EXCLUSIVE - Author and filmmaker Justine Bateman expressed optimism for the country following President-elect Donald Trump's historic victory, saying it felt like a cloud had been lifted. I feel great, in fact," Bateman told Fox News Digital in an interview. "I feel like there was this kind of suffocating cloud that was kind of over us… Regular people who had questions about decisions that were being made were threatened subtly or obviously into silence. And I feel like that's been broken, that sort of suppression has been kind of broken." Bateman, best known for playing Mallory Keaton on the hit 1980s sitcom "Family Ties," recently went viral for referring to the last four years as being "a very un-American period" for free expression and that only "permitted positions" were accepted by the powers that be. "My belief is that everyone should be free to live their life exactly how they want to live it.

Recommendation systems have become the fundamental services to facilitate users information access. Generally, recommendation system works by filtering historical behaviors to understand and learn users preferences. With the growth of online information, recommendations have become of crucial importance in information filtering to prevent the information overload problem. In this study, we considered hybrid post-fusion of two approaches of collaborative filtering, by using sequences of watched movies and considering the related movies rating. After considering both techniques and applying the weights matrix, the recommendations would be modified to correspond to the users preference as needed. We discussed that various weights would be set based on use cases. For instance, in cases where we have the rating for most classes, we will assign a higher weight to the rating matrix and in case where the rating is unavailable for the majority of cases, the higher weights might be assigned to the sequential dataset. An extensive discussion is made in the context of this paper. Sequential type of the watched movies was used in conjunction of the rating as especially that model might be inadequate in distinguishing users long-term preference and that does not account for the rating of the watched movies and thus that model along might not suffice. Extensive discussion was made regarding the literature and methodological approach to solve the problem.

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations through dynamically capturing user preferences in interactive conversations. Conventional CRSs often extract user preferences as hidden representations, which are criticized for their lack of interpretability. This diminishes the transparency and trustworthiness of the recommendation process. Recent works have explored combining the impressive capabilities of Large Language Models (LLMs) with the domain-specific knowledge of Knowledge Graphs (KGs) to generate human-understandable recommendation explanations. Despite these efforts, the integration of LLMs and KGs for CRSs remains challenging due to the modality gap between unstructured dialogues and structured KGs. Moreover, LLMs pre-trained on large-scale corpora may not be well-suited for analyzing user preferences, which require domain-specific knowledge. In this paper, we propose COMPASS, a plug-and-play framework that synergizes LLMs and KGs to unveil user preferences, enhancing the performance and explainability of existing CRSs. To address integration challenges, COMPASS employs a two-stage training approach: first, it bridges the gap between the structured KG and natural language through an innovative graph entity captioning pre-training mechanism. This enables the LLM to transform KG entities into concise natural language descriptions, allowing them to comprehend domain-specific knowledge. Following, COMPASS optimizes user preference modeling via knowledge-aware instruction fine-tuning, where the LLM learns to reason and summarize user preferences from both dialogue histories and KG-augmented context. This enables COMPASS to perform knowledge-aware reasoning and generate comprehensive and interpretable user preferences that can seamlessly integrate with existing CRS models for improving recommendation performance and explainability.

Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful news by leveraging multilingual satire detection methods in English and Arabic. We explore both zero-shot and chain-of-thought (CoT) prompting using two language models, Jais-chat(13B) and LLaMA-2-chat(7B). Our results show that CoT prompting offers a significant advantage for the Jais-chat model over the LLaMA-2-chat model. Specifically, Jais-chat achieved the best performance, with an F1-score of 80\% in English when using CoT prompting. These results highlight the importance of structured reasoning in CoT, which enhances contextual understanding and is vital for complex tasks like satire detection.

Nowadays, the rapid diffusion of fake news poses a significant problem, as it can spread misinformation and confusion. This paper aims to develop an advanced machine learning solution for detecting fake news articles. Leveraging a comprehensive dataset of news articles, including 23,502 fake news articles and 21,417 accurate news articles, we implemented and evaluated three machine-learning models. Our dataset, curated from diverse sources, provides rich textual content categorized into title, text, subject, and Date features. These features are essential for training robust classification models to distinguish between fake and authentic news articles. The initial model employed a Long Short-Term Memory (LSTM) network, achieving an accuracy of 94%. The second model improved upon this by incorporating additional regularization techniques and fine-tuning hyperparameters, resulting in a 97% accuracy. The final model combined the strengths of previous architectures with advanced optimization strategies, achieving a peak accuracy of 98%. These results demonstrate the effectiveness of our approach in identifying fake news with high precision. Implementing these models showcases significant advancements in natural language processing and machine learning techniques, contributing valuable tools for combating misinformation. Our work highlights the potential for deploying such models in real-world applications, providing a reliable method for automated fake news detection and enhancing the credibility of news dissemination.

Several GenAI coding assistants, including GitHub's Copilot [45], Tabnine [119], Codeium [24], and Cody [25], as well as general purpose tools such as ChatGPT [100], Claude [11], and Gemini [42], have become readily accessible, either as IDE extensions or standalone applications, enabling developers to perform many coding tasks with little effort, including automated code completion, summarization, and debugging.

The images and videos were hard to miss in the days leading up to November 5. There was Donald Trump with the chiseled musculature of Superman, hovering over a row of skyscrapers. People had clearly used AI to create these--an effort to show support for their candidate or to troll their opponents. But the images didn't stop after Trump won. The day after polls closed, the Statue of Liberty wept into her hands as a drizzle fell around her. Trump and Elon Musk, in space suits, stood on the surface of Mars; hours later, Trump appeared at the door of the White House, waving goodbye to Harris as she walked away, clutching a cardboard box filled with flags.

Back in 2022, when ChatGPT arrived, I was part of the first wave of users. Delighted but also a little uncertain what to do with it, I asked the system to generate all kinds of random things. The quality of what it produced was variable, but it made clear something that is even more apparent now than it was then. Instead its arrival is an inflection point in human history. Over coming years and decades, AI will transform every aspect of our lives.

Their final song was mixed with John Lennon's voice. The Beatles' return to the Grammys has come with an assist from artificial intelligence. "Now and Then" is nominated for record of the year and best rock performance at the 2025 Grammy Awards, making it the first nominated song ever to use AI in its production. The song utilized AI to clean up old demo recordings of John Lennon singing and playing piano, recorded in the late 1970s, as well as a guitar track from George Harrison, recorded six years before his death in 2001. "To me, this is a cool example of how AI can function in our current environment," Recording Academy CEO Harvey Mason Jr. said in a statement to Fox News Digital.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.