Actress, entrepreneur, and model Christie Brinkley joins'Fox & Friends' to discuss her new memoir "Uptown Girl," which reflects on her early life, marriages, and career in the public eye. Christie Brinkley and her daughter Sailor Brinkley-Cook have plenty in common despite their 44-year difference. The supermodel, 71, recently appeared on Kristin Davis' "Are You a Charlotte?" Both women were shocked by the results. "[Sailor] said, 'Mom, you're right not to go on [dating apps] because the same guys that, you know, said yes to me are saying yes to you,'" the Sports Illustrated Swimsuit model revealed.

WEHEAD connects to ChatGPT and displays a face, expressions and voice. Artificial intelligence is no longer just a tool for homework or simple tasks. Today, many teens use AI-powered companions for emotional support, advice and even simulated relationships. These apps simulate conversation in ways that feel deeply personal and, for some teens, deeply real. Recent survey data shows that nearly three-quarters of teens aged 13 to 17 have tried a digital companion at least once, and more than half continue to use them regularly.

Japan's Yomiuri Shimbun newspaper, one of the world's biggest by circulation, is suing U.S.-based AI firm Perplexity for allegedly "free-riding" on its content on its search engine. The lawsuit filed Thursday is one of a slew by media companies worldwide against AI firms using their material and is the first by a major Japanese news organization, Yomiuri said. It accuses Perplexity of "free-riding on the results of the activities of news organizations, which have invested a great deal of effort and expense." A spokesman for the paper added that this "could have a negative impact on accurate journalism ... and shake the foundations of democracy." The lawsuit filed in Tokyo seeks damages of 2.2 billion ( 14.7 million), equivalent to 120,000 Yomuiri articles used "without permission" between February and June.

Joaquin Oliver was 17 years old when he was shot in the hallway of his high school. An older teenager, expelled some months previously, had opened fire with a high-powered rifle on Valentine's Day in what became America's deadliest high school shooting. Seven years on, Joaquin says he thinks it's important to talk about what happened on that day in Parkland, Florida, "so that we can create a safer future for everyone". But sadly, what happened to Joaquin that day is that he died. The oddly metallic voice speaking to the ex-CNN journalist Jim Acosta in an interview on Substack this week was actually that of a digital ghost: an AI, trained on the teenager's old social media posts at the request of his parents, who are using it to bolster their campaign for tougher gun controls.

New Delhi, India – What if Michael had died instead of Sonny in The Godfather? Or if Rose had shared the debris plank, and Jack hadn't been left to freeze in the Atlantic in Titanic*? Eros International, one of India's largest production houses, with more than 4,000 films in its catalogue, has decided to explore this sort of what-if scenario. It has re-released one of its major hits, Raanjhanaa, a 2013 romantic drama, in cinemas – but has used artificial intelligence (AI) to change its tragic end, in which the male lead dies. In the AI-altered version, Kundan (played by popular actor Dhanush), a Hindu man who has a doomed romance with a Muslim woman, lives.

Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to model user sentiment and political perspective. Motivated by this, we apply probes to study persuasion dynamics in natural, multi-turn conversations. We leverage insights from cognitive science to train probes on distinct aspects of persuasion: persuasion success, persuadee personality, and persuasion strategy. Despite their simplicity, we show that they capture various aspects of persuasion at both the sample and dataset levels. For instance, probes can identify the point in a conversation where the persuadee was persuaded or where persuasive success generally occurs across the entire dataset. We also show that in addition to being faster than expensive prompting-based approaches, probes can do just as well and even outperform prompting in some settings, such as when uncovering persuasion strategy. This suggests probes as a plausible avenue for studying other complex behaviours such as deception and manipulation, especially in multi-turn settings and large-scale dataset analysis where prompting-based methods would be computationally inefficient.

In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repetitive listening, where users frequently replay familiar tracks. To capture these repetition patterns, recent research has introduced Personalised Popularity Scores (PPS), which quantify user-specific preferences based on historical frequency. While PPS enhances relevance in recommendation, it often reinforces already-known content, limiting the system's ability to surface novel or serendipitous items - key elements for fostering long-term user engagement and satisfaction. To address this limitation, we build upon RecJPQ, a Transformer-based framework initially developed to improve scalability in large-item catalogues through sub-item decomposition. We repurpose RecJPQ's sub-item architecture to model personalised popularity at a finer granularity. This allows us to capture shared repetition patterns across sub-embeddings - latent structures not accessible through item-level popularity alone. We propose a novel integration of sub-ID-level personalised popularity within the RecJPQ framework, enabling explicit control over the trade-off between accuracy and personalised novelty. Our sub-ID-level PPS method (sPPS) consistently outperforms item-level PPS by achieving significantly higher personalised novelty without compromising recommendation accuracy. Code and experiments are publicly available at https://github.com/sisinflab/Sub-id-Popularity.

The internet is rife with unattributed, deliberately misleading, or otherwise untrustworthy content. Though large language models (LLMs) are often tasked with autonomous web browsing, the extent to which they have learned the simple heuristics human researchers use to navigate this noisy environment is not currently known. In this paper, we introduce the Synthetic Media Literacy Test (SMeL Test), a minimal benchmark that tests the ability of language models to actively filter out untrustworthy information in context. We benchmark a variety of commonly used instruction-tuned LLMs, including reasoning models, and find that no model consistently succeeds; while reasoning in particular is associated with higher scores, even the best API model we test hallucinates up to 70% of the time. Remarkably, larger and more capable models do not necessarily outperform their smaller counterparts. We hope our work sheds more light on this important form of hallucination and guides the development of new methods to combat it.

Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause significant problems in certain tasks, including response generation in dialogue. To mitigate this issue, we propose two novel graph knowledge-augmented frameworks, Dialogue Response Generation via Textualised Graphs (TG-DRG) and Graph-Aware Dialogue Response Generation (GA-DRG), which combine reasoning-guided dialogue reformulation, dialogue sense knowledge selection, and graph-enhanced response generation to improve the factuality of dialogue responses. To evaluate the factuality of generated responses, we propose a dialogue fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods noticeably improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever, achieving improvements of 3.47% on OpendialKG and 3.12% on HybriDialogue in terms of dialogue fact score. The code will be released on GitHub.

Creating a meaningful representation by fusing single modalities (e.g., text, images, or audio) is the core concept of multimodal learning. Although several techniques for building multimodal representations have been proven successful, they have not been compared yet. Therefore it has been ambiguous which technique can be expected to yield the best results in a given scenario and what factors should be considered while choosing such a technique. This paper explores the most common techniques for building multimodal data representations -- the late fusion, the early fusion, and the sketch, and compares them in classification tasks. Experiments are conducted on three datasets: Amazon Reviews, MovieLens25M, and MovieLens1M datasets. In general, our results confirm that multimodal representations are able to boost the performance of unimodal models from 0.919 to 0.969 of accuracy on Amazon Reviews and 0.907 to 0.918 of AUC on MovieLens25M. However, experiments on both MovieLens datasets indicate the importance of the meaningful input data to the given task. In this article, we show that the choice of the technique for building multimodal representation is crucial to obtain the highest possible model's performance, that comes with the proper modalities combination. Such choice relies on: the influence that each modality has on the analyzed machine learning (ML) problem; the type of the ML task; the memory constraints while training and predicting phase.