Over the past decades, there has been an increasing concern about the prevalence of abusive and violent content in Hollywood movies. This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. By employing fine-tuned LLMs, we analyze subtitles for over a thousand movies categorised into four genres to examine the trends and shifts in emotional and abusive content over the past seven decades. Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies. Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters.

We present an Arabic crossword puzzle generator from a given text that utilizes advanced language models such as GPT-4-Turbo, GPT-3.5-Turbo and Llama3-8B-Instruct, specifically developed for educational purposes, this innovative generator leverages a meticulously compiled dataset named Arabic-Clue-Instruct with over 50,000 entries encompassing text, answers, clues, and categories. This dataset is intricately designed to aid in the generation of pertinent clues linked to specific texts and keywords within defined categories. This project addresses the scarcity of advanced educational tools tailored for the Arabic language, promoting enhanced language learning and cognitive development. By providing a culturally and linguistically relevant tool, our objective is to make learning more engaging and effective through gamification and interactivity. Integrating state-of-the-art artificial intelligence with contemporary learning methodologies, this tool can generate crossword puzzles from any given educational text, thereby facilitating an interactive and enjoyable learning experience. This tool not only advances educational paradigms but also sets a new standard in interactive and cognitive learning technologies. The model and dataset are publicly available.

Revival is an innovative live audiovisual performance and music improvisation by our artist collective K-Phi-A, blending human and AI musicianship to create electronic music with audio-reactive visuals. The performance features real-time co-creative improvisation between a percussionist, an electronic music artist, and AI musical agents. Trained in works by deceased composers and the collective's compositions, these agents dynamically respond to human input and emulate complex musical styles. An AI-driven visual synthesizer, guided by a human VJ, produces visuals that evolve with the musical landscape. Revival showcases the potential of AI and human collaboration in improvisational artistic creation.

Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop.However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise.Thus, they still require lots of manual tuning to produce desirable outcomes in practice.To address this issue, we introduce MagicBrush, the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing.MagicBrush comprises over 10K manually annotated triplets (source image, instruction, target image), which supports trainining large-scale text-guided image editing models.We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation.We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations.The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs.

Understanding different human attributes and how they affect model behavior may become a standard need for all model creation and usage, from traditional computer vision tasks to the newest multimodal generative AI systems. In computer vision specifically, we have relied on datasets augmented with perceived attribute signals (eg, gender presentation, skin tone, and age) and benchmarks enabled by these datasets. Typically labels for these tasks come from human annotators. However, annotating attribute signals, especially skin tone, is a difficult and subjective task. Perceived skin tone is affected by technical factors, like lighting conditions, and social factors that shape an annotator's lived experience.This paper examines the subjectivity of skin tone annotation through a series of annotation experiments using the Monk Skin Tone (MST) scale \cite{Monk2022Monk}, a small pool of professional photographers, and a much larger pool of trained crowdsourced annotators.

President-elect Donald Trump touted his recent Cabinet picks as he prepares his White House return. The Fox & Friends co-hosts react. Sylvester Stallone is warning his fans after a fake trailer for "The Godfather Part 4" went viral online. Stallone, 78, took to social media to comment on the fan-made video creation. Lol this is definitely not to be taken seriously!" the Hollywood actor laughed and wrote on Instagram. His post included two photos of Stallone, one of him smoking a cigar and the second of the actor holding a gun. Sylvester Stallone sent a message to his fans after a fake trailer for "The Godfather Part 4" went viral online. The creator of the fake "Godfather 4" video trailer sent a message to viewers explaining how it was made. "Please note that this video is a concept trailer created solely for artistic and entertainment purposes.

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, for instance, news story summarization. Additionally, we propose a set of quantitative metrics to comprehensively evaluate and compare methodologies. With such a testbed, we further develop and benchmark several deep learning approaches to tackling this problem.

FILE - Chinese President Xi Jinping waves at an event to introduce new members of the Politburo Standing Committee at the Great Hall of the People in Beijing on Oct. 23, 2022. A man is seen using the OpenAI ChatGPT artificial intelligence chat website in this illustration photo on July 18, 2023. AMERICA MUST WIN: OpenAI's Chris Lehane is warning of America's shrinking lead in the artificial intelligence space as the company releases its economic blueprint and policy proposals for the U.S. 'ONCE UPON A TIME': A happily-ever-after with someone a woman believed was Hollywood hunk Brad Pitt quickly turned into a living nightmare. AI TRANSFORMER HOMES: AC Future, a leading developer of AI-enabled sustainable living solutions, has partnered with world-renowned Italian design house Pininfarina to create a groundbreaking collection of transformable living spaces. This innovative collaboration has resulted in three distinct products: AI-THd (AI Transformer Home Drivable), AI-THu (AI Transformer Home Unit) and AI-THt (AI Transformer Home Trailer).

Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are susceptible to unintended modifications of regions outside the targeted area, such as on the background or on distractor objects which have some semantic or visual relationship with the targeted object. According to our experimental findings, inaccurate cross-attention maps are at the root of this problem. Based on this observation, we propose \textit{Dynamic Prompt Learning} ( DPL) to force cross-attention maps to focus on correct \textit{noun} words in the text prompt.

The claim matching (CM) task can benefit an automated fact-checking pipeline by putting together claims that can be resolved with the same fact-check. In this work, we are the first to explore zero-shot and few-shot learning approaches to the task. We consider CM as a binary classification task and experiment with a set of instruction-following large language models (GPT-3.5-turbo, Gemini-1.5-flash, Mistral-7B-Instruct, and Llama-3-8B-Instruct), investigating prompt templates. We introduce a new CM dataset, ClaimMatch, which will be released upon acceptance. We put LLMs to the test in the CM task and find that it can be tackled by leveraging more mature yet similar tasks such as natural language inference or paraphrase detection. We also propose a pipeline for CM, which we evaluate on texts of different lengths.