The creator of a viral video purporting to show Japanese Prime Minister Fumio Kishida making explicit sexual admissions in a live news broadcast said he made it in about an hour as a "little joke." "I didn't think it would create such a stir," the man in his 20s said of the video, explaining that he used generative artificial intelligence technology to create Kishida's voice and mouth movements. The video shows the prime minister speaking to the camera during a live news program on Japanese broadcaster Nippon Television Network. The company's logo appears in the top right corner of the screen along with a ticker saying, "Breaking News."

Intentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low resource language like Bangla has not been given adequate attention. To tackle clickbait titles in Bangla, we have constructed the first Bangla clickbait detection dataset containing 15,056 labeled news articles and 65,406 unlabelled news articles extracted from clickbait dense news sites. Each article has been labeled by three expert linguists and includes an article's title, body, and other metadata. By incorporating labeled and unlabelled data, we finetune a pretrained Bangla transformer model in an adversarial fashion using Semi Supervised Generative Adversarial Networks (SS GANs). The proposed model acts as a good baseline for this dataset, outperforming traditional neural network models (LSTM, GRU, CNN) and linguistic feature based models. We expect that this dataset and the detailed analysis and comparison of these clickbait detection models will provide a fundamental basis for future research into detecting clickbait titles in Bengali articles. We have released the corresponding code and dataset.

This study compares the performance of (1) fine-tuned models and (2) extremely large language models on the task of check-worthy claim detection. For the purpose of the comparison we composed a multilingual and multi-topical dataset comprising texts of various sources and styles. Building on this, we performed a benchmark analysis to determine the most general multilingual and multi-topical claim detector. We chose three state-of-the-art models in the check-worthy claim detection task and fine-tuned them. Furthermore, we selected three state-of-the-art extremely large language models without any fine-tuning. We made modifications to the models to adapt them for multilingual settings and through extensive experimentation and evaluation. We assessed the performance of all the models in terms of accuracy, recall, and F1-score in in-domain and cross-domain scenarios. Our results demonstrate that despite the technological progress in the area of natural language processing, the models fine-tuned for the task of check-worthy claim detection still outperform the zero-shot approaches in a cross-domain settings.

Recommendation systems (RSs) are the principal ingredient of many online services and platforms like Youtube, Quora, Substack, and Medium. Algorithmicall y, those platforms treat the task of recommendation as a matching problem. RSs match a user's con text, i.e., their past interactions, demographics, etc., to an item from a predetermined list of i tems, e.g., news articles, which will hopefully satisfy that user. The quality of a user-content m atch is initially unclear, so many data-driven approaches have been proposed to determine a matchin g's quality; for instance, collaborate filtering [ 23 ], matrix completion [ 37 ], and online learning [ 7 ]. However, due to their rapid adoption in commercial applications, many RSs are now dynamic economic systems with multiple stakeholders, facing challenges beyond dissolving uncertainty in matchi ng. Fairness [ 6, 15, 18, 35 ], misinformation [ 17 ], user incentives [ 3, 24 ], and privacy [ 21 ] are only some of the challenges RSs face. A recent body of research addresses tradeoffs among stakehol ders [ 9, 10, 28 ]. Online platforms have three main stakeholders: The commercial company that r uns the platform, content consumers, and content providers. Content consumers, which we refer to as users for simplicity, enjoy the RSs' content.

The announcement comes a day after Microsoft announced it was also taking a number of steps to protect elections, including offering tools to watermark AI-generated content and deploying a "Campaign Success Team" to advise political campaigns on AI, cybersecurity, and other related issues. Next year will be the most significant year for elections so far this century, with the U.S., India, the U.K., Mexico, Indonesia, and Taiwan all headed to the polls. Although many are concerned about the impact deepfakes and misinformation could have on elections, many experts stress the evidence for their impacts on elections so far is limited at best. Experts welcome the measures taken by tech companies to defend election integrity but say more fundamental changes to political systems will be required to tackle misinformation. Tech companies have come under scrutiny after the role they played in previous elections.

Fox News Flash top entertainment and celebrity headlines are here. The vastness of the universe has always captivated the human imagination, and filmmakers have often looked to the stars for inspiration. Space-related movies have become a genre of their own, offering audiences an opportunity to explore the unknown, experience the thrill of interstellar travel and ponder the profound questions of our existence. These are some of the most iconic and thought-provoking space-theme films that have left a lasting impact on both the science fiction and Hollywood. 'GRAVITY' REVIEW: THERE HAS NEVER BEFORE BEEN MOVIE LIKE THIS From "2001: A Space Odyssey" to "Interstellar" and space survival tales like "Gravity" and "The Martian," Fox News Digital dives into the cinematic cosmos, celebrating their enduring impact on our love for science fiction.

One of the longest labor crises in Hollywood history is finally coming to an end. SAG-AFTRA, the union representing tens of thousands of actors, reached a tentative deal for a new contract with entertainment companies on Wednesday, clearing the way for the $134 billion American movie and television business to swing back into motion. Hollywood's assembly lines have been at a near-standstill since May because of a pair of strikes by writers and actors, resulting in financial pain for studios and for many of the two million Americans -- makeup artists, set builders, location scouts, chauffeurs, casting directors -- who work in jobs directly or indirectly related to making TV shows and films. Upset about streaming-service pay and fearful of fast-developing artificial intelligence technology, actors joined screenwriters on picket lines in July. The writers had walked out in May over similar concerns.

The Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) has officially ended its strike, which lasted for 118 days, after reaching a tentative agreement with Hollywood studios. In its announcement, it said it was able to secure a contract "valued at over 1 billion dollars" and that it was able to negotiate "above-pattern" compensation increases, as well as "unprecedented provisions for consent and compensation that will protect members from the threat of AI." In a contract valued at over one billion dollars, we have achieved a deal of extraordinary scope that includes "above-pattern" minimum compensation increases, unprecedented provisions for consent and compensation that will protect members from the threat of AI,... pic.twitter.com/lQe6snkQsY The union will release more details about the agreement after its national board looks it over on Friday for "review and consideration." However, generative AI became the sticking point that prevented both parties from being able to strike a deal earlier than this.

After 118 days on the picket lines, the longest such strike in Hollywood's history, the Screen Actors Guild-American Federation of Television and Radio Artists has reached a deal with the Alliance of Motion Picture and Television Producers. Both sides were mum about the terms of the deal Wednesday night, but it comes following a long struggle over the use of artificial intelligence on actors' performances and actors' demands for residual payments for shows and films that play on streaming services. A committee from SAG, which represents thousands of film and television actors, approved the agreement Wednesday. The strike itself, which has featured pickets outside the offices of Netflix, Disney, Warner Bros. Discovery, and others, will end Thursday morning. It's expected that the tentative deal will head to the union's national board to be approved on Friday.

Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are unable to predict how other human raters will respond, based on their political leanings. For (1), we conduct a noise audit at an unprecedented scale that combines both machine and human responses. For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our noise audit reveals that moderation outcomes vary wildly across different machine moderators. Our experiments with human moderators suggest that political leanings combined with sensitive issues affect both first-person and vicarious offense. The dataset is available through https://github.com/Homan-Lab/voiced.