The countersuit opens up a new front in the high-stakes battle between two Silicon Valley heavyweights. "Elon's nonstop actions against us are just bad-faith tactics to slow down OpenAI and seize control of the leading AI innovations for his personal benefit," OpenAI said in a statement on Wednesday. "Today, we countersued to stop him." Last week, a federal judge in Oakland, California, set a March 2026 trial date in Mr Musk's suit in a bid to fast-track the legal fight. US District Judge Yvonne Gonzalez Rogers previously declined to grant Mr Musk an injunction that would temporarily halt OpenAI's conversion from a non-profit to a for-profit company.

A handful of wealthy, politically connected Silicon Valley investors are reportedly eyeing Greenland's icy shores as the site for a techno-utopian "freedom city." That's according to a report from Reuters, which details a proposed effort to establish a new, libertarian-minded municipality characterized by minimal corporate regulation and a focus on accelerating emerging technologies like AI and mini nuclear reactors. Supporters of increased economic development in Greenland argue its frigid climate could naturally cool massive, energy intensive AI data centers. Large deposits of critical and rare earth minerals buried beneath the island's ice sheets could also potentially be used to manufacture consumer electronics. The so-called "start-up city"--which bears similarities to another ongoing venture in California's Solano County--reportedly already has the backing of PayPal founder Peter Thiel and Ken Howery, President Donald Trump's pick for Denmark ambassador.

The two workers say their employment was terminated over the protests. Fox News' "Antisemitism Exposed" newsletter brings you stories on the rising anti-Jewish prejudice across the U.S. and the world. TOP STORY: Microsoft fired an employee who disrupted the company's 50th anniversary celebration event to voice their opposition to its work supplying artificial intelligence technology to Israel. As Microsoft AI CEO Mustafa Suleyman spoke at the event, Ibtihal Aboussad began shouting at him, accusing him of being "a war profiteer." She demanded that Suleyman "stop using AI for genocide."

OpenAI has filed a countersuit against Elon Musk, accusing him of staging press attacks and malicious campaigns on "the social media platform he controls," as well as of making "harassing legal claims" and a "sham bid for OpenAI's assets." In its filing, courtesy of TechCrunch, the ChatGPT-maker said Musk could not tolerate seeing such "success for an enterprise he had abandoned and declared doomed" and had made it his own project to take down the organization. It also said that Musk's efforts have ramped up in recent months after it announced its plans to restructure and become a for-profit entity with a non-profit division. Last year, Musk sued OpenAI, accusing it of ditching its nonprofit mission, becoming a "closed-source de facto subsidiary" Microsoft and of violating its foundational agreement to develop generative AI "for the benefit of humanity." But Musk, OpenAI said in its new lawsuit, is only pretending to represent the public and in truth is seeking to stop it from restructuring.

The former Sinn Féin president Gerry Adams is considering legal action against Meta because it may have used his books to train artificial intelligence. "Meta has used many of my books without my permission. I have placed the issue in the hands of my solicitor," he said. Sinn Féin said in a statement on Wednesday that the titles included its former leader's autobiography, Before the Dawn; a prison memoir, Cage Eleven; reflections on Northern Ireland's peace process, Hope and History; and other memoirs, a cookbook and a short story collection. Adams is the latest author to join a backlash against the parent company of Facebook, Instagram and WhatsApp.

This paper introduces a comprehensive system for detecting hallucinations in large language model (LLM) outputs in enterprise settings. We present a novel taxonomy of LLM responses specific to hallucination in enterprise applications, categorizing them into context-based, common knowledge, enterprise-specific, and innocuous statements. Our hallucination detection model HDM-2 validates LLM responses with respect to both context and generally known facts (common knowledge). It provides both hallucination scores and word-level annotations, enabling precise identification of problematic content. To evaluate it on context-based and common-knowledge hallucinations, we introduce a new dataset HDMBench. Experimental results demonstrate that HDM-2 out-performs existing approaches across RagTruth, TruthfulQA, and HDMBench datasets. This work addresses the specific challenges of enterprise deployment, including computational efficiency, domain specialization, and fine-grained error identification. Our evaluation dataset, model weights, and inference code are publicly available.

Evaluating Retrieval A ugmented G enerative Models for Document Queries in Transportation Safety C.A. Melton, A. Sorokine, S. Peterson Oak Ridge National Laboratory, Oak Ridge, TN, United States National Security Sciences Directorate ABSTRACT Applications of generative Large Language Models (LLMs) are rapidly expanding across various domains, promising significant improvements in workflow efficiency and information retrieval. However, their implementation in specialized, high - stakes domains suc h as hazardous materials transportation is challenging due to accuracy and reliability concerns. This study evaluates the performance of three fine - tuned generative models -- ChatGPT, Google's Vertex AI, and ORNL Retrieval - Augmented Generation augmented LLaMA 2 and LLaMA in retrieving regulatory information essential for hazardous material transportation compliance in the United States. Utilizing approximately 40 publicly available federal and state regulatory documents, we developed 100 realistic queries relevant to route planning and permitting requirements. Responses were qualitatively rated based on accuracy, detail, and relevance, complemented by quantitative assessments of semantic similarity between model outputs. Results demon strated that the RAG - augmented LLaMA models significantly outperformed Vertex AI and ChatGPT, providing more detailed and generally accurate information, despite occasional inconsistencies. This research introduces the first known application of RAG in tra nsportation safety, emphasizing the need for domain - specific fine - tuning and rigorous evaluation methodologies to ensure reliability and minimize the risk of inaccuracies in high - stakes environments.

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations.

Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpretability of the unlearning process, particularly concerning sample-level unlearning difficulty. Existing studies typically assume a uniform unlearning difficulty across samples. This simplification risks attributing the performance of unlearning algorithms to sample selection rather than the algorithm's design, potentially steering the development of LLM unlearning in the wrong direction. Thus, we investigate the relationship between LLM unlearning and sample characteristics, with a focus on unlearning difficulty. Drawing inspiration from neuroscience, we propose a Memory Removal Difficulty ($\mathrm{MRD}$) metric to quantify sample-level unlearning difficulty. Using $\mathrm{MRD}$, we analyze the characteristics of hard-to-unlearn versus easy-to-unlearn samples. Furthermore, we propose an $\mathrm{MRD}$-based weighted sampling method to optimize existing unlearning algorithms, which prioritizes easily forgettable samples, thereby improving unlearning efficiency and effectiveness. We validate the proposed metric and method using public benchmarks and datasets, with results confirming its effectiveness.

Machine Unlearning aims to remove undesired information from trained models without requiring full retraining from scratch. Despite recent advancements, their underlying loss landscapes and optimization dynamics received less attention. In this paper, we investigate and analyze machine unlearning through the lens of mode connectivity - the phenomenon where independently trained models can be connected by smooth low-loss paths in the parameter space. We define and study mode connectivity in unlearning across a range of overlooked conditions, including connections between different unlearning methods, models trained with and without curriculum learning, and models optimized with first-order and secondorder techniques. Our findings show distinct patterns of fluctuation of different evaluation metrics along the curve, as well as the mechanistic (dis)similarity between unlearning methods. To the best of our knowledge, this is the first study on mode connectivity in the context of machine unlearning.