Many existing AI music generation tools rely on text prompts, complex interfaces, or instrument-like controls, which may require musical or technical knowledge that non-musicians do not possess. This paper introduces DeformTune, a prototype system that combines a tactile deformable interface with the MeasureVAE model to explore more intuitive, embodied, and explainable AI interaction. We conducted a preliminary study with 11 adult participants without formal musical training to investigate their experience with AI-assisted music creation. Thematic analysis of their feedback revealed recurring challenge--including unclear control mappings, limited expressive range, and the need for guidance throughout use. We discuss several design opportunities for enhancing explainability of AI, including multimodal feedback and progressive interaction support. These findings contribute early insights toward making AI music systems more explainable and empowering for novice users.

--This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoint, the algorithm identifies a convex admissible region and solves data-driven linear-matrix-inequality problems to learn several ellipsoidal invariant sets together with their local state-feedback gains. The convex hull of these ellipsoids--still invariant under a piece-wise-affine controller obtained by interpolating the gains--is then approximated by a polytope. Safe transitions between nodes are ensured by verifying the intersection of consecutive convex-hull polytopes and introducing an intermediate node for a smooth transition. Control gains are interpolated in real time via simplex-based interpolation, keeping the state inside the invariant polytopes throughout the motion. Unlike traditional approaches that rely on system dynamics models, our method requires only data to compute safe regions and design state-feedback controllers. The approach is validated through simulations, demonstrating the effectiveness of the proposed method in achieving safe, dynamically feasible paths for complex nonlinear systems. Over the years, several motion-planning approaches have been proposed, including graph search-based methods [2], sampling-based methods like rapidly exploring random trees (RRT) [3], behavior-based approaches [4], machine learning-based approaches [5], potential fields [6], and optimization-based techniques such as differential dynamic programming [7]. Among them, RRT, as a sampling-based approach, has received a surge of interest due to its success in robotic applications. However, most of these successful strategies are under assumptions that cannot be certified in many applications [8], [9]. For instance, the planning is typically performed assuring that the waypoints are kinematically feasible.

Counterspeech is a key strategy against harmful online content, but scaling expert-driven efforts is challenging. Large Language Models (LLMs) present a potential solution, though their use in countering conspiracy theories is under-researched. Unlike for hate speech, no datasets exist that pair conspiracy theory comments with expert-crafted counterspeech. We address this gap by evaluating the ability of GPT-4o, Llama 3, and Mistral to effectively apply counterspeech strategies derived from psychological research provided through structured prompts. Our results show that the models often generate generic, repetitive, or superficial results. Additionally, they over-acknowledge fear and frequently hallucinate facts, sources, or figures, making their prompt-based use in practical applications problematic.

Generative models achieve remarkable results in multiple data domains, including images and texts, among other examples. Unfortunately, malicious users exploit synthetic media for spreading misinformation and disseminating deepfakes. Consequently, the need for robust and stable fake detectors is pressing, especially when new generative models appear everyday. While the majority of existing work train classifiers that discriminate between real and fake information, such tools typically generalize only within the same family of generators and data modalities, yielding poor results on other generative classes and data domains. Towards a universal classifier, we propose the use of large pre-trained multi-modal models for the detection of generative content. Effectively, we show that the latent code of these models naturally captures information discriminating real from fake. Building on this observation, we demonstrate that linear classifiers trained on these features can achieve state-of-the-art results across various modalities, while remaining computationally efficient, fast to train, and effective even in few-shot settings. Our work primarily focuses on fake detection in audio and images, achieving performance that surpasses or matches that of strong baseline methods.

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.