Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.

Artificial intelligence-powered influencers are the new social media trend. But there could be negative effects from the perfect influencers, a humane technologist warns. If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Jury selection begins Thursday for the alleged medical abuse case that broke a Florida family apart and inspired the Netflix documentary "Take Care of Maya." In 2016, at just 10 years old, Maya Kowalski -- the girl at the center of the Netflix documentary -- was admitted to Johns Hopkins All Children's Hospital (JHAC) in St. Petersburg, Florida, for severe pain and then promptly removed from the custody of her parents after staff accused them of "medical abuse."

In the film "2001: A Space Odyssey," an astronaut travels through a seeming tunnel of light. Earlier this summer, Artechouse, an organization producing immersive, technology-based art, started offering a science-backed version of a similar trip at its New York venue. The show, titled "Beyond the Light," is a looping twenty-six-minute journey through space and other realms inspired by images from the James Webb Space Telescope (J.W.S.T.). Artechouse began talks with NASA about a show in 2018, and started pulling this one together earlier this year, after the first images captured by J.W.S.T. were released to the public last July. More than sixteen thousand years ago, cave explorers in what's now Lascaux, France, painted animals that are believed to represent the constellations.

Edward Tian didn't think of himself as a writer. As a computer science major at Princeton, he'd taken a couple of journalism classes, where he learned the basics of reporting, and his sunny affect and tinkerer's curiosity endeared him to his teachers and classmates. But he describes his writing style at the time as "pretty bad"--formulaic and clunky. One of his journalism professors said that Tian was good at "pattern recognition," which was helpful when producing news copy. So Tian was surprised when, sophomore year, he managed to secure a spot in John McPhee's exclusive non-fiction writing seminar.

ChatGPT has proven it can help students with their homework, but now it is helping teachers create those very courses, a computer science professor told Fox News. A scientific journal was forced to retract a paper it published last month after it was discovered the authors used the artificial intelligence application ChatGPT to write it. The paper, published Aug. 9 in the journal Physica Scripta, was an attempt to uncover new solutions to a complicated math equation, but included the phrase "Regenerate response" on the third page -- something one eagle-eyed reader recognized was the phrase of a button on ChatGPT, according to a report from Nature. The authors of the paper have since acknowledged they used ChatGPT to help write the manuscript, something that wasn't caught during two months of peer review after the paper was submitted in May. The revelation led the U.K.-based publisher to retract the paper because the authors did not disclose their use of the AI app when they submitted it.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Have you ever seen the YouTube video of the young boy at Christmas unwrapping a Nintendo 64 and completely freaking out with excitement? And that kid was me! My peak experiences as a kid always coincided with groundbreaking technology launches.

When wildfires swept across Maui, Hawaii, last month with destructive fury, China's increasingly resourceful information warriors pounced. The disaster was not natural, they said in a flurry of false posts that spread across the internet, but was the result of a secret "weather weapon" being tested by the United States. To bolster plausibility, the posts carried photographs that appeared to have been generated by artificial intelligence programs, making them among the first known to have used these new tools to amplify the aura of authenticity of a disinformation campaign.

Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powered tutoring system. In this work, we explore the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs), covering automatic course planning and adjusting, tailored instruction, and flexible quiz evaluation. To make the system robust to prolonged interaction and cater to individualized education, the system is decomposed into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules. Tools are LLMs prompted to execute one specific task at a time, while memories are data storage that gets updated during education process. Statistical results from learning logs demonstrate the effectiveness and mechanism of each tool usage. Subjective feedback from human users reveal the usability of each function, and comparison with ablation systems further testify the benefits of the designed processes in long-term interaction.

The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community recently. While NLP can help to scale up analyses or contribute automatic procedures to investigate the impact of biased news in society, we argue that methodologies that are currently dominant fall short of addressing the complex questions and effects addressed in theoretical media studies. In this survey paper, we review social science approaches and draw a comparison with typical task formulations, methods, and evaluation metrics used in the analysis of media bias in NLP. We discuss open questions and suggest possible directions to close identified gaps between theory and predictive models, and their evaluation. Figure 1: Two articles about the same event written These include model transparency, considering from different political ideologies. Example taken from document-external information, and AllSides.com.

Large Language Models (LLMs) have shown impressive performance across a variety of Artificial Intelligence (AI) and natural language processing tasks, such as content creation, report generation, etc. However, unregulated malign application of these models can create undesirable consequences such as generation of fake news, plagiarism, etc. As a result, accurate detection of AI-generated language can be crucial in responsible usage of LLMs. In this work, we explore 1) whether a certain body of text is AI generated or written by human, and 2) attribution of a specific language model in generating a body of text. Texts in both English and Spanish are considered. The datasets used in this study are provided as part of the Automated Text Identification (AuTexTification) shared task. For each of the research objectives stated above, we propose an ensemble neural model that generates probabilities from different pre-trained LLMs which are used as features to a Traditional Machine Learning (TML) classifier following it. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0.733 and 0.649) for English and Spanish texts, respectively. For the second task on model attribution, our model ranked in first place with macro $F1$ scores of 0.625 and 0.653 for English and Spanish texts, respectively.