increase, the Intergovernmental Panel on Climate Change (IPCC) predicts with very high confidence that climate-related illness will also rise (IPCC, 2022). Under high-emissions scenarios, this science-policy body […]
A Scientist Says Humans Will Stop Aging by 2029, Here's the Technology That Could Make It Real
A futurist l predicts humanity could reach "longevity escape velocity", the point where life expectancy grows faster than we age. But is it realistic?
Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records
Nature Mental Health - Using electronic health records from over 140,000 children, the authors developed a neural network model that predicts attention deficit hyperactivity disorder diagnosis...
Youtility Decision Science Raises Seed Funding for Decision Science Platform That Predicts Customer Behavior
Strategic investment from a consortium including Silver Falcon Capital, Fyllo and National Media supports development of behavioral AI technology designed to help companies understand and predict customer decisionsRICHMOND, Va. and LONDON, April 23, 2026 (GLOBE NEWSWIRE) -- Youtility, a pioneer in behavioral AI for consumer engagement focused on why consumers make decisions, today announced the second close of its seed round, bringing total funding to $4.2M. The round was supported by a consorti
NFL insider predicts Anthony Richardson gets traded to the Steelers
Kay Adams was right on the money predicting Dexter Lawrence getting traded to the Bengals… does lightning strike twice with her prediction of the Steelers getting Anthony Richardson?
Who will the Baltimore Ravens draft? WJZ's Sports Team predicts their first-round pick.
M&C Saatchi predicts hit to sports and entertainment arms from Iran conflict
The advertising giant held off from handing shareholders a dividend as it battles ‘tough’ macroeconomic conditions.
2026 NFL mock draft: Trades galore as Patrick Mahomes gets a bodyguard, Cowboys find new face of their defense
Former NFL CB and two-time Super Bowl champion Bryant McFadden predicts all 32 first-round picks
UVA Center for Politics predicts narrow passage of redistricting referendum
The UVA Center for Politics predicts Virginians will pass the redistricting referendum.
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?
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."
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.
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.
“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.
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