LIV Golf Team Championships in Michigan'highly likely' to be canceled: we're'disappointed' Lynx star Kayla McBride becomes latest to speak out on WNBA 3-point contest drama: 'Half a-- invite' Arch Manning isn't backing down from expectations, and neither is Steve Sarkisian, as CFP semis aren't enough USA Today writer invokes Emmett Till, says Caitlin Clark puts Black and queer players'in danger' by flopping Smokin' Hot Charley Hull suffers a brutal quadruple bogey, USA Today gasbags & Angel Reese is back at it! First overall pick Fernando Mendoza signs guaranteed deal with Raiders, who seem to be an'arrow up' NFL club White House defends WNBA star Sophie Cunningham after she speaks out in support of protecting women's sports NHL's top American-born scorer Patrick Kane signs deal to return to Chicago Blackhawks Florida coach Jon Sumrall's cell phone comments cause fans and media members to lose their minds, he responds Shane Bieber's strong Rays splits make Toronto Blue Jays the smart first 5 innings MLB bet Maria Sharapova attacks the dog days of summer by hopping into the pool, it pays to be OSU's QB & Shatner Ukrainian drone strikes knock 40% of Russia's oil refining capacity offline A'bigger than ever' attack on Iran could be coming: Report Gordon Chang warns of China's'people's war' against the US Linda McMahon says we shouldn't be afraid of AI Stephen A Smith is only looking out for Stephen A Smith, not Ryan Clark | Don't @ Me w/Dan Dakich Dan Dakich reacts to Stephen A Smith's message about Ryan Clark being laid off by ESPN Welcome to our 32-team 2026 NFL Season Preview Series! As we count down to kickoff, we're breaking down every franchise division-by-division. Today, we're spotlighting the Atlanta Falcons in the NFC South. Each preview analyzes the team's offseason moves, coaching staff, projected strengths and weaknesses, schedule, win total and best futures bet. Atlanta missed the playoffs in 2025-26 for an eighth consecutive season and fired now-former head coach Raheem Morris afterward.

Angel Reese thanks WNBA for suspending Sandy Brondello, frames'protected species' comment as discrimination Rory McIlroy should be the last golfer to criticize others for'performative' behavior Brexton Busch wins first race since father Kyle Busch's death in emotional return to Victory Lane Caitlin Clark'cheering hard' for Argentina over Spain in World Cup final: 'I want Messi' Ella Langley turns 63-year-old former NFL head coach into'Ella Fella' I am once again begging the folks in USC's athletic department to study some Greek literature Jaxson Dart's swimsuit model girlfriend gets patriotic, golfer Hailey Ostrom takes on Lake Powell & Bigfoot Caitlin Clark's former teammate calls out WNBA for suspending Tempo head coach over'protected species' remark Christopher Nolan brings'The Odyssey' to life with groundbreaking IMAX technology WATCH: Anchor STUNNED by socialist's answer More than 200 military musicians bring America's story to life The All-Star forward finished with eight points and five rebounds in Atlanta's 93-91 victory over the Chicago Sky. Dan Dakich reacts to and fixes Angel Reese's shooting form. Angel Reese's eventful weekend ended with an injury and one of her worst performances of the season. The Atlanta Dream star left Sunday afternoon's game against her former team, the Chicago Sky, after her left leg appeared to give out as she attempted a layup early in the fourth quarter. Reese fell to the floor after missing the shot, briefly got back to her feet and then went down again.

DOGE Used AI for Housing Policy. The Government Won't Say How In response to a public records request, HUD has withheld documents about DOGE's use of AI--in part by citing a privilege that doesn't exist. Members of the so-called Department of Government Efficiency (DOGE) who were working at the Department of Housing and Urban Development (HUD) used artificial intelligence to inform policy decisions. Now, the agency appears to be denying Freedom of Information Act requests for information on the development and use of AI tools, and the way they informed policy decisions, according to documents obtained by a FOIA request by Democracy Forward, a nonprofit legal organization. Last year, WIRED reported that Christopher Sweet, who was then a third-year student at the University of Chicago, had joined the DOGE team at HUD, along with Scott Langmack, who came to DOGE from a property technology startup called Kukun. Sweet's primary focus, according to HUD employees who spoke to WIRED at the time, was on using artificial intelligence to identify agency rules for potential rescission, or contract cancellations, as part of a similar effort across the government .

The University of Chicago is banning laptops, tablets and phones for first-year law students in a sweeping AI ban aimed at preserving critical thinking in legal education.

Three of the most popular methods for training language models to reason look like three different tricks. They are not. All three adjust a single number: standard deviation, reflecting how much a prompt's sampled answers disagree. When such a model is trained, it answers each problem many times, and an automatic checker marks every answer right or wrong. The standard deviation of those marks measures the disagreement: largest when the answers split evenly between right and wrong, and zero when they all agree. Group Relative Policy Optimization (GRPO) divides by this number, GRPO Done Right (Dr. GRPO) drops the division, and Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) discards the groups where it is zero. Each is presented as its own fix, yet this paper proves they are three settings of one dial. That dial is not cosmetic: for right-or-wrong rewards, the disagreement is exactly the size of the training update, the group-standard-deviation identity. A split group teaches the most, while a unanimous group teaches nothing and falls silent. The same result says which problems deserve the most weight and how many tries each one needs. This paper confirms the intuition on a large real difficulty dataset (Big-Math) and in a controlled training run. What looks like a harmless normalization step is the dial that decides where learning happens and how strongly.

Watermarking promises a statistical trace of large language model (LLM) use, but real documents, after editing or paraphrasing, rarely arrive as purely human-written or purely machine-generated. This motivates a quantitative question beyond detection: what proportion of a document is generated from a pre-specified watermarked LLM? We study this watermark proportion estimation problem under the Gumbel--max watermarking mechanism, treating the next-token prediction (NTP) distributions as unknown and arbitrary nuisance parameters subject to a non-degeneracy condition. We compare two observation regimes: in the full observation regime, the estimator observes the pseudorandom vector and the selected token at each position; under the more popular setting of pivotal reduction, it observes only a scalar pivot, which follows a one-dimensional Uniform--Beta mixture distribution. Under pivotal reduction, we develop a Laguerre-polynomial estimator and establish a matching information-theoretic lower bound for the sample complexity. For full observation, we introduce an event-counting estimator and show a matching lower bound, yielding a substantially smaller sample complexity. As our results imply, although reducing to pivotal statistics is an elegant and widely used procedure, it is not always sample-efficient for estimating the proportion of watermarks.

We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time-$T$ solution operators of dissipative evolution equations. The analysis builds on the premise that FNOs can efficiently approximate and learn solution operators whenever these operators admit stable and accurate spectral discretizations. To formalize this idea, we introduce classes of evolution operators defined through spectral methods and derive FNO approximation bounds and polynomial sample complexity guarantees for these classes. For equations with polynomial nonlinearities, the learning rates depend primarily on the smoothness of the input space and the dimension of the physical domain. Our results hold uniformly over broad families of dissipative equations, rather than for a single fixed PDE, and apply in particular to the Navier--Stokes, Allen--Cahn, and Cahn--Hilliard equations. For equations with non-polynomial smooth nonlinearities, we prove that polynomial sample complexity still holds with rates that now additionally depend on the smoothness of the nonlinear terms and the dissipation strength. Overall, we connect classical spectral approximation theory with modern operator learning and explain when FNOs can learn nonlinear evolution operators efficiently.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Flock cameras track more than your license plate, and they're spreading fast Flock cameras track more than your license plate, and they're spreading fast You can't get a breath of fresh air ... without us knowing. Thanks to the rise of AI, a new kind of surveillance camera has rapidly proliferated across the United States. Typically referred to as automated license plate readers, or ALPRs, they're most often mounted along roadways, where they log the movements of cars which pass through their field of vision. Though various companies offer them, the most well known come from Flock Security, and the company has consequently been a lightning rod for public opinion. Shocking exactly nobody, there has been widespread public backlash to cameras that track everyone, whether or not they've been suspected of a crime.

ICE agents detain a suspect during a targeted enforcement operation in Lyons, Illinois, on 26 January. ICE agents detain a suspect during a targeted enforcement operation in Lyons, Illinois, on 26 January. 'We should be worried': report sheds light on ICE's booming arsenal of hi-tech surveillance tools Spending on government contracts with tech firms that use AI-powered tools to track immigrants has soared to record levels under Trump 2.0, report says A new report sheds light on the unprecedented growth of the US government's immigration surveillance arsenal, revealing fresh details about how spending on technology and AI tools to find and track migrants has soared to record levels during Donald Trump's second term. They found the money awarded to these firms doubled from 2024 to 2025, to just over $310m - and in 2026, that number soared to a record $513m. Researchers traced these contracts as far back as 2013, when they hovered under $50m, and found a steady increase over time - with a bigger jump over the last two years.