Most social media users come from non-English speaking countries in the Global South. Despite the widespread prevalence of harmful content in these regions, current moderation systems repeatedly struggle in low-resource languages spoken there. In this work, we examine the challenges AI researchers and practitioners face when building moderation tools for low-resource languages. We conducted semi-structured interviews with 22 AI researchers and practitioners specializing in automatic detection of harmful content in four diverse low-resource languages from the Global South. These are: Tamil from South Asia, Swahili from East Africa, Maghrebi Arabic from North Africa, and Quechua from South America. Our findings reveal that social media companies' restrictions on researchers' access to data exacerbate the historical marginalization of these languages, which have long lacked datasets for studying online harms. Moreover, common preprocessing techniques and language models, predominantly designed for data-rich English, fail to account for the linguistic complexity of low-resource languages. This leads to critical errors when moderating content in Tamil, Swahili, Arabic, and Quechua, which are morphologically richer than English. Based on our findings, we establish that the precarities in current moderation pipelines are rooted in deep systemic inequities and continue to reinforce historical power imbalances. We conclude by discussing multi-stakeholder approaches to improve moderation for low-resource languages.

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Summary and Contributions: This paper proposes a novel pipeline approach for improving piano music/audio generation from silent videos with a top-view of a pianist's fingers playing on a keyboard. Prior work [27] used an end-to-end approach to directly predict a symbolic piano performance from video using ResNets. This paper points out there's a lot of mismatch between the video and music/audio streams and hence the processing requires multiple stages of transformation. The proposed pipeline consists of three interpretable components / stages. Video2Roll consists of three stages.

Nearly every TV on the market today is a smart TV, but not every operating system is a winner. A media streaming device lets you pair whichever user interface you prefer with just about any screen that has an HDMI port. In some cases, such as with older or less expensive smart TVs, a streaming stick or dongle could even be speedier and less glitchy than your TV's built-in system. At home, these handy gadgets make it easier for cord cutters to watch the millions of hours of content streaming services provide without cable. And while traveling, a streaming player lets you watch your preferred content on hotel sets (without painstakingly typing in a bunch of passwords or activation codes). We tested out streaming players from Roku, Google, Apple, Amazon and more, gauging the usability and the performance of each to come up with our list of the best streaming devices you can buy. Google's TV Streamer, the Apple TV 4K, Amazon's Fire TV Sticks and Roku devices are the most popular players in the space.

OpenAI did not respond to questions about its lobbying efforts. But perhaps more important, the disclosure is a clear signal of the company's arrival as a political player, as its first year of serious lobbying ends and Republican control of Washington begins. While OpenAI's lobbying spending is still dwarfed by its peers'--Meta tops the list of Big Tech spenders, with more than 24 million in 2024--the uptick comes as it and other AI companies have helped redraw the shape of AI policy. For the past few years, AI policy has been something like a whack-a-mole response to the risks posed by deepfakes and misinformation. But over the last year, AI companies have started to position the success of the technology as pivotal to national security and American competitiveness, arguing that the government must therefore support the industry's growth.

In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful information from long user behavior sequences, even within their context limits. To tackle this, we propose ReLLaX (Retrieval-enhanced Large Language models Plus), a framework offering optimization across data, prompt, and parameter levels. At the data level, we introduce Semantic User Behavior Retrieval (SUBR) to reduce sequence heterogeneity, making it easier for LLMs to extract key information. For prompt-level enhancement, we employ Soft Prompt Augmentation (SPA) to inject collaborative knowledge, aligning item representations with recommendation tasks and improving LLMs's exploration of item relationships. Finally, at the parameter level, we propose Component Fully-interactive LoRA (CFLoRA), which enhances LoRA's expressiveness by enabling interactions between its components, allowing better capture of sequential information. Moreover, we present new perspectives to compare current LoRA-based LLM4Rec methods, i.e. from both a composite and a decomposed view. We theoretically demonstrate that the ways they employ LoRA for recommendation are degraded versions of our CFLoRA, with different constraints on atom component interactions. Extensive experiments on three public datasets demonstrate ReLLaX's superiority over existing baselines and its ability to mitigate lifelong sequential behavior incomprehension effectively.

Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recent advances in automated decision-making with language agent-based societies, this paper introduces FilmAgent, a novel LLM-based multi-agent collaborative framework for end-to-end film automation in our constructed 3D virtual spaces. FilmAgent simulates various crew roles, including directors, screenwriters, actors, and cinematographers, and covers key stages of a film production workflow: (1) idea development transforms brainstormed ideas into structured story outlines; (2) scriptwriting elaborates on dialogue and character actions for each scene; (3) cinematography determines the camera setups for each shot. A team of agents collaborates through iterative feedback and revisions, thereby verifying intermediate scripts and reducing hallucinations. We evaluate the generated videos on 15 ideas and 4 key aspects. Human evaluation shows that FilmAgent outperforms all baselines across all aspects and scores 3.98 out of 5 on average, showing the feasibility of multi-agent collaboration in filmmaking. Further analysis reveals that FilmAgent, despite using the less advanced GPT-4o model, surpasses the single-agent o1, showing the advantage of a well-coordinated multi-agent system. Lastly, we discuss the complementary strengths and weaknesses of OpenAI's text-to-video model Sora and our FilmAgent in filmmaking.

The prevalence of harmful content on social media platforms poses significant risks to users and society, necessitating more effective and scalable content moderation strategies. Current approaches rely on human moderators, supervised classifiers, and large volumes of training data, and often struggle with scalability, subjectivity, and the dynamic nature of harmful content (e.g., violent content, dangerous challenge trends, etc.). To bridge these gaps, we utilize Large Language Models (LLMs) to undertake few-shot dynamic content moderation via in-context learning. Through extensive experiments on multiple LLMs, we demonstrate that our few-shot approaches can outperform existing proprietary baselines (Perspective and OpenAI Moderation) as well as prior state-of-the-art few-shot learning methods, in identifying harm. We also incorporate visual information (video thumbnails) and assess if different multimodal techniques improve model performance. Our results underscore the significant benefits of employing LLM based methods for scalable and dynamic harmful content moderation online.

In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grained labels, we investigate hybrid losses, such as generalised triplet and cross-entropy losses, to enforce similarity between labels within a multi-task learning framework. We propose metrics to evaluate the embedding space structure and assess the model's ability to generalise to unseen classes, that is, to infer similar classes for data belonging to unseen categories. Our experiments on OrchideaSOL, a four-level hierarchical instrument sound dataset with nearly 200 detailed categories, demonstrate that the proposed hybrid losses outperform previous works in classification, retrieval, embedding space structure, and generalisation.

Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with scalability and adapting to new forms of harm. To address these challenges, we propose a novel re-ranking approach using Large Language Models (LLMs) in zero-shot and few-shot settings. Our method dynamically assesses and re-ranks content sequences, effectively mitigating harmful content exposure without requiring extensive labeled data. Alongside traditional ranking metrics, we also introduce two new metrics to evaluate the effectiveness of re-ranking in reducing exposure to harmful content. Through experiments on three datasets, three models and across three configurations, we demonstrate that our LLM-based approach significantly outperforms existing proprietary moderation approaches, offering a scalable and adaptable solution for harm mitigation.