In an interview with Fox News Digital, filmmaker David Zucker declared that he would not be watching "The Naked Gun" starring Liam Neeson, stating the entire concept of a "Naked Gun" reboot was unoriginal and played out. The director of the first two "Naked Gun" movies said he will not be seeing the 2025 reboot of his classic spoof series. In an interview with Fox News Digital, filmmaker David Zucker declared that he would not be watching "The Naked Gun" starring Liam Neeson, stating the entire concept of a "Naked Gun" reboot was unoriginal and played out. "I don't see any reason to see it," he said. "And so, it's like, well, Jim Abrahams said, if your daughter became a prostitute, would you go watch her work?"

Fox News anchor and executive editor Bret Baier has the latest on fears over the'darker side' of artificial intelligence on'Special Report.' Generative Artificial Intelligence can create lifelike imaging and audio, which is likely why an increasing number of film studios are incorporating A.I. into special effects. It comes just two years after Hollywood's largest union went on strike, in part over the impact A.I. would bring. "Popular culture movies like The Terminator have created a very dark dystopian version of what this could look like," White House A.I. and Crypto Czar David Sacks said. "The version of the future of A.I. that I think is probably most accurate if you want to pop cultural references is Star Trek Enterprise. Think about the ship computer in that. It can perform tasks for you. But it doesn't have a will of its own, it doesn't' have a mind of its' own. It's there to help the crew, and it needs to be supervised by humans."

CEO of Meta Mark Zuckerberg arrives for a Senate Judiciary Committee hearing with representatives of social media companies at the Dirksen Senate Office Building on Jan. AI FOR ALL: Meta CEO Mark Zuckerberg on Wednesday announced the tech giant will focus on developing a personal superintelligence for everyone, which will further enable creative and leisurely pursuits. PUSHING BACK: Tech giant Nvidia said on Thursday that its chips do not contain any "backdoors" that would allow others to remotely access or control them, following concerns from China over the security of the company's H20 artificial intelligence chip. EXCLUSIVE CLUB: Microsoft touched 4 trillion in market cap Thursday, joining Nvidia as the only two companies to reach this level. REGULATORY RECALL: The Trump administration's DOGE developed a new tool that leverages artificial intelligence (AI) to review federal regulations for potential elimination, according a new report. ROBOT RAMPAGE: A jaw-dropping video showing a Unitree H1 humanoid robot flailing violently during a test has captured the internet's attention and sparked a new wave of concern about the safety of advanced robotics.

Chicago Mayor Brandon Johnson addressed his controversial support for a 1% tax on groceries after a state tax is set to expire during a press conference. The Chicago Tribune warned on Thursday that Mayor Brandon Johnson's progressive policy proposals may scare businesses away from the already struggling city. As officials anticipate a 1.2 billion deficit, Johnson spoke to reporters on Tuesday about his plans to fix the local economy, particularly how the "billionaires and ultra-rich" can have "more skin in the game." "Everything has to be on the table. Everything has to be on the table," Johnson said of his plans.

A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market by analyzing dynamic pricing distributions. Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as scaled Beta distributions. This enables accurate parameter estimation from incomplete statistical data using a hybrid of quantile matching and the method of moments. Incorporating the estimated $α$ and $β$ parameters into Random Forest classifiers significantly improves pairwise artist classification accuracy, demonstrating the unique economic signatures in event pricing data. Additionally, we provide theoretical and empirical evidence that incorporating zero-variance (constant-value) features into Random Forest models acts as an implicit regularizer, enhancing feature variety and robustness. This regularization promotes deeper, more varied trees in the ensemble, improving the bias-variance tradeoff and mitigating overfitting to dominant features. These findings are validated on both the new ticket pricing dataset and the standard UCI ML handwritten digits dataset.

As news reporting becomes increasingly global and decentralized online, tracking related events across multiple sources presents significant challenges. Existing news summarization methods typically utilizes Large Language Models and Graphical methods on article-based summaries. However, this is not effective since it only considers the textual content of similarly dated articles to understand the gist of the event. To counteract the lack of analysis on the parties involved, it is essential to come up with a novel framework to gauge the importance of stakeholders and the connection of related events through the relevant entities involved. Therefore, we present SUnSET: Synergistic Understanding of Stakeholder, Events and Time for the task of Timeline Summarization (TLS). We leverage powerful Large Language Models (LLMs) to build SET triplets and introduced the use of stakeholder-based ranking to construct a $Relevancy$ metric, which can be extended into general situations. Our experimental results outperform all prior baselines and emerged as the new State-of-the-Art, highlighting the impact of stakeholder information within news article.

Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidden bridging facts. This process is fundamentally interleaved, as effective reasoning relies on dynamically retrieved evidence, while effective search demands reasoning to refine queries based on partial information. To achieve this, we propose Hierarchical Agent Reasoning and Information Search (HARIS), explicitly modeling the coordinated process of reasoning-driven searching and search-informed reasoning. HARIS consists of a high-level reasoning agent that focuses on constructing the main verification chain, generating factual questions when more information is needed, and a low-level search agent that iteratively retrieves more information, refining its search based on intermediate findings. This design allows each agent to specialize in its respective task, enhancing verification accuracy and interpretability. HARIS is trained using reinforcement learning with outcome-based rewards. Experimental results on the EX-FEVER and HOVER benchmarks demonstrate that HARIS achieves strong performance, greatly advancing multi-hop claim verification.

Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. However, the computational study of these Sociocultural Linguistic Phenomena (SLP) has often been limited to bespoke analyses of specific groups or topics, hindering the pace of scientific discovery. To address this, we introduce Splits!, a 9.7 million-post dataset from Reddit designed for systematic and flexible research. The dataset contains posts from over 53,000 users across 6 demographic groups, organized into 89 discussion topics to enable comparative analysis. We validate Splits! via self-identification and by successfully replicating several known SLPs from existing literature. We complement this dataset with a framework that leverages efficient retrieval methods to rapidly validate potential SLPs (PSLPs) by automatically evaluating whether a given hypothesis is supported by our data. Crucially, to distinguish between novel and obvious insights, the framework incorporates a human-validated measure of a hypothesis's ``unexpectedness.'' We demonstrate that the two-stage process reduces the number of statistically significant findings requiring manual inspection by a factor of 1.5-1.8x, streamlining the discovery of promising phenomena for further investigation.

I reflect on my experience creating two music albums centered on state-of-the-art prompt-based AI music generation platforms. The first album explicitly poses the question: What happens when I collide my junk mail with these platforms? The second album is a direct response to the first, and toys with the inability of state-of-the-art prompt-based AI music generation platforms to generate music that is not ``practiced'', ``polished'', and ``produced''. I seed a large language model (LLM) with information about these albums and have it interview me, which results in the exploration of several deeper questions: To what extent am I the author? Where am I in the resulting music? How is my musical identity changing as I am faced with machines that are in some ways far more talented than I? What new musical spaces does my work open, for me or anyone/thing else? I conclude by reflecting on my reflections, as well as LLM-mediated self-reflection as method.

Social platforms such as Reddit have a network of communities of shared interests, with a prevalence of posts and comments from which one can infer users' Personal Information Identifiers (PIIs). While such self-disclosures can lead to rewarding social interactions, they pose privacy risks and the threat of online harms. Research into the identification and retrieval of such risky self-disclosures of PIIs is hampered by the lack of open-source labeled datasets. Important hindrances to sharing high-quality labelled data include high annotation costs and privacy risks associated with the release of datasets containing self-disclosive text, especially when users include vulnerable populations. To foster reproducible research into PII-revealing text detection, we develop a novel methodology to create synthetic equivalents of PII-revealing data that can be safely shared. Our contributions include creating a taxonomy of 19 PII-revealing categories for vulnerable populations and the creation and release of a synthetic PII-labeled multi-text span dataset generated from 3 text generation Large Language Models (LLMs), Llama2-7B, Llama3-8B, and zephyr-7b-beta, with sequential instruction prompting to resemble the original Reddit posts. The utility of our methodology to generate this synthetic dataset is evaluated with three metrics: First, we require reproducibility equivalence, i.e., results from training a model on the synthetic data should be comparable to those obtained by training the same models on the original posts. Second, we require that the synthetic data be unlinkable to the original users, through common mechanisms such as Google Search. Third, we wish to ensure that the synthetic data be indistinguishable from the original, i.e., trained humans should not be able to tell them apart.