HM Revenue and Customs has announced a 10-year, £175m deal with the British tech firm Quantexa to provide AI-powered technology to help improve its performance. Quantexa says its systems will combine data collected by HMRC with external sources to help the tax office identify incidents of fraud and fix unintentional errors more quickly. Its tasks will include helping HMRC to assist customer service staff, as well as to identify hidden networks of companies and individuals masking fraudulent activity. Public dissatisfaction with HMRC performance has crept up in recent years, according to government figures. A Freedom of Information request made by the campaigners at the Contentious Tax Group found there were more than 93,000 complaints made about the department in 2024-2025 .

Trump's hidden five-tap code in handshake with Xi... and the tell-tale'bullfrog smile' that betrayed the president How Kylie Kelce REALLY feels about Taylor Swift after her foul-mouthed wedding rant: Insiders reveal stark'differences' between the sisters-in-law... and truth about'feud' What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Xi greets Trump with ominous warning about risk of war between US and China: 'Beware the Thucydides Trap' Inside Eric Swalwell's marriage implosion: Disgraced Democrat nowhere in sight at family home as his furious wife appears without her ring and delivers ultimate insult Home Depot and Lowe's use sneaky cameras in theft crackdown - but honest customers are the real victims Buster Murdaugh's explosive reaction as his father's murder conviction is overturned: Insiders reveal all about his secret new life... and jailhouse calls with Alex Inside Carrie Underwood's'grounded' and'traditional' home life on her 400-acre Tennessee farm Grotesque new Michael Jackson allegations raise questions about his accusers so taboo they're almost impossible to ask... but we must: MAUREEN CALLAHAN Kylie Jenner and Timothee Chalamet's mortifying relationship secrets exposed: Her'jealousy'... his pleas for'space'... and why he's now finally'on board' with a proposal Beautiful young mom appeared to have it all. Now her two toddlers are dead after falling into a pool while on COCAINE... and her own parents allegedly made very troubling comments about her Walmart axes 1,000 workers as white-collar jobs bloodbath reaches America's biggest private employer Grief author Kouri Richins gives 40-minute rant about love and calls husband's poisoning murder a'tragedy' as she learns fate in Moscow Mule slaying... and sends deranged message to her sons This miracle drug rapidly reversed my balding. It wrecked my sex life... but a microdosing hack gave me my libido and my hair back MORE: FBI files reveal reports of'four-foot tall' beings emerging from UFOs Scientists have announced a groundbreaking discovery lurking beyond our solar system that rewrites humanity's understanding of the cosmos. A new study led by Princeton University in New Jersey identified more than 10,000 new possible planets trillions of miles from Earth. That included at least 11 worlds described by scientists as'super-Earths.'

MUFG Bank, Mizuho Bank and Sumitomo Mitsui Banking are all likely to gain access to Anthropic's artificial intelligence model, Mythos. Japan's three megabanks are set to secure access to Anthropic's artificial intelligence model, Mythos, according to a person familiar with the matter, after its limited release last month sparked fears of a new age of cybersecurity risks. MUFG Bank, Sumitomo Mitsui Banking Corp. and Mizuho Bank are all likely to gain access to the artificial intelligence model developed by the U.S. firm, the person said, asking not to be identified because the information is private. The planned access was earlier reported by Nikkei. The move comes as financial institutions around the world grow alarmed about the risks created by Mythos, which has an unprecedented ability to detect software vulnerabilities. That has raised concerns that hackers could use Mythos to disrupt critical infrastructure, and access has so far been limited to a small number of U.S. companies and organizations.

Tech founder Jesse Proudman is leaving Washington as the state’s new 9.9% millionaire tax takes hold, warning of a looming "tax flight" to states like Texas.

Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has extended conformal prediction to online and sequential settings, existing methods typically focus on a single coverage level and do not ensure consistency across multiple confidence levels. In many real-world applications, such as weather forecasting, macroeconomic prediction, and risk management, different users operate under heterogeneous risk tolerances and require calibrated uncertainty estimates across a range of coverage levels. In such settings, it is desirable to produce prediction sets corresponding to different coverage levels that are nested and valid simultaneously. In this paper, we propose two novel online conformal prediction methods that output \emph{nested prediction sets} across a range of coverage levels, enabling simultaneous uncertainty quantification across the entire risk spectrum. Beyond interpretability, jointly estimating multiple coverage levels is known to improve statistical efficiency in classical quantile regression by enforcing non-crossing constraints and sharing information across quantiles. Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets. Empirical results on synthetic and real-world datasets, including applications in forecasting tasks with heterogeneous risk requirements, demonstrate that our method achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency compared to existing online conformal baselines.

Single-arm trials are an important study design for evaluating drug efficacy and safety without enrolling patients into a control arm. Although they do not provide the gold-standard evidence of randomized controlled trials, they are increasingly used in clinical development as they offer an efficient, ethical, and practical alternative. A wide variety of approaches can be used to construct control comparators and estimate treatment effects, from fixed comparators informed by clinical knowledge to data-based and model-based patient-level comparators, also known as synthetic controls. Powerful and flexible machine learning models can allow outcome-model-based synthetic controls to overcome key limitations of direct data-based approaches, yield more robust estimates of treatment effects, and provide a principled way to incorporate corrections or encode additional assumptions when external data are not directly comparable. In this work, we argue that outcome-model-based synthetic control arms are an important tool for single-arm trials. We focus on digital twins, personalized predictions of disease progression generated from machine learning models trained on historical datasets, which naturally leverage these flexible approaches. We review doubly robust estimators, present power and sample size formulas, and discuss trade-offs in selecting historical data for training and analysis. We also outline practical considerations for deploying digital twins within the framework of recent FDA draft guidance on the use of artificial intelligence in drug development. Finally, we reanalyze data from trials in amyotrophic lateral sclerosis and Huntington's disease to demonstrate the proposed methods.

While deep learning has revolutionised inverse problems, its safe deployment is hindered by three primary reliability concerns: hallucinations, instabilities, and performance volatility [48]. Hallucinations manifest as high-fidelity features that are factually false; instabilities reflect heightened sensitivity to measurement noise; and performance volatility refers to significant fluctuations in reconstruction quality across the data, yielding high-fidelity results for some samples while failing on seemingly similar images. In many applications, the risk of generating realistic but unfaithful content can impede the safe deployment of AI methods for inverse problems. The choice of "hallucinate" as the Cambridge Dictionary's word of the year in 2023 illustrates this open problem [53]. The problem of AI hallucinations persists, as the Financial Times [44] highlighted that, "AI hallucinations haunt users more than job losses." A first step toward training AI methods that do not suffer from hallucinations is the assessment and identification of hallucinated outputs. Consider the inverse problem of recovering xfrom noisy measurements y " Fpx,eq, x PM1 ĂX, e PEĂY, (1.1)

Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences. Their denoisers have a special structure absent from generic continuous diffusion models: each block of the denoising mean is a posterior marginal distribution over the clean token at that position. Standard DDPM-style samplers collapse these marginals to a single conditional-mean endpoint and bridge toward this simplex-valued point, which is generally not a valid one-hot sequence. We argue that the natural sampler for an FLM is instead posterior-predictive. At each reverse step, we sample a clean one-hot endpoint from the factorized posterior defined by the FLM token marginals, and then sample the next continuous state from the analytic Ornstein--Uhlenbeck bridge conditioned on that endpoint. The method is training-free, uses the same model evaluations as standard sampling, and gives a principled interface for token-level decoding controls such as temperature scaling and nucleus truncation. We show that, under exact posterior marginals, the endpoint approximation error is exactly the conditional multi-information among token positions. The induced one-step bridge kernel preserves all token-wise posterior-predictive marginals and loses only the residual cross-position dependence. Finally, we prove a Girsanov path-space comparison showing that the marginal-conditioned bridge has a no-larger denoising-error term than the frozen conditional-mean bridge, with strict improvement whenever intermediate coordinate-wise bridge observations reveal additional information about the clean token. Experiments with FLMs show that the sampler improves the quality--diversity tradeoff. Code is available at: github.com/imbirik/mcb.

If this hasn't been said before, it should've been -- you can't hide in the bushes at a bachelorette pool party Shakira cranks up the heat with a World Cup song that has people dancing, buy Elvis' rhinestone jock & BBQ UCF graduates clobber commencement speaker with boos after she says AI is the'next Industrial Revolution' Hang gliding Lookout Mountain: What it's really like to be aero-towed 1,700 feet above Georgia Paige Spiranac and her mom stun the internet, Lane Kiffin's incredible shot at Ole Miss & the NFL did it again Maggie Sajak appears at Savannah Bananas game as Jackson Olson's girlfriend, e-bike near death & MEAT! Mike Pompeo: I've never seen anyone colder, more ruthless than Xi Jinping Trump to press Xi to'open up' China as tech CEOs join key summit South Carolina AG on overturned Murdaugh conviction: 'We have time to try him again' Former CDC director says'outside scientists' might have influenced COVID-19 origins findings Dr. Fauci's role in COVID cover-up was'INTENTIONAL,' CIA whistleblower says CIA calls COVID whistleblower hearing'political theater' in new statement Sen. Moreno warns Chinese cars pose data risks, could devastate US auto industry Olivia Dunne and her Baywatch co-stars are gearing up for a big season while Miller Lite continues to raise the bar. Fox News Flash top sports headlines are here. Check out what's clicking on FoxNews.com. We're halfway to June, somehow, and that means ... well, it means very little. It's a pretty slow(ish) time of year, which is fine with me.

The US Department of Homeland Security, in collaboration with the Defense Research and Development Canada, is looking to send autonomous drones and vehicles along the US-Canada border this fall, testing which products can stream surveillance video and sensor data between the two countries using commercial 5G networks. A new DHS call for participants frames the experiment, known as ACE-CASPER, as a multiday exercise "simulating a national emergency response scenario," with drones and ground vehicles relaying live feeds to a bi-national command-and-control center as they cross the border. Vehicle autonomy, the document notes, is secondary to its primary aim: demonstrating "resilient, persistent 5G communications." DHS and DRDC did not immediately respond to a request for comment. Scheduled for November, the tests would be the first joint US-Canada cross-border technology experiment along their shared border in nearly a decade.