The widespread adoption of Artificial Intelligence (AI) is likely to displace people from jobs in a similar way seen during the Industrial Revolution, the governor of the Bank of England has said. Andrew Bailey said the UK needed to have the training, education, [and] skills in place so workers could shift into jobs that use AI. He told the BBC Radio 4's Today programme people looking for a job would find securing employment a lot easier if they had such skills. However, he warned that there was an issue with younger, inexperienced professionals finding it difficult to secure entry-level roles due to AI. We do have to think about, what is it doing to the pipeline of people?

Revealed: Chilling text NASCAR star Greg Biffle's wife sent to her mom just minutes before tragic plane crash'Old age' doesn't kill us... scientists reveal true causes of death Immutable: I can't get enough of Melania, the Real Housewife of Washington, says JAN MOIR The tiny diet change that brought down my sky-high cholesterol WITHOUT statins or drugs. Mike was told he risked a heart attack or stroke. CNBC anchor who slammed Trump's tariffs as'insane' stunned live on air as inflation figures send shockwaves through Wall Street Dramatic bodycam video shows moment suspected kidnapper is arrested after 40 years on the run... as her neighbor thinks arrest is a joke Rob Reiner's'petrified' parting words about son Nick at Conan O'Brien party... and why his haunted A-list friends can't stop talking about it Reiner family bombshell as insiders reveal who is paying for Nick's celebrity lawyer... their secret motive... and who will REALLY inherit $200m fortune Doctors said my hip pain was just tendinitis from sitting all day at work. The real cause may kill me... they had left it far too late Bondi hero is handed $2.5million cheque in his hospital bed - as hero asks unbelievable question Pete Davidson is a dad! Kim Kardashian's ex welcomes first child with model girlfriend Elsie Hewitt Mica Miller's pastor husband is indicted for shocking acts before his wife was killed days after filing for divorce Transportation Secretary Sean Duffy's daughter slams TSA as'unconstitutional' after she was subjected to a pat down for refusing to go through the body scanner Jeffrey Epstein attended dinner with tech billionaires three years after he was convicted of sex crimes - as new photos of the event are released from pedophile's estate Terrifying maps break down exactly who is at risk of new'super flu' exploding across America... as doctors reveal symptoms to really worry about READ MORE: Blind psychic Baba Vanga's world-changing 2025 prophecy feared to occur in just DAYS A blind psychic, who allegedly foretold 9/11 and the Covid pandemic, shared several world-changing prophecies for 2026 before her death nearly 30 years ago. Baba Vanga was a Bulgarian mystic and clairvoyant who became a cult figure among conspiracy theorists after several of her eerie pronouncements were proved true.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Three people, including two crew members of a cargo vessel, were killed in overnight Ukrainian drone attacks on the Russian port of Rostov-on-Don and the town of Bataysk in the country's southern Rostov region, local governor Yury Slyusar said. Russian strikes near Ukraine's Black Sea port of Odesa killed a woman in her car and hit infrastructure.

Proven techniques to escape frustration AI customer service loops, including magic words and confusion tactics that force human transfers.

Generalizing causal knowledge across diverse environments is challenging, especially when estimates from large-scale datasets must be applied to smaller or systematically different contexts, where external validity is critical. Model-based estimators of individual treatment effects (ITE) from machine learning require large sample sizes, limiting their applicability in domains such as behavioral sciences with smaller datasets. We demonstrate how estimation of ITEs with Treatment Agnostic Representation Networks (TARNet; Shalit et al., 2017) can be improved by leveraging knowledge from source datasets and adapting it to new settings via transfer learning (TL-TARNet; Aloui et al., 2023). In simulations that vary source and sample sizes and consider both randomized and non-randomized intervention target settings, the transfer-learning extension TL-TARNet improves upon standard TARNet, reducing ITE error and attenuating bias when a large unbiased source is available and target samples are small. In an empirical application using the India Human Development Survey (IHDS-II), we estimate the effect of mothers' firewood collection time on children's weekly study time; transfer learning pulls the target mean ITEs toward the source ITE estimate, reducing bias in the estimates obtained without transfer. These results suggest that transfer learning for causal models can improve the estimation of ITE in small samples.

Intestinal parasites that still plague us today were all over Roman Britain. Breakthroughs, discoveries, and DIY tips sent every weekday. The soldiers guarding the Roman Empire's northwestern frontier had a real parasite problem. Scientists analyzing the sewer drains from the Roman fort Vindolanda (near Hadrian's Wall in northern England) found three types of intestinal parasites --roundworm,whipworm, and . The findings published in the journal mark the first time that has been documented in Roman Britain.

Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm and classical artificial neural networks (ANNs). In this work, we analyze the representational power of SNNs by viewing them as sequence-to-sequence processors of spikes, i.e., systems that transform a stream of input spikes into a stream of output spikes. We establish the universal representation property for a natural class of spike train functions. Our results are fully quantitative, constructive, and near-optimal in the number of required weights and neurons. The analysis reveals that SNNs are particularly well-suited to represent functions with few inputs, low temporal complexity, or compositions of such functions. The latter is of particular interest, as it indicates that deep SNNs can efficiently capture composite functions via a modular design. As an application of our results, we discuss spike train classification. Overall, these results contribute to a rigorous foundation for understanding the capabilities and limitations of spike-based neuromorphic systems.

Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survival curves across groups. Traditional approaches for identifying clusters of survival curves often rely on computationally intensive bootstrap techniques to approximate the null hypothesis distribution. While effective, these methods impose significant computational burdens. In this work, we propose a novel approach that leverages the k-means and log-rank test to efficiently identify and cluster survival curves. Our method eliminates the need for computationally expensive resampling, significantly reducing processing time while maintaining statistical reliability. By systematically evaluating survival curves and determining optimal clusters, the proposed method ensures a practical and scalable alternative for large-scale survival data analysis. Through simulation studies, we demonstrate that our approach achieves results comparable to existing bootstrap-based clustering methods while dramatically improving computational efficiency. These findings suggest that the log-rank-based clustering procedure offers a viable and time-efficient solution for researchers working with multiple survival curves in medical and epidemiological studies.

Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challenging for multivariate targets. We propose Tomographic Quantile Forests (TQF), a nonparametric, uncertainty-aware, tree-based regression model for multivariate targets. TQF learns conditional quantiles of directional projections $\mathbf{n}^{\top}\mathbf{y}$ as functions of the input $\mathbf{x}$ and the unit direction $\mathbf{n}$. At inference, it aggregates quantiles across many directions and reconstructs the multivariate conditional distribution by minimizing the sliced Wasserstein distance via an efficient alternating scheme with convex subproblems. Unlike classical directional-quantile approaches that typically produce only convex quantile regions and require training separate models for different directions, TQF covers all directions with a single model without imposing convexity restrictions. We evaluate TQF on synthetic and real-world datasets, and release the source code on GitHub.

We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constraint. Our Zero-Inflated Continuous Optimization (ZICO) approach uses node-wise likelihoods with canonical links and enforces acyclicity through a differentiable surrogate constraint combined with sparsity regularization. ZICO achieves superior performance with faster runtimes on simulated data. It also performs comparably to or better than common algorithms for reverse engineering gene regulatory networks. ZICO is fully vectorized and mini-batched, enabling learning on larger variable sets with practical runtimes in a wide range of domains.