After an anti-Israel protest escalated at New York University on Monday – requiring city police presence – the university released a statement explaining while it supports students' rights to protest, safety remains its priority. HATE RAGES – Elite university reverses on NYPD presence as antisemitic mob takes over campus and more top headlines. POISON IVY – Columbia University shifts to hybrid learning as escalating anti-Israel protests cause safety concerns. NO COFFEE, NO PEACE – Angry Alec Baldwin smacks anti-Israel agitator's phone after hounding actor. TRUMP TRIAL – Judge to hear gag order arguments after former president's all-caps rant on social media.

'Terminator' stars Arnold Schwarzenegger and Linda Hamilton reunited to promote the new sequel'Terminator: Dark Fate.' Linda Hamilton became a star after appearing in 1984's sci-fi classic "The Terminator," alongside Arnold Schwarzenegger. But after appearing in the latest film in the franchise, "Terminator: Dark Fate" in 2019, the 67-year-old was ready to retire – not just from her iconic character, Sarah Connor, but the industry as well. "I don't do a lot of regret. I think in the end, it holds true that we regret what we didn't do, not what we did," she told The Hollywood Reporter in a new interview. Speaking on "Dark Fate," she continued, "I'm very glad I went back. I loved [director Tim Miller], I love my ladies [Mackenzie Davis and Natalia Reyes], and while I can't say I love the film, that's because I was so attached to it. I felt like it was too fast. But we did so much good work, and it was the greatest time of my life, and the worst time of my life, all rolled into one film. Linda Hamilton told The Hollywood Reporter that working on "Terminator: Dark Fate" was "the greatest time of my life, and the worst time of my life, all rolled into one film." "I was 63 or whatever I was, and it was the hardest shoot.

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. Researchers are warning that facial recognition technologies are "more threatening than previously thought" and pose "serious challenges to privacy" after a study found that artificial intelligence can be successful in predicting a person's political orientation based on images of expressionless faces. A recent study published in the journal American Psychologist says an algorithm's ability to accurately guess one's political views is "on par with how well job interviews predict job success, or alcohol drives aggressiveness." Lead author Michal Kosinski told Fox News Digital that 591 participants filled out a political orientation questionnaire before the AI captured what he described as a numerical "fingerprint" of their faces and compared them to a database of their responses to predict their views.

"It's a complete offering... where the upfront cost is very reduced. So it's quite affordable for these companies to get access to automation and start to get the benefits out of it. And since the technology is very flexible and scalable, you can continue to basically increase volume by adding more robots rather than more storage capacity," says Carlos Fernández, chief product officer at AutoStore.

To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale. With the pipeline, we construct CultureBank, a knowledge base built upon users' self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. Unlike previous cultural knowledge resources, CultureBank contains diverse views on cultural descriptors to allow flexible interpretation of cultural knowledge, and contextualized cultural scenarios to help grounded evaluation. With CultureBank, we evaluate different LLMs' cultural awareness, and identify areas for improvement. We also fine-tune a language model on CultureBank: experiments show that it achieves better performances on two downstream cultural tasks in a zero-shot setting. Finally, we offer recommendations based on our findings for future culturally aware language technologies. The project page is https://culturebank.github.io . The code and model is at https://github.com/SALT-NLP/CultureBank . The released CultureBank dataset is at https://huggingface.co/datasets/SALT-NLP/CultureBank .

A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various NLP and multi-mode tasks to answer users' arbitrary questions and specific-domain content generation. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and reducing the cost of manual annotation. In this survey, we conduct a comprehensive investigation of existing LLM studies on graph data, which summarizes the relevant graph analytics tasks solved by advanced LLM models and points out the existing remaining challenges and future directions. Specifically, we study the key problems of LLM-based generative graph analytics (LLM-GGA) with three categories: LLM-based graph query processing (LLM-GQP), LLM-based graph inference and learning (LLM-GIL), and graph-LLM-based applications. LLM-GQP focuses on an integration of graph analytics techniques and LLM prompts, including graph understanding and knowledge graph (KG) based augmented retrieval, while LLM-GIL focuses on learning and reasoning over graphs, including graph learning, graph-formed reasoning and graph representation. We summarize the useful prompts incorporated into LLM to handle different graph downstream tasks. Moreover, we give a summary of LLM model evaluation, benchmark datasets/tasks, and a deep pro and cons analysis of LLM models. We also explore open problems and future directions in this exciting interdisciplinary research area of LLMs and graph analytics.

Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that involve learning schemes. A poisoning attack is where an adversary injects carefully crafted data into the process of training a model, with the goal of manipulating the system's final recommendations. Based on recent advancements in artificial intelligence, such attacks have gained importance recently. While numerous countermeasures to poisoning attacks have been developed, they have not yet been systematically linked to the properties of the attacks. Consequently, assessing the respective risks and potential success of mitigation strategies is difficult, if not impossible. This survey aims to fill this gap by primarily focusing on poisoning attacks and their countermeasures. This is in contrast to prior surveys that mainly focus on attacks and their detection methods. Through an exhaustive literature review, we provide a novel taxonomy for poisoning attacks, formalise its dimensions, and accordingly organise 30+ attacks described in the literature. Further, we review 40+ countermeasures to detect and/or prevent poisoning attacks, evaluating their effectiveness against specific types of attacks. This comprehensive survey should serve as a point of reference for protecting recommender systems against poisoning attacks. The article concludes with a discussion on open issues in the field and impactful directions for future research. A rich repository of resources associated with poisoning attacks is available at https://github.com/tamlhp/awesome-recsys-poisoning.

Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity for reciprocal exchange and prolonged interactions. This has led to growing concerns about harms from AI persuasion and how they can be mitigated, highlighting the need for a systematic study of AI persuasion. The current definitions of AI persuasion are unclear and related harms are insufficiently studied. Existing harm mitigation approaches prioritise harms from the outcome of persuasion over harms from the process of persuasion. In this paper, we lay the groundwork for the systematic study of AI persuasion. We first put forward definitions of persuasive generative AI. We distinguish between rationally persuasive generative AI, which relies on providing relevant facts, sound reasoning, or other forms of trustworthy evidence, and manipulative generative AI, which relies on taking advantage of cognitive biases and heuristics or misrepresenting information. We also put forward a map of harms from AI persuasion, including definitions and examples of economic, physical, environmental, psychological, sociocultural, political, privacy, and autonomy harm. We then introduce a map of mechanisms that contribute to harmful persuasion. Lastly, we provide an overview of approaches that can be used to mitigate against process harms of persuasion, including prompt engineering for manipulation classification and red teaming. Future work will operationalise these mitigations and study the interaction between different types of mechanisms of persuasion.

Previous studies on music style transfer have mainly focused on one-to-one style conversion, which is relatively limited. When considering the conversion between multiple styles, previous methods required designing multiple modes to disentangle the complex style of the music, resulting in large computational costs and slow audio generation. The existing music style transfer methods generate spectrograms with artifacts, leading to significant noise in the generated audio. To address these issues, this study proposes a music style transfer framework based on diffusion models (DM) and uses spectrogram-based methods to achieve multi-to-multi music style transfer. The GuideDiff method is used to restore spectrograms to high-fidelity audio, accelerating audio generation speed and reducing noise in the generated audio. Experimental results show that our model has good performance in multi-mode music style transfer compared to the baseline and can generate high-quality audio in real-time on consumer-grade GPUs.

Hollywood has really begun flexing its video game adaptation muscle in the wake of the spectacular success of the Fallout TV show and The Super Mario Bros. Movie. Even indie publishers are getting some of those sweet, sweet development contracts. The hit third-person shooter El Paso, Elsewhere is being adapted into a feature length film, as reported by Deadline. Academy Award nominee LaKeith Stanfield is in talks to both star and produce. Stanfield is known for a slew of great films, like Sorry to Bother You, Judas and the Black Messiah and The Book of Clarence, among others.