In the Swiss Alps, a plan to tidy up Romansh--spoken by less than one per cent of the country--set off a decades-long quarrel over identity, belonging, and the sound of authenticity. After reformers launched Rumantsch Grischun, a standardized version of Romansh's various dialects, traditionalists denounced it as a "bastard," a "castrated" tongue, an act of "linguistic murder." Ask him how it all began, and he remembers the ice. It was a bitter morning in January, 1982, when Bernard Cathomas, aged thirty-six, carefully picked his way up a slippery, sloping Zurich street. His destination was No. 33, an ochre house with green shutters--the home of Heinrich Schmid, a linguist at the University of Zurich. Inside, the décor suggested that "professor" was an encompassing identity: old wooden floors, a faded carpet, a living room seemingly untouched since the nineteen-thirties, when Schmid had grown up in the house. Schmid's wife served, a Swiss carrot cake that manages bourgeois indulgence with a vegetable alibi. Cathomas had already written from Chur, in the canton of the Grisons, having recently become the general secretary of the Lia Rumantscha, a small association charged with protecting Switzerland's least known national language, Romansh. Spoken by less than one per cent of the Swiss population, the language was itself splintered into five major "idioms," not always readily intelligible to one another, each with its own spelling conventions. Earlier attempts at unification had collapsed in rivalries. In his letter, Cathomas said that Schmid's authority would be valuable in standardizing the language. Cathomas wrote in German but started and ended in his native Sursilvan, the biggest of the Romansh idioms: " ." Translation: "I thank you very much for your interest and attention to this problem." Schmid, the man he was counting on, hadn't grown up speaking Romansh; he first learned it in high school, and later worked on the "Dicziunari Rumantsch Grischun," a Romansh dictionary begun in 1904 and still lumbering toward completion.

The model is built to detect when crimes are being "contemplated." A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on building other state-or county-specific models. Over the past year, Elder says, Securus has been piloting the AI tools to monitor inmate conversations in real time (the company declined to specify where this is taking place, but its customers include jails holding people awaiting trial, prisons for those serving sentences, and Immigrations and Customs Enforcement detention facilities). "We can point that large language model at an entire treasure trove [of data]," Elder says, "to detect and understand when crimes are being thought about or contemplated, so that you're catching it much earlier in the cycle."

'It's going much too fast': the inside story of the race to create the ultimate AI On the 8.49am train through Silicon Valley, the tables are packed with young people glued to laptops, earbuds in, rattling out code. As the northern California hills scroll past, instructions flash up on screens from bosses: fix this bug; add new script. There is no time to enjoy the view. These commuters are foot soldiers in the global race towards artificial general intelligence - when AI systems become as or more capable than highly qualified humans. Here in the Bay Area of San Francisco, some of the world's biggest companies are fighting it out to gain some kind of an advantage. And, in turn, they are competing with China. This race to seize control of a technology that could reshape the world is being fuelled by bets in the trillions of dollars by the US's most powerful capitalists. Passengers get off a train at Palo Alto station.

Drone video shows devastation from floods in Indonesia's Sumatra NewsFeed Drone video shows devastation from floods in Indonesia's Sumatra Drone video shows widespread destruction in part of Sumatra in Indonesia, where more than 440 people have died in flooding and landslides across the country. Hundreds of others are still missing. Pope Leo says two-state is'only solution' for Israel-Palestine Netanyahu requests Israel's president grant a pardon in corruption cases

LDP and JIP lawmakers discuss Lower House seats reduction in Tokyo on Nov. 21. Prime Minister Sanae Takaichi and Japan Innovation Party (Nippon Ishin no Kai) leader Hirofumi Yoshimura agreed Monday to submit a bill during the current parliamentary session that will reduce the number of Lower House seats by 10%, fulfilling a key goal of their coalition agreement. Introducing and passing a bill to reduce the number of parliamentary seats in the lower chamber was a core demand for the JIP in exchange for agreeing to a coalition government with Takaichi and the Liberal Democratic Party. "Takaichi and I agreed to submit a seat-reduction bill during the current parliamentary session," Yoshimura said on his X account. "To ensure the bill's effectiveness -- and taking into account the opposition parties' views -- we agreed on a plan that will reduce seats by 10% in single-seat constituencies and proportional representation seats, rather than reducing seats only in the proportional representation system."

'It was extremely pornographic': Cara Hunter on the deepfake video that nearly ended her political career The Irish politician was targeted in 2022, in the final weeks of her run for office. When Cara Hunter, the Irish politician, looks back on the moment she found out she had been deepfaked, she says it is "like watching a horror movie". The setting is her grandmother's rural home in the west of Tyrone on her 90th birthday, April 2022. "Everyone was there," she says. "I was sitting with all my closest family members and family friends when I got a notification through Facebook Messenger." It was from a stranger.

Here's where things stand on Monday, December 1. The number of casualties from a Russian attack on Ukraine's Kyiv on Sunday rose to one person killed and 18 wounded, according to regional Governor Mykola Kalashnyk. In southern Kherson, at least two people were killed, and seven others were wounded in more Russian attacks, Governor Oleksandr Prokudin said on Telegram. In the Donetsk region, at least two people were killed, and five were injured in Russian attacks on Saturday, according to Governor Vadym Filashkin. In Russia, a Ukrainian drone attack killed two men in the Belgorod region, the region's operational headquarters said in a post on Telegram.

Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for each data point, provide a simple approach to uncertainty quantification. However, overparameterized mean-variance models struggle with signal-to-noise ambiguity, deciding whether prediction targets should be attributed to signal (mean) or noise (variance). At one extreme, models fit all training targets perfectly with zero residual noise, while at the other, they provide constant, uninformative predictions and explain the targets as noise. We observe a sharp phase transition between these extremes, driven by model regularization. Empirical studies with varying regularization levels illustrate this transition, revealing substantial variability across repeated runs. To explain this behavior, we develop a statistical field theory framework, which captures the observed phase transition in alignment with experimental results. This analysis reduces the regularization hyperparameter search space from two dimensions to one, significantly lowering computational costs. Experiments on UCI datasets and the large-scale ClimSim dataset demonstrate robust calibration performance, effectively quantifying predictive uncertainty.

This paper introduces BRIES, a novel compound AI architecture designed to detect and measure the effectiveness of persuasion attacks across information environments. We present a system with specialized agents: a Twister that generates adversarial content employing targeted persuasion tactics, a Detector that identifies attack types with configurable parameters, a Defender that creates resilient content through content inoculation, and an Assessor that employs causal inference to evaluate inoculation effectiveness. Experimenting with the SemEval 2023 Task 3 taxonomy across the synthetic persuasion dataset, we demonstrate significant variations in detection performance across language agents. Our comparative analysis reveals significant performance disparities with GPT-4 achieving superior detection accuracy on complex persuasion techniques, while open-source models like Llama3 and Mistral demonstrated notable weaknesses in identifying subtle rhetorical, suggesting that different architectures encode and process persuasive language patterns in fundamentally different ways. We show that prompt engineering dramatically affects detection efficacy, with temperature settings and confidence scoring producing model-specific variations; Gemma and GPT-4 perform optimally at lower temperatures while Llama3 and Mistral show improved capabilities at higher temperatures. Our causal analysis provides novel insights into socio-emotional-cognitive signatures of persuasion attacks, revealing that different attack types target specific cognitive dimensions. This research advances generative AI safety and cognitive security by quantifying LLM-specific vulnerabilities to persuasion attacks and delivers a framework for enhancing human cognitive resilience through structured interventions before exposure to harmful content.

We apply physics-informed machine learning (PIML) to solve inverse problems in holography and classical mechanics, focusing on neural ordinary differential equations (Neural ODEs) and physics-informed neural networks (PINNs) for solving non-linear differential equations of motion. First, we introduce holographic inverse problems and demonstrate how PIML can reconstruct bulk spacetime and effective potentials from boundary quantum data. To illustrate this, two case studies are explored: the QCD equation of state in holographic QCD and $T$-linear resistivity in holographic strange metals. Additionally, we explicitly show how such holographic problems can be analogized to inverse problems in classical mechanics, modeling frictional forces with neural networks. We also explore Kolmogorov-Arnold Networks (KANs) as an alternative to traditional neural networks, offering more efficient solutions in certain cases. This manuscript aim to provide a systematic framework for using neural networks in inverse problems, serving as a comprehensive reference for researchers in machine learning for high-energy physics, with methodologies that also have broader applications in mathematics, engineering, and the natural sciences.