ChatGPT accused of acting as'suicide coach' in series of US lawsuits Chatbot was first used for'general help' with schoolwork or research but'evolved into a psychologically manipulative presence', plaintiffs say ChatGPT has been accused of acting as a "suicide coach" in a series of lawsuits filed this week in California alleging that interactions with the chatbot led to severe mental breakdowns and several deaths. The seven lawsuits include allegations of wrongful death, assisted suicide, involuntary manslaughter, negligence and product liability. Each of the seven plaintiffs initially used ChatGPT for "general help with schoolwork, research, writing, recipes, work, or spiritual guidance", according to a joint statement from the Social Media Victims Law Center and Tech Justice Law Project, which filed the lawsuits in California on Thursday. Over time, however, the chatbot "evolved into a psychologically manipulative presence, positioning itself as a confidant and emotional support", the groups said. "Rather than guiding people toward professional help when they needed it ChatGPT reinforced harmful delusions, and, in some cases, acted as a'suicide coach'."

Vanguard feels and smells like a new RV. It has long, gray banquettes that convert into bunks, a microwave cleverly hidden under a counter, a functional steel sink with a French press and crockery above. Once it is sealed and moved to its permanent home beneath the waves of the Florida Keys National Marine Sanctuary early next year, Vanguard will be the world's first new subsea habitat in nearly four decades. Teams of four scientists will live and work on the seabed for a week at a time, entering and leaving the habitat as scuba divers. This week, we heard that Tom Brady had his dog cloned. The former quarterback revealed that his Junie is actually a clone of Lua, a pit bull mix that died in 2023.

America's flight-mare begins as more than 700 departures ALREADY canceled across US and Trump steps in to end the shutdown Multiple people hospitalized from'white powder' as suspicious package with'political propaganda' sparks evacuation at Joint Base Andrews Prince Harry apologises to Canada over baseball cap'Hatgate' - and adds a joke about thinning on top Alix Earle suffers'total humiliation' at hands of her stepmom: Family insiders reveal former escort's betrayal that they fear will now'completely break' star Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out Moment Prince William refuses to be drawn on Andrew scandal and Harry and Meghan rift as he tells CNN: 'I want to surround myself with people who want to do good' Melania Trump stuns as she accepts'Patriot of the Year' award and issues inspiring message to Americans Elon Musk used biometric data from employees to program'sexy' chatbot during epic quest to win AI arms race Sydney Sweeney wins patriotic hearts with stunning response to criticism of her'good genes' ad Ritzy suburb of NJ's new governor stunned as cops pounce on'yuppie jihadi' neighbor at his $1.2M home over alleged bomb plot Iconic golf ball-sized Florentine diamond once owned by Medici and Habsburg dynasties is FOUND in unusual location 100 years after'vanishing' My addiction to ADHD medication ruined me. I had to choose to either abort my baby or lose my own life... but that was just the start Distressing red flags before Dallas Cowboys star's sudden death at 24 - revealed by roommate who shares harrowing backstory... including recent family tragedy Real-life horror as progressives elect convicted KILLER as councilmember of Maine town that inspired Stephen King's It Israeli hostage who revealed sexual abuse by his captors details full horror he endured: 20-minute torture seven times a day, made to dance, blindfolded with stones in his ears for weeks - 'I have met the Devil' It triggered an earthquake across America. Now, TUCKER CARLSON gives an astonishing defense of the interview that nearly destroyed him... and what he wished he'd known first READ MORE: Gen Z are'rawdogging boredom' to fix their attention spans TikTok has given rise to many strange trends over the years - from'rawdogging boredom' to the viral'turtle rabbit' choreography . While most trends are harmless fun, experts have raised concerns about others - including some that have proved deadly. In a new report, the Omega Law Group has highlighted five of the most dangerous trends that have swept social media in recent years.

America's flight-mare begins as more than 700 departures ALREADY canceled across US and Trump steps in to end the shutdown Israeli hostage who revealed sexual abuse by his captors details full horror he endured: 20-minute torture seven times a day, made to dance, blindfolded with stones in his ears for weeks - 'I have met the Devil' Prince Harry apologises to Canada over baseball cap'Hatgate' - and adds a joke about thinning on top Alix Earle suffers'total humiliation' at hands of her stepmom: Family insiders reveal former escort's betrayal that they fear will now'completely break' star Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out Moment Prince William refuses to be drawn on Andrew scandal and Harry and Meghan rift as he tells CNN: 'I want to surround myself with people who want to do good' Ritzy suburb of NJ's new governor stunned as cops pounce on'yuppie jihadi' neighbor at his $1.2M home over alleged bomb plot Elon Musk used biometric data from employees to program'sexy' chatbot during epic quest to win AI arms race Sydney Sweeney wins patriotic hearts with stunning response to criticism of her'good genes' ad Melania Trump stuns as she accepts'Patriot of the Year' award and issues inspiring message to Americans Iconic golf ball-sized Florentine diamond once owned by Medici and Habsburg dynasties is FOUND in unusual location 100 years after'vanishing' My addiction to ADHD medication ruined me. I had to choose to either abort my baby or lose my own life... but that was just the start Distressing red flags before Dallas Cowboys star's sudden death at 24 - revealed by roommate who shares harrowing backstory... including recent family tragedy Real-life horror as progressives elect convicted KILLER as councilmember of Maine town that inspired Stephen King's It Multiple people hospitalized at Joint Base Andrews as suspicious package containing'white powder' and political message sparks evacuation - one day after Trump's visit My best friend became my bully, says CATHERINE RENTON. She called me fat and then traduced me. It's taboo to say, but the consequences ruined my life. No one's honest about what childhood bullying really does Now that's what you call a blast from the past!

Category V ariable Definition Tax Avoidance CETR Cash Effective T ax Rate = Cash Taxes Paid / Pre - tax Income GETR GAAP Effective Tax Rate = T otal Tax Expense / Pre - tax Income CETR3 Three - year average CETR GETR3 Three - year average GETR CETR5 Five - year average CETR GETR5 Five - year average GETR A_CETR Adjusted Cash Effective Tax Rate A_GETR Adjusted GAAP Effective T ax Rate A_CETR3 Adjusted three - year average CETR A_GETR3 Adjusted three - year average GETR A_CETR5 Adjusted five - year average CETR A_GETR5 Adjusted five - year average GETR TSTA Total Book - T ax Difference (accrual - based measure) TSDA Discretionary Book - Tax Difference (discretionary accrual - based measure) Profitability ROA Return on Assets = Net Income / Lagged T otal Assets ROE Return on Equity = Net Income / Lagged Equity CFO Operating Cash Flow scaled by total assets LOSS Loss dummy (1 if prior - year net income < 0) Stability LEV Leverage = T otal Liabilities / Total Assets CUR Current Ratio = Current Assets / Current Liabilities SIZE Natural logarithm of total assets PPE Ratio of Property, Plant, and Equipment to total assets AGE Natural logarithm of firm age (based on year of establishment) INVREC Ratio of inventories and receivables to total assets Growth GRW Sales growth rate MB Market - to - Book Ratio = Market Capitalization / Book Equity TQ Tobin's Q = (Market Capitalization + Total Liabilities) / T otal Assets Market Valuation & Governance KOSPI KOSPI listing status dummy BIG4 Big4 audit dummy FORN Foreign ownership share (%) OWN Largest shareholder ownership share (%) Stability Measures Stability measures reflect a firm's financial soundness and its ability to meet obligations. Leverage (LEV) is defined as total liabilities divided by total assets, indicating the firm's degree of financial leverage. The current ratio (CUR), calculated as current assets divided by current liabilities, captures short - term liquidity and payment capacity. Firm size (SIZE) is measured as the natural logarithm of total assets, providing a quantitative indicator of scale. The proportion of property, plant, and eq uipment (PPE), defined as tangible fixed assets divided by total assets, is used to assess the structural stability of the asset base.

Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for other languages. We present PLLuM (Polish Large Language Model), the largest open-source family of foundation models tailored specifically for the Polish language. Developed by a consortium of major Polish research institutions, PLLuM addresses the need for high-quality, transparent, and culturally relevant language models beyond the English-centric commercial landscape. We describe the development process, including the construction of a new 140-billion-token Polish text corpus for pre-training, a 77k custom instructions dataset, and a 100k preference optimization dataset. A key component is a Responsible AI framework that incorporates strict data governance and a hybrid module for output correction and safety filtering. We detail the models' architecture, training procedures, and alignment techniques for both base and instruction-tuned variants, and demonstrate their utility in a downstream task within public administration. By releasing these models publicly, PLLuM aims to foster open research and strengthen sovereign AI technologies in Poland.

One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension. Much attention has focused on rare, high-stakes dilemmas such as trolley problems. Yet similar conflicts arise in routine driving when human considerations, such as legality, efficiency, and comfort, come into conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and often lead to behaviour that misaligns with human expectations. This paper introduces a reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework represents human agents reasons (e.g., regulatory compliance) as quantifiable functions and evaluates how well candidate trajectories align with them. It assigns adjustable weights to agent priorities and includes a balance function to discourage excluding any agent. To demonstrate the approach, we use a real-world-inspired overtaking scenario, which highlights tensions between compliance, efficiency, and comfort. Our results show that different trajectories emerge as preferable depending on how agents reasons are weighted, and small shifts in priorities can lead to discrete changes in the selected action. This demonstrates that everyday ethical decisions in AV driving are highly sensitive to the weights assigned to the reasons of different human agents.

Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerged as a promising approach to mitigate such failures by incorporating human input when autonomy is uncertain. However, most existing methods restrict arbitration to low-level trajectories, which represent only geometric paths and therefore fail to preserve the underlying driving intent. We propose a unified shared autonomy framework that integrates human input and autonomous planners at a higher level of abstraction. Our method leverages Vision Language Models (VLMs) to infer driver intent from multi-modal cues -- such as driver actions and environmental context -- and to synthesize coherent strategies that mediate between human and autonomous control. We first study the framework in a mock-human setting, where it achieves perfect recall alongside high accuracy and precision. A human-subject survey further shows strong alignment, with participants agreeing with arbitration outcomes in 92% of cases. Finally, evaluation on the Bench2Drive benchmark demonstrates a substantial reduction in collision rate and improvement in overall performance compared to pure autonomy. Arbitration at the level of semantic, language-based representations emerges as a design principle for shared autonomy, enabling systems to exercise common-sense reasoning and maintain continuity with human intent.

The LLM-as-a-judge paradigm enables flexible, user-defined evaluation, but its effectiveness is often limited by the scarcity of diverse, representative data for refining criteria. We present a tool that integrates synthetic data generation into the LLM-as-a-judge workflow, empowering users to create tailored and challenging test cases with configurable domains, personas, lengths, and desired outcomes, including borderline cases. The tool also supports AI-assisted inline editing of existing test cases. To enhance transparency and interpretability, it reveals the prompts and explanations behind each generation. In a user study (N=24), 83% of participants preferred the tool over manually creating or selecting test cases, as it allowed them to rapidly generate diverse synthetic data without additional workload. The generated synthetic data proved as effective as hand-crafted data for both refining evaluation criteria and aligning with human preferences. These findings highlight synthetic data as a promising alternative, particularly in contexts where efficiency and scalability are critical.

We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.