Kurt "CyberGuy" Knutsson talks about the best transmitter to use for your TV for Bluetooth earbuds or headphones. Have you ever wished self-checkout was easier than the glitchy scanning of barcodes? A new checkout process using old technology is rolling out to happy shoppers. RFID stands for "radio frequency identification," a technology that uses radio waves to identify and track objects. RFID tags are small electronic devices that can be attached to products, and RFID readers are devices that can scan the tags and communicate with them.

But who would take on the leading roles if Hollywood cast a new movie based on The Nativity? To answer this burning question, MailOnline turned to ChatGPT. While the AI bot claims that producers would have to use CGI for Baby Jesus, it suggests a host of famous faces to take on the other roles. So, do you agree with its star-studded suggestions? Who would take on the leading roles if Hollywood cast a new movie based on The Nativity? The first Nativity Scene was created back in 1223, and has been performed around the world every Christmas since.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' If you received medical care any time this year, there's a good chance you had a close encounter with artificial intelligence. Widely regarded as the breakout year for AI, 2023 ushered in a whole crop of new and improved tech tools, many of which have impacted the health and wellness space. "2023 has been a landmark year for AI in health care, witnessing groundbreaking advancements that have reshaped medical practices and paved the way for a future where health care is more personalized, efficient and accessible," Dr. Harvey Castro, a Dallas, Texas-based board-certified emergency medicine physician and national speaker on AI in health care, told Fox News Digital. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on "Special Report." The rise of artificial intelligence has helped proliferate the spread of fake news, with the internet seeing a surge in websites devoted to disseminating misinformation. "Artificial intelligence tools to propagate fake news are going to snowball out of control quickly," Ziven Havens, the policy director at the Bull Moose Project, a nonprofit "dedicated to building the conservative populist movement," told Fox News Digital. The comments come as AI-created false articles have increased across the internet by 1,000% since May, going from 49 sites to more than 600 in that time span, according to a report from the Washington Post. The report notes that AI has made it easier than ever to disseminate fake news, an operation that used to depend on large groups of low-wage workers to pump out articles that can be hard to differentiate from legitimate news sources.

Republican activist Ying Tan joined'Fox & Friends First' to discuss her reaction and why she believes the Democrat is taking advantage of artificial intelligence. New York City Democratic Councilwoman-elect Susan Zhuang used modern technology to her advantage by answering a news outlet's questionnaire with the help of artificial intelligence, and it left her former opponent outraged. "If she can't answer a single question on her own, how can she represent a district?" Republican activist Ying Tan, who ran against Zhuang, said Tuesday on "Fox & Friends First." "And as a first-generation immigrant myself, I don't feel shy to speak with the accent I have, and during the campaign I insisted on going out on the street, to reach out to the voters…" she continued. Microsoft Bing Chat and ChatGPT AI chat applications are seen on a mobile device in this photo illustration in Warsaw, Poland, on July 21, 2023.

We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. Unlike existing benchmarks, MMMU focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of 14 open-source LMMs as well as the proprietary GPT-4V(ision) and Gemini highlights the substantial challenges posed by MMMU. Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence.

Navigating toy drones through uncharted GPS-denied indoor spaces poses significant difficulties due to their reliance on GPS for location determination. In such circumstances, the necessity for achieving proper navigation is a primary concern. In response to this formidable challenge, we introduce a real-time autonomous indoor exploration system tailored for drones equipped with a monocular \emph{RGB} camera. Our system utilizes \emph{ORB-SLAM3}, a state-of-the-art vision feature-based SLAM, to handle both the localization of toy drones and the mapping of unmapped indoor terrains. Aside from the practicability of \emph{ORB-SLAM3}, the generated maps are represented as sparse point clouds, making them prone to the presence of outlier data. To address this challenge, we propose an outlier removal algorithm with provable guarantees. Furthermore, our system incorporates a novel exit detection algorithm, ensuring continuous exploration by the toy drone throughout the unfamiliar indoor environment. We also transform the sparse point to ensure proper path planning using existing path planners. To validate the efficacy and efficiency of our proposed system, we conducted offline and real-time experiments on the autonomous exploration of indoor spaces. The results from these endeavors demonstrate the effectiveness of our methods.

Radio Frequency Reinforcement Learning (RFRL) is anticipated to be a widely applicable technology in the next generation of wireless communication systems, particularly 6G and next-gen military communications. Given this, our research is focused on developing a tool to promote the development of RFRL techniques that leverage spectrum sensing. In particular, the tool was designed to address two cognitive radio applications, specifically dynamic spectrum access and jamming. In order to train and test reinforcement learning (RL) algorithms for these applications, a simulation environment is necessary to simulate the conditions that an agent will encounter within the Radio Frequency (RF) spectrum. In this paper, such an environment has been developed, herein referred to as the RFRL Gym. Through the RFRL Gym, users can design their own scenarios to model what an RL agent may encounter within the RF spectrum as well as experiment with different spectrum sensing techniques. Additionally, the RFRL Gym is a subclass of OpenAI gym, enabling the use of third-party ML/RL Libraries. We plan to open-source this codebase to enable other researchers to utilize the RFRL Gym to test their own scenarios and RL algorithms, ultimately leading to the advancement of RL research in the wireless communications domain. This paper describes in further detail the components of the Gym, results from example scenarios, and plans for future additions. Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym

Recommender systems utilizing explicit feedback have witnessed significant advancements and widespread applications over the past years. However, generating recommendations in few-shot scenarios remains a persistent challenge. Recently, large language models (LLMs) have emerged as a promising solution for addressing natural language processing (NLP) tasks, thereby offering novel insights into tackling the few-shot scenarios encountered by explicit feedback-based recommender systems. To bridge recommender systems and LLMs, we devise a prompting template that generates user and item representations based on explicit feedback. Subsequently, we integrate these LLM-processed representations into various recommendation models to evaluate their significance across diverse recommendation tasks. Our ablation experiments and case study analysis collectively demonstrate the effectiveness of LLMs in processing explicit feedback, highlighting that LLMs equipped with generative and logical reasoning capabilities can effectively serve as a component of recommender systems to enhance their performance in few-shot scenarios. Furthermore, the broad adaptability of LLMs augments the generalization potential of recommender models, despite certain inherent constraints. We anticipate that our study can inspire researchers to delve deeper into the multifaceted dimensions of LLMs's involvement in recommender systems and contribute to the advancement of the explicit feedback-based recommender systems field.

This article presents a comparative analysis of the ability of two large language model (LLM)-based chatbots, ChatGPT and Bing Chat, recently rebranded to Microsoft Copilot, to detect veracity of political information. We use AI auditing methodology to investigate how chatbots evaluate true, false, and borderline statements on five topics: COVID-19, Russian aggression against Ukraine, the Holocaust, climate change, and LGBTQ+ related debates. We compare how the chatbots perform in high- and low-resource languages by using prompts in English, Russian, and Ukrainian. Furthermore, we explore the ability of chatbots to evaluate statements according to political communication concepts of disinformation, misinformation, and conspiracy theory, using definition-oriented prompts. We also systematically test how such evaluations are influenced by source bias which we model by attributing specific claims to various political and social actors. The results show high performance of ChatGPT for the baseline veracity evaluation task, with 72 percent of the cases evaluated correctly on average across languages without pre-training. Bing Chat performed worse with a 67 percent accuracy. We observe significant disparities in how chatbots evaluate prompts in high- and low-resource languages and how they adapt their evaluations to political communication concepts with ChatGPT providing more nuanced outputs than Bing Chat. Finally, we find that for some veracity detection-related tasks, the performance of chatbots varied depending on the topic of the statement or the source to which it is attributed. These findings highlight the potential of LLM-based chatbots in tackling different forms of false information in online environments, but also points to the substantial variation in terms of how such potential is realized due to specific factors, such as language of the prompt or the topic.