Kris Jenner gets compared to her daughters with new look; "Mormon Wives" stars' say fame tore their friendships apart. Welcome to the Fox News Entertainment Newsletter. Billy Joel cancels all shows after a rare brain disorder diagnosis. MUSIC ON HOLD - Billy Joel cancels all concerts due to brain disorder diagnosis. ROCK TRAGEDY - Music exec and rock drummer among those killed in San Diego plane crash.

She needs to be spoken to slowly and clearly, as you'd talk to an aged relative with diminished faculties. '"Alexa, how long do wasps live for?" "Alexa, how long do wasps live if you hit them with a tea towel and then a saucepan?" In September 2016, a new presence appears in our house, squatting on the kitchen counter between the kettle and the coffee machine. It is blandly futuristic, a minimal cylinder with an LED ring that glows blue to alert us to the fact that it is ready, poised to answer our questions or carry out our instructions, as long as those instructions are clearly stated and fall within a narrow band of available "skills".

But opting out is impossible to do without AI transparency. The plan is a charter for theft, since creatives would have no idea who is taking what, when and from whom. When the government stoops to a preferred outcome that undermines the moral right to your work and income, you might reasonably be angered. As Elton John said last weekend: "The government have no right to do this to my songs. They have no right to do it to anybody's songs, or anybody's prose."

Tyler Saltsman, founder and CEO of EdgeRunner AI, warns that creating artificial general intelligence could "destroy the world as we know it." Whether it's powering your phone's autocorrect or helping someone create a new recipe with a few words, artificial intelligence (AI) is everywhere right now. But if you're still nodding along when someone mentions "neural networks" or "generative AI," you're not alone. Today I am breaking down five buzzy AI terms that you've probably seen in headlines, group chats or app updates, minus the tech talk. Understanding these basics will help you talk AI with confidence, even if you're not a programmer.

Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the "Decoding Broken Hearts" initiative on "Special Report." Imagine having a tireless guardian watching over you during your subway commute. New York City's subway system is testing artificial intelligence to boost security and reduce crime. Michael Kemper, a 33-year NYPD veteran and the chief security officer for the Metropolitan Transportation Authority (MTA), which is the largest transit agency in the United States, is leading the rollout of AI software designed to spot suspicious behavior as it happens. The MTA says this technology represents the future of subway surveillance and reassures riders that privacy concerns are being taken seriously.

While artificial intelligence (AI) is playing a bigger role than ever in Hollywood, award-winning actor Kelsey Grammer is warning it may be "dangerous." The "Karen: A Brother Remembers" author opened up about his growing concern over AI deepfakes and the potential blurred lines between reality and manipulation. "What I'm a little sad about is our prevalence these days to come up with so many, as they try to say deepfakes," he told Fox News Digital. "You know, the ones who say it usually are the ones who are actually doing it. "Karen: A Brother Remembers" author Kelsey Grammer warns about the dangers of AI deepfakes in Hollywood, expressing concerns over the blurred lines between reality and manipulation. AI-generated images, known as "deepfakes," often involve editing videos or photos of people to make them look like someone else by using artificial intelligence. While the "Frasier" star has acknowledged AI to be beneficial in some capacity, including in the medical field, Grammer shared his reservations about how the system can potentially fabricate someone's identity in seconds. WATCH: KELSEY GRAMMER WARNS AI WILL'NEVER REFLECT THE SAME SPONTANEITY' AS HUMANS "I recognize the validity and the potential in AI," Grammer said. "I recognize the validity and the potential in AI, especially in medicine and a number of other things." Grammer warned, "But AI still is...

Continual post-training adapts a single text-to-image diffusion model to learn new tasks without incurring the cost of separate models, but naive post-training causes forgetting of pretrained knowledge and undermines zero-shot compositionality. We observe that the absence of a standardized evaluation protocol hampers related research for continual post-training. To address this, we introduce T2I-ConBench, a unified benchmark for continual post-training of text-to-image models. T2I-ConBench focuses on two practical scenarios, item customization and domain enhancement, and analyzes four dimensions: (1) retention of generality, (2) target-task performance, (3) catastrophic forgetting, and (4) cross-task generalization. It combines automated metrics, human-preference modeling, and vision-language QA for comprehensive assessment. We benchmark ten representative methods across three realistic task sequences and find that no approach excels on all fronts. Even joint "oracle" training does not succeed for every task, and cross-task generalization remains unsolved. We release all datasets, code, and evaluation tools to accelerate research in continual post-training for text-to-image models.

This paper introduces a method for detecting inappropriately targeting language in online conversations by integrating crowd and expert annotations with ChatGPT. We focus on English conversation threads from Reddit, examining comments that target individuals or groups. Our approach involves a comprehensive annotation framework that labels a diverse data set for various target categories and specific target words within the conversational context. We perform a comparative analysis of annotations from human experts, crowd annotators, and ChatGPT, revealing strengths and limitations of each method in recognizing both explicit hate speech and subtler discriminatory language. Our findings highlight the significant role of contextual factors in identifying hate speech and uncover new categories of targeting, such as social belief and body image. We also address the challenges and subjective judgments involved in annotation and the limitations of ChatGPT in grasping nuanced language. This study provides insights for improving automated content moderation strategies to enhance online safety and inclusivity.

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augmented reasoning approaches rely on separate retrieval models, limiting the LRM's role in retrieval to deciding when to retrieve and how to query. This separation not only increases hardware and operational costs but also leads to errors in the retrieval process due to the representation bottleneck, a phenomenon where the retriever's embedding space is not expressive enough to meet the generator's requirements. To address this, we shift our perspective from sequence-to-sequence matching to locating the answer-containing paths within the corpus, and propose a novel framework called FREESON (Retriever-FREE Retrieval-Augmented ReaSONing). This framework enables LRMs to retrieve relevant knowledge on their own by acting as both a generator and retriever. To achieve this, we introduce a variant of the MCTS algorithm specialized for the retrieval task, which we call CT-MCTS (Corpus-Traversing Monte Carlo Tree Search). In this algorithm, LRMs traverse through the corpus toward answer-containing regions. Our results on five open-domain QA benchmarks, including single-hop and multi-hop questions, show that FREESON achieves an average improvement of 14.4% in EM and F1 over four multi-step reasoning models with a separate retriever, and it also performs comparably to the strongest baseline, surpassing it by 3% on PopQA and 2WikiMultihopQA.

Recently, pre-trained models for music information retrieval based on self-supervised learning (SSL) are becoming popular, showing success in various downstream tasks. However, there is limited research on the specific meanings of the encoded information and their applicability. Exploring these aspects can help us better understand their capabilities and limitations, leading to more effective use in downstream tasks. In this study, we analyze the advanced music representation model MusicFM and the newly emerged SSL model MuQ. We focus on three main aspects: (i) validating the advantages of SSL models across multiple downstream tasks, (ii) exploring the specialization of layer-wise information for different tasks, and (iii) comparing performance differences when selecting specific layers. Through this analysis, we reveal insights into the structure and potential applications of SSL models in music information retrieval.