Othering, the act of portraying outgroups as fundamentally different from the ingroup, often escalates into framing them as existential threats--fueling intergroup conflict and justifying exclusion and violence. These dynamics are alarmingly pervasive, spanning from the extreme historical examples of genocides against minorities in Germany and Rwanda to the ongoing violence and rhetoric targeting migrants in the US and Europe. While concepts like hate speech and fear speech have been explored in existing literature, they capture only part of this broader and more nuanced dynamic which can often be harder to detect, particularly in online speech and propaganda. To address this challenge, we introduce a novel computational framework that leverages large language models (LLMs) to quantify othering across diverse contexts, extending beyond traditional linguistic indicators of hostility. Applying the model to real-world data from Telegram war bloggers and political discussions on Gab reveals how othering escalates during conflicts, interacts with moral language, and garners significant attention, particularly during periods of crisis. Our framework, designed to offer deeper insights into othering dynamics, combines with a rapid adaptation process to provide essential tools for mitigating othering's adverse impacts on social cohesion.

AI-generated content doesn't seem to have swayed recent European elections But there's still a risk it could in the future, say researchers. AI-generated falsehoods and deepfakes seem to have had no effect on election results in the UK, France, and the European Parliament this year, according to new research. Since the beginning of the generative-AI boom, there has been widespread fear that AI tools could boost bad actors' ability to spread fake content with the potential to interfere with elections or even sway the results. Such worries were particularly heightened this year, when billions of people were expected to vote in over 70 countries. Those fears seem to have been unwarranted, says Sam Stockwell, the researcher at the Alan Turing Institute who conducted the study . He focused on three elections over a four-month period from May to August 2024, collecting data on public reports and news articles on AI misuse.

It's hard not to feel the ripple effect when big shifts happen. One such shift came Wednesday when Lionsgate--the studio responsible for the John Wick, Hunger Games, and Twilight franchises--announced it had teamed up with artificial intelligence firm Runway for a "first-of-its-kind partnership" that would give the AI firm access to the studio's archives in order to create a custom AI tool for preproduction and postproduction on its film and TV shows. Runway's forthcoming tool will "help Lionsgate Studios, its filmmakers, directors, and other creative talent augment their work" and "generate cinematic video that can be further iterated using Runway's suite of controllable tools," according to a press release announcing the deal. If that sounds like it might pique the interest of those who have been watching AI's influence on creatives' work, it did. If anything, the new deal could serve as a test of the AI protections that unions like the Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) got in their contract negotiations with studios last year.

Stay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future with Fox News here. This article was written by Fox News staff.

Remember when the Writers Guild of America (WGA) and SAG-AFTRA went on strike for months, in great part to get protections against AI? Well, while they did get some stipulations in there, it's not stopping AI from coming to Hollywood anyways. Lionsgate, the studio behind the John Wick and Hunger Games franchises, has struck a deal with AI startup Runway, the Wall Street Journal first reported and Runway confirmed in a press release. The arrangement will allow Runway access to Lionsgate's content library in exchange for a fresh, custom AI model that the studio can use in production and editing. The deal is similar to recent (and equally icky feeling) ones with publishing houses such as TIME and Dotdash Meredith, but it is the first of its kind for the film and TV industry.

Lionsgate has signed a deal with the artificial intelligence research firm Runway, allowing it access to the company's large film and TV library to train a new generative model. According to the Wall Street Journal, the model will be "customized to Lionsgate's proprietary portfolio" which includes hit franchises such as John Wick, Saw and The Hunger Games. The aim is to help film-makers and other creatives "augment their work" through the use of AI. "Runway is a visionary, best-in-class partner who will help us utilize AI to develop cutting-edge, capital-efficient content creation opportunities," said Michael Burns, Lionsgate's vice-chair. "Several of our film-makers are already excited about its potential applications to their pre-production and post-production process. We view AI as a great tool for augmenting, enhancing and supplementing our current operations."

Recent advances in machine learning have paved the way for the development of musical and entertainment robots. However, human-robot cooperative instrument playing remains a challenge, particularly due to the intricate motor coordination and temporal synchronization. In this paper, we propose a theoretical framework for human-robot cooperative piano playing based on non-verbal cues. First, we present a music improvisation model that employs a recurrent neural network (RNN) to predict appropriate chord progressions based on the human's melodic input. Second, we propose a behavior-adaptive controller to facilitate seamless temporal synchronization, allowing the cobot to generate harmonious acoustics. The collaboration takes into account the bidirectional information flow between the human and robot. We have developed an entropy-based system to assess the quality of cooperation by analyzing the impact of different communication modalities during human-robot collaboration. Experiments demonstrate that our RNN-based improvisation can achieve a 93\% accuracy rate. Meanwhile, with the MPC adaptive controller, the robot could respond to the human teammate in homophony performances with real-time accompaniment. Our designed framework has been validated to be effective in allowing humans and robots to work collaboratively in the artistic piano-playing task.

Although recommenders can ship items to users automatically based on the users' preferences, they often cause unfairness to groups or individuals. For instance, when users can be divided into two groups according to a sensitive social attribute and there is a significant difference in terms of activity between the two groups, the learned recommendation algorithm will result in a recommendation gap between the two groups, which causes group unfairness. In this work, we propose a novel recommendation algorithm named Diffusion-based Fair Recommender (DifFaiRec) to provide fair recommendations. DifFaiRec is built upon the conditional diffusion model and hence has a strong ability to learn the distribution of user preferences from their ratings on items and is able to generate diverse recommendations effectively. To guarantee fairness, we design a counterfactual module to reduce the model sensitivity to protected attributes and provide mathematical explanations. The experiments on benchmark datasets demonstrate the superiority of DifFaiRec over competitive baselines.

In this paper, we introduce Geometry-Inverse-Meet-Pixel-Insert, short for GEO, an exceptionally versatile image editing technique designed to cater to customized user requirements at both local and global scales. Our approach seamlessly integrates text prompts and image prompts to yield diverse and precise editing outcomes. Notably, our method operates without the need for training and is driven by two key contributions: (i) a novel geometric accumulation loss that enhances DDIM inversion to faithfully preserve pixel space geometry and layout, and (ii) an innovative boosted image prompt technique that combines pixel-level editing for text-only inversion with latent space geometry guidance for standard classifier-free reversion. Leveraging the publicly available Stable Diffusion model, our approach undergoes extensive evaluation across various image types and challenging prompt editing scenarios, consistently delivering high-fidelity editing results for real images.

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. While prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how cross-functional collaboration unfolds between AI professionals and journalists. Through interviews with 17 journalists, 6 AI technologists, and 3 AI workers with cross-functional experience from leading news organizations, we investigate the current practices, challenges, and opportunities for cross-functional collaboration around AI in today's news industry. We first study how journalists and AI professionals perceive existing cross-collaboration strategies. We further explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry.