Many online platforms of today, including social media sites, are two-sided markets bridging content creators and users. Most of the existing literature on platform recommendation algorithms largely focuses on user preferences and decisions, and does not simultaneously address creator incentives. We propose a model of content recommendation that explicitly focuses on the dynamics of user-content matching, with the novel property that both users and creators may leave the platform permanently if they do not experience sufficient engagement. In our model, each player decides to participate at each time step based on utilities derived from the current match: users based on alignment of the recommended content with their preferences, and creators based on their audience size. We show that a user-centric greedy algorithm that does not consider creator departures can result in arbitrarily poor total engagement, relative to an algorithm that maximizes total engagement while accounting for two-sided departures. Moreover, in stark contrast to the case where only users or only creators leave the platform, we prove that with two-sided departures, approximating maximum total engagement within any constant factor is NP-hard. We present two practical algorithms, one with performance guarantees under mild assumptions on user preferences, and another that tends to outperform algorithms that ignore two-sided departures in practice.

Correction, January 18, 2024, 4:55 PM ET: This story originally claimed that AI-generated content was being promoted in Google News. We did not note that to find such stories required heavily manipulating the search results in Google News, so much so that it didn't surface an original, more legitimate source. As 404 Media itself writes, "Both of these rip-off articles appear in Google News search results. The first appears when searching for "Star Wars theory" and setting the results to the past 24 hours. The second appears when searching for the subject of the article with a similar 24 hour setting."

Stability AI, the makers of the popular AI image generation model Stable Diffusion, had trained the model by feeding it with millions of images that had been "scraped" from the internet, without the consent of their creators. Newton-Rex, the head of Stability's audio team, disagreed. "Companies worth billions of dollars are, without permission, training generative AI models on creators' works, which are then being used to create new content that in many cases can compete with the original works. In December, the New York Times sued OpenAI in a Manhattan court, alleging that the creator of ChatGPT had illegally used millions of the newspaper's articles to train AI systems that are intended to compete with the Times as a reliable source of information. Meanwhile, in July 2023, comedian Sarah Silverman and other writers sued OpenAI and Meta, accusing the companies of using their writing to train AI models without their permission.

The European Parliament is calling for new regulations to ensure streaming services pay artists fairly. The proposal also calls for more transparency around how algorithms generate suggestions for which artists to stream and what tracks get the most promotion. The proposed changes will be designed to ensure smaller artists are compensated fairly. Currently, royalty rates are set in a way that makes artists accept lower pay for the distribution of their content in exchange for visibility on streaming platforms like Spotify and Apple Music. The members of the European Parliament (MEPs) are primarily concerned with introducing new legal frameworks to help support artists.

"Elvis Evolution" combines the use of live actors, theatrical sets, artificial intelligence and holograms to bring Elvis back to the stage. If you are a fan of Elvis Presley, you are in for a treat. A new immersive show called "Elvis Evolution" is coming soon, and it will let you see, hear and feel the legend like never before. The special tribute is pulled off all thanks to some amazing technology. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER "Elvis Evolution" uses a mix of live actors, intricate theatrical sets and cutting-edge technology, such as AI, holograms, virtual/mixed reality and projection mapping, to bring a life-size Elvis back to life onstage.

Robert Patrick, best known to many audiences as the unstoppable T-1000 in "Terminator 2: Judgment Day," wasn't always set on becoming an actor. In a new interview with the Wall Street Journal, he revealed that though he was interested in acting from a young age, he didn't pursue it because "that wasn't done in my family." Instead, while living in the Cleveland area, he worked in a bank and dabbled in modeling before a life-changing boat accident inspired him to pursue his dream. Patrick said, "In 1984, I was piloting a 30-foot boat that belonged to a buddy when it capsized and sank. Five of us were in the water, including my brother, Lewis. The boat hadn't been properly prepped."

Last year saw new artificial intelligence products released at the most rapid pace yet, though predictions of an AI boom on the scale of last decade's tech explosion have yet to come to fruition. "I think 2023 was the year that AI astonished people and 2024 will be the year of retrenchment as people learn the limitations of AI and where various AI systems have the greatest utility," Christopher Alexander, chief analytics officer for Pioneer Development Group, told Fox News Digital. "I think that the race for AI utility has just begun and AI will become a permanent fixture in people's lives. I think that the grand predictions for AI in this past year confused the current state of AI and the future state, which has led to some confusion in the market." Alexander's comments come after what was in many ways a landmark year for AI technology in 2023, with new platforms and developments making headlines throughout the year.

He adds that the software also has to factor in the needs of the increasing number of electric buses. "The widespread and accelerating adoption of electric buses requires regular, coordinated and precisely timed charging schedules to be integrated into daily operations. The movement of vehicles in and around the depot needs to be precisely managed to support this."

Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. A distinctive element of FactCHD is its integration of fact-based evidence chains, significantly enhancing the depth of evaluating the detectors' explanations. Experiments on different LLMs expose the shortcomings of current approaches in detecting factual errors accurately. Furthermore, we introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence. The benchmark dataset is available at https://github.com/zjunlp/FactCHD.

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.