UPenn Wharton School Associate Professor Ethan Mollick weighs in on the Biden White House's new guidelines for artificial intelligence in the workplace on'Fox News Live.' Fox News' "Antisemitism Exposed" newsletter brings you stories on the rising anti-Jewish prejudice across the U.S. and the world. IN TODAY'S NEWSLETTER: - ADL issues'urgent call' alleging anti-Israel bias in 4 AI large language models - Georgetown grad student accused of spreading Hamas propaganda - Israeli hostages' families sue Mahmoud Khalil, Columbia organizers as alleged'Hamas' propaganda arm' The ADL's report found that virtually all artifical intelligence tools displayed a built-in bias against Israel and Jews. TOP STORY: A new report from the Anti-Defamation League (ADL) shows anti-Jewish and anti-Israel biases among AI large language models. The organization used thousands of AI queries to find "a concerning inability to accurately reject antisemitic tropes and conspiracy theories." Additionally, every LLM except GPT showed bias regarding Jewish conspiracy theories and even more bias against Israel than Jews, the ADL said.

Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents are shown to deliver more accurate and contextualized responses. We empirically validate the efficacy of our proposed approach in three task scenarios; conversational recommendation, question answering and event summarization. On the LLM-REDIAL dataset, MemInsight boosts persuasiveness of recommendations by up to 14%. Moreover, it outperforms a RAG baseline by 34% in recall for LoCoMo retrieval. Our empirical results show the potential of MemInsight to enhance the contextual performance of LLM agents across multiple tasks.

Deep generative models have been used in style transfer tasks for images. In this study, we adapt and improve CycleGAN model to perform music style transfer on Jazz and Classic genres. By doing so, we aim to easily generate new songs, cover music to different music genres and reduce the arrangements needed in those processes. We train and use music genre classifier to assess the performance of the transfer models. To that end, we obtain 87.7% accuracy with Multi-layer Perceptron algorithm. To improve our style transfer baseline, we add auxiliary discriminators and triplet loss to our model. According to our experiments, we obtain the best accuracies as 69.4% in Jazz to Classic task and 39.3% in Classic to Jazz task with our developed genre classifier. We also run a subjective experiment and results of it show that the overall performance of our transfer model is good and it manages to conserve melody of inputs on the transferred outputs. Our code is available at https://github.com/ fidansamet/tune-it-up

Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music. In this paper, we systematically review the research progress in the field of vision-to-music generation. We first analyze the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. We then summarize the existing methodologies on vision-to-music generation from the architecture perspective. A detailed review of common datasets and evaluation metrics is provided. Finally, we discuss current challenges and promising directions for future research. We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. To follow latest works and foster further innovation in this field, we are continuously maintaining a GitHub repository at https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.

Accurate camera calibration is a fundamental task for 3D perception, especially when dealing with real-world, in-the-wild environments where complex optical distortions are common. Existing methods often rely on pre-rectified images or calibration patterns, which limits their applicability and flexibility. In this work, we introduce a novel framework that addresses these challenges by jointly modeling camera intrinsic and extrinsic parameters using a generic ray camera model. Unlike previous approaches, AlignDiff shifts focus from semantic to geometric features, enabling more accurate modeling of local distortions. We propose AlignDiff, a diffusion model conditioned on geometric priors, enabling the simultaneous estimation of camera distortions and scene geometry. To enhance distortion prediction, we incorporate edge-aware attention, focusing the model on geometric features around image edges, rather than semantic content. Furthermore, to enhance generalizability to real-world captures, we incorporate a large database of ray-traced lenses containing over three thousand samples. This database characterizes the distortion inherent in a diverse variety of lens forms. Our experiments demonstrate that the proposed method significantly reduces the angular error of estimated ray bundles by ~8.2 degrees and overall calibration accuracy, outperforming existing approaches on challenging, real-world datasets.

We study the ability of large language models (LLMs) to generate comprehensive and accurate book summaries solely from their internal knowledge, without recourse to the original text. Employing a diverse set of books and multiple LLM architectures, we examine whether these models can synthesize meaningful narratives that align with established human interpretations. Evaluation is performed with a LLM-as-a-judge paradigm: each AI-generated summary is compared against a high-quality, human-written summary via a cross-model assessment, where all participating LLMs evaluate not only their own outputs but also those produced by others. This methodology enables the identification of potential biases, such as the proclivity for models to favor their own summarization style over others. In addition, alignment between the human-crafted and LLM-generated summaries is quantified using ROUGE and BERTScore metrics, assessing the depth of grammatical and semantic correspondence. The results reveal nuanced variations in content representation and stylistic preferences among the models, highlighting both strengths and limitations inherent in relying on internal knowledge for summarization tasks. These findings contribute to a deeper understanding of LLM internal encodings of factual information and the dynamics of cross-model evaluation, with implications for the development of more robust natural language generative systems.

Celebrity matchmaker Alessandra Conti told Fox News Digital that Garner and Affleck are incredible co-parents. Gwyneth Paltrow and Nikki Glaser are spilling the tea when it comes to their connections to Ben Affleck. During a recent episode of Paltrow's "Goop Podcast," the duo openly discussed Glaser's past history of using Raya, an exclusive dating app. While discussing her 2025 Golden Globe Awards opening monologue in which she joked about Affleck yelling the titles of movies "after he orgasms," Glaser said, "When I used to be on Raya and [Ben] would come across, [I would give him a] very concentrated check mark'yes' and, like, never [got] it back." GWYNETH PALTROW SAYS BEN AFFLECK WAS'EXCELLENT' IN BED COMPARED TO BRAD PITT Nikki Glaser told Gwyneth Paltrow she once tried to hook up with the actress' ex, Ben Affleck.

Alpha School co-founder Mackenzie Price and a junior at the school Elle Kristine join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. Will A.I. make schools'obsolete,' or does it present a new'opportunity' for the education system? STUDY BUDDY: A Texas private school is seeing student test scores soar to new heights following the implementation of an artificial intelligence "tutor." 'URGENT CALL': A new report from the Anti-Defamation League shows anti-Jewish and anti-Israel biases among AI large language models. ROBOTS SWARM: The automotive industry is undergoing a seismic shift driven by the integration of AI-powered humanoid robots into production lines. UBTech Robotics, in collaboration with Zeekr, has pioneered a groundbreaking initiative where swarm robots work together to build cars faster and more efficiently than ever before.

The latest development tips the scales in favor of use: A judge has rejected Universal Music Group, ABKCO and other music publishers' preliminary bid to block Anthropic from using their lyrics to train its AI assistant Claude, Reuters reports. US District Judge Eumi Lee ruled that UMG and co had submitted too broad a request and failed to demonstrate that Anthropic's use of the lyrics caused the companies "irreparable harm." Lee stated, "Publishers are essentially asking the Court to define the contours of a licensing market for AI training where the threshold question of fair use remains unsettled." The two sides came to a partial agreement in January of this year.

It's one of the most difficult decisions a new parent can make – what shall we call our baby? Now, a huge analysis has revealed that names associated with intelligence are dying out, while those linked to beauty, elegance or strength are on the up. The study, carried out by The Economist, scrutinised the names of nearly 400 million infants born in Britain and the US over the last 143 years. Researchers used a large language model – the type of AI that powers the likes of ChatGPT – for their analysis. They fed it with an enormous amount of text taken from the internet and asked it to identify the five most common terms linked with each name.