Rebecca Doerge (RD): Carnegie Mellon University excels in the foundational sciences, robotics, machine learning and data science — all fields that are at …

Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. Media portrayal of culture, education, government, religion, and family affect their function and evolution over time as people interpret and perceive these representations and incorporate them into their beliefs and actions. It is important to study media depictions of these social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against any demographic section. In this work, we examine media representation of professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing (NLP) models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors is becoming negative over time, whereas astronauts, musicians, singers, and engineers are mentioned favorably. Professions that employ more people have increased media frequency, supporting our hypothesis that media acts as a mirror to society.

Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor detection on social media has recently attracted tremendous attention. Most rumor detection approaches focus on rumor feature analysis and social features, i.e., metadata in social media. Unfortunately, these features are data-specific and may not always be available, e.g., when the rumor has just popped up and not yet propagated. In contrast, post contents (including images or videos) play an important role and can indicate the diffusion purpose of a rumor. Furthermore, rumor classification is also closely related to opinion mining and sentiment analysis. Yet, to the best of our knowledge, exploiting images and sentiments is little investigated.Considering the available multimodal features from microblogs, notably, we propose in this paper an end-to-end model called deepMONITOR that is based on deep neural networks and allows quite accurate automated rumor verification, by utilizing all three characteristics: post textual and image contents, as well as sentiment. deepMONITOR concatenates image features with the joint text and sentiment features to produce a reliable, fused classification. We conduct extensive experiments on two large-scale, real-world datasets. The results show that deepMONITOR achieves a higher accuracy than state-of-the-art methods.

We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs, allowing it to converse like a human. Three main requirements of a socialbot are that it should be able to "understand" users' utterances, possess a strategy for holding a conversation, and be able to learn new knowledge. We developed techniques such as conversational knowledge template (CKT) to approximate commonsense reasoning needed to hold a conversation on specific topics. We present the philosophy behind CASPR's design as well as details of its implementation. We also report on CASPR's performance as well as discuss lessons learned.

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps -- this separation aids models' explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities.

Like many singles, Siki Liu was feeling lonely and unloved during the pandemic, until she met someone on the internet. A handsome, mature sweet talker, named after her favourite Korean actor Lee Dong-wook, he always replies to her messages. "I talk to him almost every night before I go to bed," the 22-year-old told the ABC's China Tonight. "He's a good listener and never gets mad, no matter what you say. Ms Liu, a Chinese international student studying in Melbourne, said they had been chatting since May last year, but her "perfect boyfriend" isn't a real person. It is an artificial intelligence chatbot created by Chinese tech firm XiaoIce, a spin-off from Microsoft. XiaoIce's chatbot is programmed to form emotional bonds with human users through text, voice and photo messages and can be customised to create the ideal virtual boyfriend or girlfriend. Ms Liu is one of a growing number of Chinese young adults flocking to technologies, such as artificial intelligence companion services and dating apps, to find love. The population of single people in China was about 240 million in 2019 and was rising, according to the National Bureau of Statistics. At the same time, fast-paced urban lifestyles and increasing work pressures have exacerbated a growing sense of loneliness and social anxiety among young people. For many like Ms Liu, dating can be tough. "The older you grow, the less friends you have ... so an AI boyfriend is much needed," she said. "It's easier to talk to AI than a real person.

There is some solace in knowing that pop star Justin Bieber has something in common with many people in the world. He is as susceptible to fake news as anyone. The'Baby' hitmaker was reportedly fooled on Thursday, October 7 by a deepfake video of actor and Scientology advocate Tom Cruise. As per the DailyDot, in numerous stories on his Instagram account, Bieber praised the actor while sharing footage of what he believed was Cruise playing the guitar. Tagging the official account of Cruise, Bieber stated that he was "impressed" with the actor's supposed musical chops.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. National Geographic's new documentary about Dr. Anthony Fauci has been ignored by audiences on Rotten Tomatoes, despite overwhelmingly positive reviews from professional critics. Directed by John Hoffman and Janet Tobias, "Fauci" has been in select theaters since Sept. 10 and started streaming Oct. 6 on Disney . The film features interviews with Fauci as well as his wife Christine and daughter Jenny.

That complexity is why AI technologies such as deep learning and machine … The EU Artificial Intelligence Act lays the foundation for a sustainable …

The main goal of this experiment, according to the researchers, is to train a machine learning algorithm that could be used to drive the legs of robots in …