Last week, A.I.–generated nude images of pop superstar Taylor Swift were produced and distributed without her consent. They circulated throughout the internet, with one single post on X (née Twitter) garnering 45 million views before the site took it down. Deepfakes, as they've come to be called in recent years, often target female celebrities, but with the rise of A.I., it's easier than ever for everyday people (almost always women) to be targeted. Last year, more than 143,000 deepfake porn videos were created, according to one estimate from the independent researcher Genevieve Oh, more than every other previous year combined. That number will, in all likelihood, only continue to rise.

A billion streams puts you in the same league as musicians including Drake, Taylor Swift and Harry Styles. But one producer has hit this milestone by focusing on four-legged, furry listeners instead of human beings. Speaking from Los Angeles, Amman Ahmed tells DailyMail.com he pioneered the idea of music for pets via a YouTube channel, playing songs and listening to dog owner feedback until he created music which genuinely relaxed the animals. He tapped into a post-pandemic trend when separation anxiety among pets got worse when animals got used to spending so much time with their working-from-home owners. The pioneering dog musician now offers dozens of playlists to relax cats and dogs, and says that his'creative process' is driven by his four-legged listeners themselves.

Social media platform X has blocked searches for one of the world's most popular personalties, Taylor Swift, after explicit artificial intelligence images of the singer-songwriter went viral. The deepfakes flooded several social media sites from Reddit to Facebook. This has renewed calls to strengthen legislation around AI, particularly when it is misused for sexual harassment. Here's what you need to know about the Swift episode and legality around deepfakes. On Wednesday, AI-generated, sexually explicit images began circulating on social media sites, particularly gaining traction on X.

As generative AI (and access to AI tools) continues to grow, expect to see more things like the tumult over "George Carlin: I'm Glad I'm Dead." Released on (then pulled from) YouTube, it's framed as an hour of new "material" by the comedian, who died in 2008. It isn't based on old notes or lost routines, either, like recent releases from the Beatles, and George Carlin's estate has filed a lawsuit against the makers. Initial reports from NPR said the AI was trained on thousands of hours of Carlin routines to create the material. Dudesy, the channel that created and posted the video, was later approached by The New York Times, and their spokesperson said the video was "completely written by Chad Kultgen" -- one of the channel's hosts.

Sir Lucian Grainge, the chairman and C.E.O. of Universal Music Group, the largest music company in the world, is curious, empathetic, and, if not exactly humble, a master of the humblebrag. His superpower is his humanity. A sixty-three-year-old Englishman, who was knighted in 2016 for his contributions to the music industry and has topped Billboard's Power 100 list of music-industry players several times in the past decade, Grainge is compact and a bit chubby, with alert eyes behind owlish glasses. He isn't trying to be noticed. He presides over a public company worth more than fifty billion dollars, but he could be a small-business owner who sells music in a London shop, as did his father, Cecil.

Research into large language models shows that they repeat conspiracy theories, harmful stereotypes, and other forms of misinformation. In a recent study, researchers at the University of Waterloo systematically tested an early version of ChatGPT's understanding of statements in six categories: facts, conspiracies, controversies, misconceptions, stereotypes, and fiction. This was part of Waterloo researchers' efforts to investigate human-technology interactions and explore how to mitigate risks. They discovered that GPT-3 frequently made mistakes, contradicted itself within the course of a single answer, and repeated harmful misinformation. Though the study commenced shortly before ChatGPT was released, the researchers emphasize the continuing relevance of this research.

Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data generation. We created a dataset specifically designed for OOCD and developed an efficient detector for accurate classification. Our experimental findings validate the use of synthetic data generation and demonstrate its efficacy in addressing the data limitations associated with OOCD. The dataset and detector should serve as valuable resources for future research and the development of robust misinformation detection systems.

In this paper, we present a variety of classification experiments related to the task of fictional discourse detection. We utilize a diverse array of datasets, including contemporary professionally published fiction, historical fiction from the Hathi Trust, fanfiction, stories from Reddit, folk tales, GPT-generated stories, and anglophone world literature. Additionally, we introduce a new feature set of word "supersenses" that facilitate the goal of semantic generalization. The detection of fictional discourse can help enrich our knowledge of large cultural heritage archives and assist with the process of understanding the distinctive qualities of fictional storytelling more broadly.

Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehensive understanding of the conversational inputs to formulate a good search query based on historical information. In particular, the search query should include the relevant information from the previous conversation turns. However, current approaches for conversational dense retrieval primarily rely on fine-tuning a pre-trained ad-hoc retriever using the whole conversational search session, which can be lengthy and noisy. Moreover, existing approaches are limited by the amount of manual supervision signals in the existing datasets. To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns. Experiments on two public conversational search datasets demonstrate the improved history modeling capability of HAConvDR, in particular for long conversations with topic shifts.

The number of Hindi speakers on social media has increased dramatically in recent years. Regret is a common emotional experience in our everyday life. Many speakers on social media, share their regretful experiences and opinions regularly. It might cause a re-evaluation of one's choices and a desire to make a different option if given the chance. As a result, knowing the source of regret is critical for investigating its impact on behavior and decision-making. This study focuses on regret and how it is expressed, specifically in Hindi, on various social media platforms. In our study, we present a novel dataset from three different sources, where each sentence has been manually classified into one of three classes "Regret by action", "Regret by inaction", and "No regret". Next, we use this dataset to investigate the linguistic expressions of regret in Hindi text and also identify the textual domains that are most frequently associated with regret. Our findings indicate that individuals on social media platforms frequently express regret for both past inactions and actions, particularly within the domain of interpersonal relationships. We use a pre-trained BERT model to generate word embeddings for the Hindi dataset and also compare deep learning models with conventional machine learning models in order to demonstrate accuracy. Our results show that BERT embedding with CNN consistently surpassed other models. This described the effectiveness of BERT for conveying the context and meaning of words in the regret domain.