Mention Lucy Letby and you're likely to get a strong reaction. The nurse was sentenced to spend the rest of her life in prison for murdering seven babies and attempting to murder seven more. But though two juries have convicted her, and she has twice been denied permission to appeal, it seems everyone has an opinion on whether she really did it. She has become the centre of an information war. The name Lady Justice Thirlwall, however, attracts less recognition. She's the judge who's been tasked with examining how Letby was able to commit her crimes at the Countess of Chester Hospital in 2015-2016, and whether her managers should have responded differently when suspicions about her were raised.

Follow this author 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. The extent to which the pioneering venture capitalist Sarah Guo believes in the promise of AI can be summed up in a stunning sentence: "Yes, I think we can solve disease." That is to say, the 37-year-old isn't simply one of her generation's savviest and most forward-thinking investors--she's a true believer.

A Border Force officer and his handler who worked for Chinese intelligence in the UK have been jailed. Chi Leung Peter Wai, 40, was sentenced to 10 years and Chung Biu Bill Yuen, 65, given an eight year term after being found guilty of assisting a foreign intelligence service, an offence under the National Security Act. Wai used his position as a Border Force officer to access to the Home Office computer system to track Hong Kong dissidents in the UK, was also convicted of misconduct in public office. The judge Mrs Justice Cheema-Grubb told the men that their actions threaten the sovereignty of the state during sentencing remarks at the Old Bailey on Thursday. The dual Chinese-British nationals were found guilty after a trial last month.

Is Cognitive Dissonance Actually a Thing? In 1934, an 8.0-magnitude earthquake hit eastern India, killing thousands and devastating several cities. Curiously, in areas that were spared the worst destruction, stories soon spread that an even bigger disaster was on its way. Leon Festinger, a young American psychologist at the University of Minnesota, read about these rumors in the early nineteen-fifties and was puzzled. Festinger didn't think people would voluntarily adopt anxiety-inducing ideas. Instead, he reasoned, the rumors could better be described as "anxiety justifying." Some had felt the earth shake and were overwhelmed with fear. When the outcome--they were spared--didn't match their emotions, they embraced predictions that affirmed their fright.

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Traditional machine learning relies on explicit models and domain assumptions, limiting flexibility and interpretability. We introduce a model-free framework using surprisal (information theoretic uncertainty) to directly analyze and perform inferences from raw data, eliminating distribution modeling, reducing bias, and enabling efficient updates including direct edits and deletion of training data. By quantifying relevance through uncertainty, the approach enables generalizable inference across tasks including generative inference, causal discovery, anomaly detection, and time series forecasting. It emphasizes traceability, interpretability, and data-driven decision making, offering a unified, human-understandable framework for machine learning, and achieves at or near state-of-the-art performance across most common machine learning tasks. The mathematical foundations create a ``physics'' of information, which enable these techniques to apply effectively to a wide variety of complex data types, including missing data. Empirical results indicate that this may be a viable alternative path to neural networks with regard to scalable machine learning and artificial intelligence that can maintain human understandability of the underlying mechanics.

Karen Price was just 15 when she vanished in 1981 and, had it not been for a chance discovery by two builders, her body might never have been found. Because no-one was looking for her. Dubbed Little Miss Nobody, Karen had not been seen for eight years when her skeletal remains, wrapped in a carpet, were uncovered by two unsuspecting builders in Cardiff city centre on 7 December 1989. Her body, found in a shallow grave outside a basement flat on Fitzhamon Embankment, was so badly decomposed it was impossible to establish the cause of her death. Now, more than 40 years on and after the release of her killer, a new documentary has examined how police put together the jigsaw to solve the killing of a teenager known to no-one and how it involved groundbreaking methods to bring two men to justice.

SGMSE [1] solves a stochastic differential equation with a learned score network, while the Schr odinger Bridge (SB) [3, 4] casts speech enhancement as an optimal transport problem. These approaches often outperform predictive baselines in terms of perceptual quality and robustness [2, 10]. A key limitation, however, is the emergence of generative artifacts. Unlike predictive models, which mainly distort or suppress existing speech, diffusion-based SE can "hallucinate" new content. Artifacts include phonetic errors such as insertions or substitutions, spurious breathing or hissing, robotic tones, and high-frequency attenuation [10]. These effects are most pronounced at low SNR as shown in Figure 1, where uncertainty drives the model to generate plausible but incorrect phonetic structures, leading to poor ASR performance despite high PESQ or STOI scores [9]. Existing metrics fail to fully capture these errors: intrusive metrics emphasize energy-based distortions at the signal-level, while non-intrusive predictors favor naturalness and overrate generative outputs. Complementary measures such as Levenshtein phoneme distance (LPD) and hallucination error rate (HER) have been proposed to address this gap [11, 12].

Samantha posted her story on TikTok and shared the scenario on a private Facebook group; many women responded--including her date's wife. Ultimately, as a result of this conversation, Samantha decided to report his profile to Hinge. The next day, the company contacted her to let her know it would be deleting his profile. Mandy and Samantha were pleased with Bumble's and Hinge's swift action to take down the profiles of the men they had matched with--but the experience was indelible. Neither of them plans to use dating apps again.

A new brain teaser reveals the most respected aspects of your personality. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Hidden in the picture is a lion, panther and bunch of dandelions, and the one you see first means you are either a natural-born leader, a problem solver or have a strong sense of conviction. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Did you see a lion, panther or dandelions first?