The potential for negative impacts of AI has rapidly become more pervasive around the world, and this has intensified a need for responsible AI governance. While many regulatory bodies endorse risk-based approaches and a multitude of risk mitigation practices are proposed by companies and academic scholars, these approaches are commonly expert-centered and thus lack the inclusion of a significant group of stakeholders. Ensuring that AI policies align with democratic expectations requires methods that prioritize the voices and needs of those impacted. In this work we develop a participative and forward-looking approach to inform policy-makers and academics that grounds the needs of lay stakeholders at the forefront and enriches the development of risk mitigation strategies. Our approach (1) maps potential mitigation and prevention strategies of negative AI impacts that assign responsibility to various stakeholders, (2) explores the importance and prioritization thereof in the eyes of laypeople, and (3) presents these insights in policy fact sheets, i.e., a digestible format for informing policy processes. We emphasize that this approach is not targeted towards replacing policy-makers; rather our aim is to present an informative method that enriches mitigation strategies and enables a more participatory approach to policy development.

Roli just introduced the simply-named Piano at NAMM, a 49-key smart keyboard that's primarily intended for learners, but has some neat bells and whistles for experienced musicians. It features light-up keys across all octaves, to help newbies get a handle on chords. These keys will also glow to show scales, arpeggios and more. For veterans, the Roli Piano offers per-key pitch bend and polyphonic aftertouch, which should make for expressive playing. It also tracks fingers in four different ways while playing.

The 2025 Oscar nominations were announced on Thursday, and two New Yorker films are among the contenders. "Incident," which uses body-camera and surveillance footage to examine a police shooting in Chicago, is nominated in the Documentary Short Film category, while "I'm Not a Robot," a darkly humorous Dutch film about a woman taking a series of CAPTCHA tests, is nominated for best Live Action Short. Seventeen previous New Yorker films have been nominated for Academy Awards; a victory at this year's ceremony, scheduled for March 2nd in Los Angeles, would be the magazine's first win. "Incident," directed by Bill Morrison, who produced with Jamie Kalven, chronicles a police killing and its aftermath. On a Chicago sidewalk, an African American man named Harith (Snoop) Augustus is questioned and then pursued by a foot patrol after leaving the barbershop where he works; after a brief scuffle, he is fatally wounded.

Pope Francis has warned global leaders in Davos that artificial intelligence raises "critical concerns" about humanity's future and it could exacerbate a growing "crisis of truth". Francis said governments and businesses must exercise "due diligence and vigilance" to navigate the complexities of AI. In a written address at the World Economic Forum (WEF) in Switzerland on Thursday, the pope said AI could fuel the "growing crisis of truth in the public forum", as its output was almost indistinguishable from those of humans. "This technology is designed to learn and make certain choices autonomously, adapting to new situations and providing answers not foreseen by its programmers, thus raising fundamental questions about ethical responsibility, human safety, and the broader implications of these developments for society," he said in a statement read to Davos delegates by Cardinal Peter Turkson, a Vatican official. The pope has first-hand experience of artificial intelligence's ability to distort the truth – he is a popular subject in AI-generated deepfake images, including one of him embracing the singer Madonna and a second in a Balenciaga puffer jacket.

Though there are many subreddits which already disallow posts from social media, those built around professional sports in particular may have a big impact on referrals to X. That's because sports subreddits generally get a lot of content from links to athletes, analysts and journalists who spend a lot of time posting online. For example, the top two most popular posts of all time on the NBA subreddits are screenshots taken from X, while three of the top ten most popular posts of all time on the AEW wrestling subreddit are screenshots from the platform. And gaming subreddits have a similar story, with the top posts on the Animal Crossing and Kingdom Hearts communities both screenshots from X. But that is not to say the bans will necessarily be permanent - Reddit is known for this sort of community movement to protest against wider issues, which doesn't always work out. In 2023, thousands of communities "went dark" to contest changes to how the platform was being run. Some of the biggest Reddit communities then began only allowing photos and videos of comedian John Oliver, following comments from disgruntled users.

Detailed comments: Contribution 1 [Significance: High]: New approach for learning and generating multi-field documents i.e. documents where not only a "body" of text is to be generated, but which also contain other fields such as domain, date, authors and headline. The approach is a language modelling approach where these fields are used as context. The approach consists in ordering the fields into two subsets during the training in an attempt to address issues such as expensive marginalization or dealing with a large number of field orderings during inference time. The approach seems novel to me as I am not personally aware of previous work that approaches this problem in this way. Question: It wasn't clear to me if some of the fields have some restricted domains from which they can be sampled.

Document level Urdu Sentiment Analysis (SA) is a challenging Natural Language Processing (NLP) task as it deals with large documents in a resource-poor language. In large documents, there are ample amounts of words that exhibit different viewpoints. Deep learning (DL) models comprise of complex neural network architectures that have the ability to learn diverse features of the data to classify various sentiments. Besides audio, image and video classification; DL algorithms are now extensively used in text-based classification problems. To explore the powerful DL techniques for Urdu SA, we have applied five different DL architectures namely, Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), Bidirectional Encoder Representation from Transformer (BERT). In this paper, we have proposed a DL hybrid model that integrates BiLSTM with Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN). The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pretrained Urdu word embeddings that are suitable for (SA) at the document level. Results of these techniques are evaluated and our proposed model outperforms all other DL techniques for Urdu SA. BiLSTM-SLMFCNN outperformed the baseline DL models and achieved 83{\%}, 79{\%}, 83{\%} and 94{\%} accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation. Recently launched models such as FLUX.1 and Ideogram2.0, along with others like Dall-E3 and Stable Diffusion 3, have demonstrated exceptional performance across various complex tasks, raising questions about whether T2I models are moving towards general-purpose applicability. Beyond traditional image generation, these models exhibit capabilities across a range of fields, including controllable generation, image editing, video, audio, 3D, and motion generation, as well as computer vision tasks like semantic segmentation and depth estimation. However, current evaluation frameworks are insufficient to comprehensively assess these models' performance across expanding domains. To thoroughly evaluate these models, we developed the IMAGINE-E and tested six prominent models: FLUX.1, Ideogram2.0, Midjourney, Dall-E3, Stable Diffusion 3, and Jimeng. Our evaluation is divided into five key domains: structured output generation, realism, and physical consistency, specific domain generation, challenging scenario generation, and multi-style creation tasks. This comprehensive assessment highlights each model's strengths and limitations, particularly the outstanding performance of FLUX.1 and Ideogram2.0 in structured and specific domain tasks, underscoring the expanding applications and potential of T2I models as foundational AI tools. This study provides valuable insights into the current state and future trajectory of T2I models as they evolve towards general-purpose usability. Evaluation scripts will be released at https://github.com/jylei16/Imagine-e.

This paper studies a text classification algorithm based on an improved Transformer to improve the performance and efficiency of the model in text classification tasks. Aiming at the shortcomings of the traditional Transformer model in capturing deep semantic relationships and optimizing computational complexity, this paper introduces a multi-level attention mechanism and a contrastive learning strategy. The multi-level attention mechanism effectively models the global semantics and local features in the text by combining global attention with local attention; the contrastive learning strategy enhances the model's ability to distinguish between different categories by constructing positive and negative sample pairs while improving the classification effect. In addition, in order to improve the training and inference efficiency of the model on large-scale text data, this paper designs a lightweight module to optimize the feature transformation process and reduce the computational cost. Experimental results on the dataset show that the improved Transformer model outperforms the comparative models such as BiLSTM, CNN, standard Transformer, and BERT in terms of classification accuracy, F1 score, and recall rate, showing stronger semantic representation ability and generalization performance. The method proposed in this paper provides a new idea for algorithm optimization in the field of text classification and has good application potential and practical value. Future work will focus on studying the performance of this model in multi-category imbalanced datasets and cross-domain tasks and explore the integration wi

Memes have emerged as a powerful form of communication, integrating visual and textual elements to convey humor, satire, and cultural messages. Existing research has focused primarily on aspects such as emotion classification, meme generation, propagation, interpretation, figurative language, and sociolinguistics, but has often overlooked deeper meme comprehension and meme-text retrieval. To address these gaps, this study introduces ClassicMemes-50-templates (CM50), a large-scale dataset consisting of over 33,000 memes, centered around 50 popular meme templates. We also present an automated knowledge-grounded annotation pipeline leveraging large vision-language models to produce high-quality image captions, meme captions, and literary device labels overcoming the labor intensive demands of manual annotation. Additionally, we propose a meme-text retrieval CLIP model (mtrCLIP) that utilizes cross-modal embedding to enhance meme analysis, significantly improving retrieval performance. Our contributions include:(1) a novel dataset for large-scale meme study, (2) a scalable meme annotation framework, and (3) a fine-tuned CLIP for meme-text retrieval, all aimed at advancing the understanding and analysis of memes at scale.