Charlie Kirk's wife reveals she was'praying to God' she was pregnant when her husband was killed It all seems to be falling apart now! Marriage drama for lawyer mom whose stepdad infamously dropped daughter, 2, to her death off cruise ship... as she debuts raunchy new look and bad boy lover Gavin Newsom's inner circle on edge as multiple aides receive ominous letter from FBI just days after California governor's chief of staff was indicted Full House's Jodie Sweetin reveals how addiction struggle began at 14 at costar Candace Cameron Bure's wedding Cunning new tactic women are using to cheat. Fans turn on RichTok influencer Becca Bloom over shocking comments... as she makes stunning admission about her marriage and her wild extravagance is revealed Slash your cholesterol by a third in just a month... hundreds of thousands are on a new diet that's transforming lives. Top doctor reveals little-known procedure to fix agonizing issue that plagues half of men over 50. It could cure those late-night trips to the bathroom... AND save your sex life World's first lung cancer vaccine to enter clinical trials... but quitting smoking is still recommended as top way to avoid developing the disease First pieces of $20B trove retrieved from 300-year-old'Holy Grail' shipwreck off Colombia Curse of $30m'Netflix mansion' where Meghan and Harry declared war on the Royal Family as owner takes drastic action to sell it Scientists issue ominous warning over mind-altering'brain weapons' that can control your perception, memory and behaviour Mind control weapons may sound like something from a dystopian science fiction film, but experts now say they are becoming a reality.

It all seems to be falling apart now! Cunning new tactic women are using to cheat. Trump delivers savage parting shot to'lowlifes' MTG and Thomas Massie while declaring GOP has'never been so united' Gavin Newsom's inner circle on edge as multiple aides receive ominous letter from FBI just days after California governor's chief of staff was indicted Experts discover there are EIGHT different types of long Covid... do you have any of them? Full House's Jodie Sweetin reveals how addiction struggle began at 14 at costar Candace Cameron Bure's wedding Fans turn on RichTok influencer Becca Bloom over shocking comments... as she makes stunning admission about her marriage and her wild extravagance is revealed Morgan was searching for her soulmate in church... then she uncovered the sinister underbelly of Christian dating in MAGA America. Rich moms of Manhattan go to WAR: Innocent comment plunges gilded zip code into anarchy... and everyone's looking over their shoulder Two Texas men's twisted fantasy to recruit homeless to invade remote island, kill its inhabitants and ravage their women WANTED: One VERY tolerant Lady! Picky aristocrat, 79, launches bid to find a wife.

AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online editions of 1.5K American newspapers published in the summer of 2025. Using Pangram, a state-of-the-art AI detector, we discover that approximately 9% of newly-published articles are either partially or fully AI-generated. This AI use is unevenly distributed, appearing more frequently in smaller, local outlets, in specific topics such as weather and technology, and within certain ownership groups. We also analyze 45K opinion pieces from Washington Post, New York Times, and Wall Street Journal, finding that they are 6.4 times more likely to contain AI-generated content than news articles from the same publications, with many AI-flagged op-eds authored by prominent public figures. Despite this prevalence, we find that AI use is rarely disclosed: a manual audit of 100 AI-flagged articles found only five disclosures of AI use. Overall, our audit highlights the immediate need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust.

Booth is a reporter at TIME. Hemant Taneja, CEO, General Catalyst speaks on stage during The Summit on U.S. Resilience hosted by General Catalyst Institute at The Salamander on Nov. 17, 2025 in Washington, DC. Hemant Taneja, CEO, General Catalyst speaks on stage during The Summit on U.S. Resilience hosted by General Catalyst Institute at The Salamander on Nov. 17, 2025 in Washington, DC. Booth is a reporter at TIME. Hemant Taneja, who leads one of the world's largest venture firms, believes doing good isn't just the right thing to do.

Leading the Future said it will spend millions to keep Alex Bores out of Congress. It might be helping him instead. It turns out that when an AI-friendly super PAC with $100 million in backing from Silicon Valley bigwigs identifies you as its first target, it ends up generating a lot of attention. "I want to thank [the PAC] for their partnership in raising up the issue of how we regulate an incredibly powerful technology so that the future is one that benefits all of us," says Alex Bores, a New York Assembly member and Democratic congressional candidate, in an interview with WIRED. "I couldn't imagine a better partner this week."

Scientists reveal what triggered Santorini'earthquake swarm' The swarm of tens of thousands of earthquakes near the Greek island of Santorini earlier this year was triggered by molten rock pumping through an underground channel over three months, scientists have discovered. They used physics and artificial intelligence to work out exactly what caused the more than 25,000 earthquakes, which travelled about 20km (12 miles) horizontally through the Earth's crust. They used each of the tremors as virtual sensors, then used artificial intelligence to analyse patterns associated with them. One of the lead researchers, Dr Stephen Hicks from UCL, said combining physics and machine learning in this way could help forecast volcanic eruptions. The seismic activity started to stir beneath the Greek islands of Santorini, Amorgos, and Anafi in January 2025.

In real-world task-oriented dialogue (TOD) settings, agents are required to strictly adhere to complex instructions while conducting multi-turn conversations with customers. These instructions are typically presented in natural language format and include general guidelines and step-by-step procedures with complex constraints. Existing TOD benchmarks often oversimplify the complex nature of these instructions by reducing them to simple schemas composed of intents, slots, and API call configurations. To address this gap and systematically benchmark LLMs' instruction-following capabilities, we propose TOD-ProcBench, a challenging benchmark featuring complex process instructions with intricate, fine-grained constraints that evaluates various LLMs' abilities to understand and follow instructions in multi-turn TODs. Our benchmark dataset comprises instruction documents derived from the high-quality ABCD dataset with corresponding conversations under human quality control. We formulate fine-grained constraints and action procedures as multi-level condition-action instruction statements. We design three tasks to comprehensively benchmark LLMs' complex instruction-following capabilities in multi-turn TODs. Task 1 evaluates how LLMs retrieve the most relevant statement from a complex instruction and predict the corresponding next action. In Task 2, we synthesize instruction-violating responses by injecting inconsistencies and manipulating the original instructions, and then we analyze how effectively LLMs can identify instruction-violating responses. Task 3 investigates LLMs' abilities in conditional generation of instruction-following responses based on the original complex instructions. Additionally, we conduct studies on the impact of multilingual settings and different instruction text formats on compliance performance. We release our benchmark under the Llama 3.3 Community License Agreement.

Applications of artificial intelligence or machine learning in research Modes of use Surrogate modeling for physics - based models Modeling of poorly understood phenomena Data preprocessing Large language model use Applications AI/ML as research tool Production process design, monitoring, & output prediction Part design & properties prediction Materials design & properties prediction AI/ML as research product Generative AI design tool for consumers Generic research tasks Large language models for coding Large language models for literature review Benefits of artificial intelligence or machine learning in research Reduction in accuracy/cost/speed trade - off in research, especially computer modeling Reduced computation time Replacing experimentation Reducing need for computationally intensive, physics - based models Saving research labor Exploring larger design spaces Address of previously unsolvable problems Model poorly understood relationships between variables Identify human - unidentifiable patterns or phenomena Downsides of artificial intelligence or machine learning in research Accuracy weaknesses Predict poorly outside regions of dense, high - quality training data Interpretability weaknesses Bounds of accuracy can be unclear Accuracy assessment can be difficult Long - run scientific progress concerns AI/ML cannot develop novel scientific theory AI/ML may bypass opportunities to identify empirical or theoretical novelties Resource issues Data acquisition and cleaning is time - intensive AI/ML models are computation - and energy - intensive to develop Inappropriate use issues Easy to over - trust May be inappropriately used to address problems soluble with simpler methods 8 Second, AI/ML models can be trained on input and output data for phenomena (e.g., complex production processes) which lack robust theoretical models, developing novel predictive capabilities in the absence of explicit, human - designed theory. This is somet imes referred to as "phenomenological modeling," as it attempts to model phenomena in the absence of mechanistic, explanatory understanding: [T]he first reason we choose to use AI is because we don't have a good model of what our system is. . . I get a bunch of data coming in and I have a bunch of sensor readings, you know. . . And I use the AI to map the bunch of sensor readings to the process health or process status or machine status that I have.

Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.

The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.