Its first rollout includes over 650 long-form narrated articles. Spotify is expanding its offerings with a pretty wide selection of narrated long-form magazine articles from several publications that are most likely already familiar to you. The audio streaming service has announced that it's adding over 650 long-form articles to its audiobook library. While all the pieces it added are in the English language only, they will be available in all of Spotify's regions where audiobooks are available. The articles included in this rollout include pieces from and .

AI-generated vloggers like Chloe VS History (left) and Nova VS History are, their creators say, 'taking an already-proven format and applying it to history' AI-generated vloggers like Chloe VS History (left) and Nova VS History are, their creators say, 'taking an already-proven format and applying it to history' The content creators behind channels like Chloe VS History are using AI tools to'bring history to life in a really visceral way' "I have just arrived in Tudor London, 1536," a young woman in a green puffer jacket tells the camera. "I'm going to check in at my room in the inn, get into the market. Then, later I am meeting the actual king - yep, Henry VIII - in person." On YouTube and other social platforms, users are flocking to watch AI-generated "history influencers", characters that vlog their travels to historical settings. One of the most popular channels is Chloe VS History, with more than 610,000 Instagram followers and 15m views on YouTube.

There's a mad dash to automate the world's most hated calls. You'll hear from an AI debt collector sometime soon. She introduced herself as Eve, but Ben knew right away that the voice on the other end of the line was a bot. She also knew how much money he'd owed a former landlord ($266). She didn't seem to know that he'd settled with a collection agency five months prior. Eve said she was an AI agent from ProCollect and was calling to collect a debt.

Here's Exactly How That Happened The definitive story of how Claude Code and OpenClaw kicked off computing's biggest transformation possibly ever. "Hi, my name is Peter, and I'm a Claudeholic." It was August 2025 and Peter Steinberger was addressing a meetup in London called Claude Code Anonymous. Steinberger and some fellow addicts had arranged the event to network with people like themselves--techies swept up by coding tools such as Anthropic's paradigm-busting Claude Code. "I dedicate pretty much all my waking time to this, yet it doesn't feel enough," he told the gathering in a cozy, brick-walled room. A few months later, Anthropic released a new version of Claude Code, and the ranks of Claudeholics exploded . Called Opus 4.5, it could handle more complicated programming tasks, retain much more in its memory, run for many hours on end, and manage a team of AI subagents. Anthropic has what it describes as a "notoriously difficult" take-home exam for prospective engineering hires; in a head-to-head comparison of those people and its models, Anthropic claimed that Opus 4.5 "scored higher than any human candidate ever," which "raises questions on how AI will change engineering as a profession."

A new AI-powered avatar of Black Sabbath singer Ozzy Osbourne could make its first UK appearance in Birmingham. Osbourne's wife Sharon and son Jack announced plans for the hyper-real version of the Birmingham-born singer at an expo in the US last week. Talking to Ed James on BBC Radio WM, she said that plans for the avatar were brilliant. I've seen the tests that they've done of Ozzy and you can see every pore on his face, his beard's coming through, it's that detailed, she said. Osbourne died in July aged 76, less than three weeks after he had performed at Villa Park with Black Sabbath.

Graham Platner, Maine's presumptive Democratic Senate nominee, embraced democratic socialism at a Bernie Sanders rally, condemning Sen. Susan Collins and U.S. support for Israel.

The Tokyo District Court on Monday found two former executives of artificial intelligence developer Alt guilty of window dressing in violation of the financial instruments and exchange law. The Tokyo District Court on Monday found two former executives of Japanese artificial intelligence developer Alt guilty of window dressing in violation of the financial instruments and exchange law. Former executive officer Katsuya Asai, 46, and former treasury and accounting division chief Takayuki Ariizumi, 53, were both sentenced to three years in prison, suspended for five years. The Tokyo-based company was fined ¥300 million ($1.89 million). Noting that fictitious sales at the firm reached about ¥11 billion in total, Judge Shoji Miyata said, "The window-dressing rate was extremely high, and the company achieved a stock listing that should not have been approved."

Greg Sankey makes it clear that SEC didn't start the 16-team CFP format discussion, that's on the Big Ten Emmanuel Acho says it was'pretty stupid' for Jaxson Dart to introduce President Trump Lincoln Riley claims USC was'snaps away' from the playoff, says he's a better coach now than when at Oklahoma Notre Dame's Josh Yago delivers Memorial Day salute during anthem before lacrosse championship game Dak Prescott reunites with ex-fiancée Sarah Jane Ramos to celebrate daughter's first birthday Celtics guard Jaylen Brown challenges ESPN's Stephen A Smith to a debate at Harvard or MIT Wyndham Clark adds to his funky resume, TPC Craig Ranch slander and LIV Golf's pitch to new investors Unearthed fan video shows who Kyle Busch really was, NASCAR's darkest hour & Bubba Wallace's'Rowdy' story California mom speaks with compassion but brutal honesty about presence of trans athlete in daughter's sport Curt Cignetti jokes he had to'coach the hell out' of undefeated Hoosiers to be Indy 500 pace car driver A screenshot has WNBA fans asking: did a player endorse a threat toward Caitlin Clark? MLB reporter Tricia Whitaker hit with line drive during Orioles' game Brit Hume: A Trump endorsement'repeatedly' gives candidates a leg up Democrats' 2028 presidential hopefuls face scrutiny over elitism, political attacks'The Five' reveals what fans always wanted to know about them Defense expert argues Iran has never been'so isolated' Joey Jones calls out Dem candidate Platner for'hiding behind the Purple Hearts' of fellow vets Trump doesn't want Iran to become his Afghanistan: Mike Sarraille Any Iran deal will be judged by'how much it cost' to secure, ex-CIA station chief says Dr Rebecca Grant: Iran has'no place to go,' will have to sign a deal Pope Leo XIV calls for AI to be'disarmed' in critical warning about emerging tech'Fox News @ Night' panelists evaluate Spencer Pratt's Los Angeles mayoral campaign. Milwaukee Bucks forward Kyle Kuzma is betting big that LA will change its ways. Kuzma added some intrigue to next week's nonpartisan primary, placing a $36,000 bet that former The Hills reality star Spencer Pratt will pull off an upset victory and become the next mayor of Los Angeles. With the June 2 vote just days away, Kuzma, who won a championship with the Lakers in 2020, is backing Pratt's campaign.

This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world applications exist to this one sample per client setup up including learning from fitness trackers, data/app usage aggregators, body-worn sensing devices, and daily event monitors to name a few. When a client has only one sample, the standard federated learning paradigm breaks down as a local update based on that single point is far from being useful, especially in the earlier rounds for estimation of the model coefficients. This utility is further weakened by the privacy-inducing noise applied at every round. This work caters to this problem to enable such clients to collaboratively contribute to effectively learn a global model without leaking the privacy of their data. The proposed approach injects a single, carefully calibrated noisy perturbation to transform the sample at each client, followed by a post-processed representation which is shared with the server. These representations aggregated at the server are processed to obtain an unbiased gradient update that in expectation matches the non-private centralized gradient while preserving data privacy. This approach is different than traditional private federated learning, where the communication payloads involve model coefficients as opposed to privately transformed data samples. This method enables devices with extremely limited data to collaborate and learn accurate, privacy-preserving models without requiring large local datasets or sacrificing individual privacy.

Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice. However, the two communities often evaluate models under markedly different conditions. Methodological work typically relies on semi-simulated benchmarks and metrics that require counterfactual outcomes, whereas real-world applications rely on observable metrics based on ranking or test outcomes. Despite the well-known gap between methodological progress and practical deployment, the relationship between these evaluation regimes has not been examined systematically. We conduct a large-scale empirical study of treatment effect evaluation across standard semi-simulated benchmark families and real-world datasets. Our benchmark covers meta-learners paired with multiple base learners, as well as specialized causal machine learning models. We evaluate these methods using observable metrics common in application-oriented literature, alongside counterfactual metrics commonly used in methods papers. Our results reveal two complementary gaps. First, counterfactual metrics do not reliably recover the estimators preferred by observable metrics, even on the same semi-simulated benchmarks. Second, rankings obtained on semi-simulated benchmarks do not transfer to real datasets. We further find that simple meta-learners with strong base models are consistently competitive, in contrast to specialized causal models. Overall, our findings suggest that progress in treatment effect estimation research should not be assessed solely through counterfactual metrics and semi-simulated benchmarks, but it would benefit from incorporating observable metrics and real-data validation.