A US teenager was handcuffed by armed police after an artificial intelligence (AI) system mistakenly said he was carrying a gun - when really he was holding a packet of crisps. Police showed up, like eight cop cars, and then they all came out with guns pointed at me talking about getting on the ground, 16-year-old Baltimore pupil Taki Allen told local outlet WMAR-2 News . Baltimore County Police Department said their officers responded appropriately and proportionally based on the information provided at the time. It said the AI alert was sent to human reviewers who found no threat - but the principal missed this and contacted the school's safety team, who ultimately called the police. But the incident has prompted calls by some for the schools' procedures around the use of such technology to be reviewed.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future An Israeli drone strike has killed two people in southern Lebanon, according to the country's Ministry of Public Health, a day after Israeli warplanes launched a series of deadly strikes on the country's eastern mountain range and south. It's the latest in near-daily Israeli violations of the United States-brokered ceasefire between Israel and the Lebanese armed group Hezbollah that began in November. It did not provide details on their condition. A security source told Al Jazeera that the drone fired a missile at a car, directly hitting the vehicle and causing it to catch fire.

European diplomats reveal the'tough guy' US negotiator leading the charge on Greenland: 'He hates us' A former Marine was unmasked as the'Zodiac killer' after a bombshell new investigation. I suffered a horrific side effect of a drug used by millions of Americans... and my face'melted off' The ICE backlash isn't the end of Kristi Noem It may have just saved her career FedEx driver accused of abducting and killing little girl while delivering her Christmas present says he shouldn't be executed because he has autism Senator accused of steamy affair with her bodyguard in bombshell lawsuit from his WIFE: 'Bring MDMA so I can guide you' Hunter Biden's stripper baby mama asks for him to be ARRESTED over claims he is still failing to pay her child support Family of Tyler Robinson's transgender lover speaks out for first time since Charlie Kirk assassination and reveals where he is now Dodgers agree with Kyle Tucker'on $240m deal' as champs beat out Mets, Blue Jays for top free agent World's sexiest hockey star and OnlyFans model Mikayla Demaiter spills out of little dress in latest post Nicole Richie addresses her daughter's new identity after unveiling transformation on her 18th birthday Trump gushes over'young beautiful' hockey players and teases rebranding of famed presidential wall Trump's AG secretary sparks mockery with tone-deaf $3 dinner advice as food costs soar Karoline Leavitt reveals the thinking behind Trump's call to cancel elections Microsoft's paperclip mascot delights users as it returns - 18 years after it was axed from Office It was the original virtual assistant, released years before Siri, Alexa, and Bixby. Now, almost two decades after it was axed, Microsoft's Clippy is officially back. The friendly anthropomorphic paper clip has been spotted as an Easter egg in Microsoft's latest announcement about a new AI companion called Mico. Mico - whose name is a nod to Microsoft Copilot - is a small blob with a friendly smiley face, and doesn't look much like its much-loved predecessor.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Ukraine's Zelenskyy to meet European leaders in London over military aid Ukrainian President Volodymyr Zelenskyy is due to meet European leaders in the United Kingdom for talks on military aid to stave off future Russian aggression if a ceasefire stops the war now in its fourth year. Zelenskyy and British Prime Minister Keir Starmer are expected to be joined at the Foreign Office in London on Friday by NATO Secretary-General Mark Rutte, Danish Prime Minister Mette Frederiksen and Dutch Prime Minister Dick Schoof.

As the Netherlands gears up for a snap parliamentary election on October 29, less than halfway through parliament's usual four-year term following the collapse of the ruling coalition, the likelihood of another win for the country's far-right Party for Freedom (PVV) is mounting. An outright win is next to impossible. The Netherlands has always had a coalition government formed by a minimum of two parties due to its proportional representation electoral system, under which seats in parliament are awarded to parties in proportion to the number of votes they win. It then partnered with three other far-right parties - the Farmer-Citizen Movement (BBB), New Social Contract (NSC), and the People's Party for Freedom and Democracy (VVD) - to form a coalition government. But in June, PVV made a dramatic exit from the coalition government over a disagreement on immigration policy.

Next week, five teens from Palestine will head to Panama to compete in one of the world's largest youth robotics competitions. To win--and then teach STEM to their peers displaced by the Israel-Hamas war. For the entirety of the past year, as the teenage roboticists of Team Palestine have been working on their latest project, their homeland has been engulfed in Israel's war with Hamas . Earlier this month, that all changed. With a fragile ceasefire in place, Israeli forces began to pull back from parts of Gaza, and the teens put the final touches on the project they hope will bring them victory: a robot that can maneuver through a series of simulated challenges based on the effects of climate change.

Republican John Reid used AI to create a mock debate with Democratic rival Ghazala Hashmi for the Virginia lieutenant governor's race after she declined real debates.

Online advertising platforms use automated auctions to connect advertisers with potential customers, requiring effective bidding strategies to maximize profits. Accurate ad impact estimation requires considering three key factors: delayed and long-term effects, cumulative ad impacts such as reinforcement or fatigue, and customer heterogeneity. However, these effects are often not jointly addressed in previous studies. To capture these factors, we model ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards. For efficient estimation, we propose a two-stage maximum likelihood estimator combined with data-splitting strategies, ensuring controlled estimation error based on the first-stage estimator's (in)accuracy. Building on this, we design a reinforcement learning algorithm to derive efficient personalized bidding strategies. This approach achieves a near-optimal regret bound of $\tilde{O}{(dH^2\sqrt{T})}$, where $d$ is the contextual dimension, $H$ is the number of rounds, and $T$ is the number of customers. Our theoretical findings are validated by simulation experiments.

The worlds people have strong opinions about artificial intelligence (AI), and they want policymakers to listen. Governments are inviting public comment on AI, but as they translate input into policy, much of what citizens say is lost. Policymakers are missing a critical opportunity to build trust in AI and its governance. This paper compares three countries, Australia, Colombia, and the United States, that invited citizens to comment on AI risks and policies. Using a landscape analysis, the authors examined how each government solicited feedback and whether that input shaped governance. Yet in none of the three cases did citizens and policymakers establish a meaningful dialogue. Governments did little to attract diverse voices or publicize calls for comment, leaving most citizens unaware or unprepared to respond. In each nation, fewer than one percent of the population participated. Moreover, officials showed limited responsiveness to the feedback they received, failing to create an effective feedback loop. The study finds a persistent gap between the promise and practice of participatory AI governance. The authors conclude that current approaches are unlikely to build trust or legitimacy in AI because policymakers are not adequately listening or responding to public concerns. They offer eight recommendations: promote AI literacy; monitor public feedback; broaden outreach; hold regular online forums; use innovative engagement methods; include underrepresented groups; respond publicly to input; and make participation easier.

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values. Large Language Models (LLMs) have transformed Natural Language Processing (NLP), Computer Vision (CV), and machine learning (ML) more broadly. They achieve state-of-the-art performance in text generation and comprehension across diverse domains, including code synthesis (Rozi ` ere et al., 2023), mathematical reasoning (Ahn et al., 2024), scientific writing (Geng et al., 2025; Eger et al., 2025), multimodal tasks such as text-image understanding and generation (Alayrac et al., 2022), and evaluation of machine translation and related tasks (Gu et al., 2025). This success stems from scaling to millions and billions of parameters. However, this scaling requires large computational resources, motivating the search for parameter-efficient fine-tuning (PEFT) techniques. Recent advances have made it possible to adapt LLMs to task-specific criteria, which is crucial for a broader applicability and acceptance of NLP systems. A recent stream of research leverages PEFT techniques (Ding et al., 2023; Weyssow et al., 2023; Prottasha et al., 2024), e.g., Low-Rank Adaptions (LoRA) (Hu et al., 2021) to adapt for desired task-specific values in an LLM. LoRA achieves this by freezing most of the pre-trained model's parameters and introducing trainable low-rank matrices, yielding weight correction terms. However, stand-alone LoRA approaches are primarily tailored for a single-task adaptation and may lose their effectiveness in a setting where an LLM needs to be adapted to various downstream settings.