Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Jonathan Owens struck gold (pun intended) when he married one of the best gymnasts of all-time. The Green Bay Packers safety wifed up four-time Olympic gold medal winner Simone Biles earlier this year, but when they met, Owens had no idea of Biles' celebrity status. The irony of it all is the fact that the two had met on a celebrity dating app, Raya.

In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning, the quest for adaptive, user-centric recommendations has led to the emergence of the formulation of contextual bandits. This tutorial investigates the contextual bandits as a powerful framework for personalized recommendations. We delve into the challenges, advanced algorithms and theories, collaborative strategies, and open challenges and future prospects within this field. Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i.e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.

The man charged with administering Arizona's elections isn't concerned about the state's ability to securely hold elections. But he's going to have to persuade millions of other people to feel the same way. Adrian Fontes, a Democrat, was elected Arizona's secretary of state in 2022. A lawyer who previously worked as a prosecutor in Colorado and Arizona, and served as the Maricopa County Recorder before taking office, Fontes must now take on the role of convincing the state's voters that its elections are legitimate. Arizona is possibly the market leader in ridiculous election conspiracies and deniers.

Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the hallucination of large language models (LLMs). However, existing research lacks rigorous evaluation of the impact of retrieval-augmented generation on different large language models, which make it challenging to identify the potential bottlenecks in the capabilities of RAG for different LLMs. In this paper, we systematically investigate the impact of Retrieval-Augmented Generation on large language models. We analyze the performance of different large language models in 4 fundamental abilities required for RAG, including noise robustness, negative rejection, information integration, and counterfactual robustness. To this end, we establish Retrieval-Augmented Generation Benchmark (RGB), a new corpus for RAG evaluation in both English and Chinese. RGB divides the instances within the benchmark into 4 separate testbeds based on the aforementioned fundamental abilities required to resolve the case. Then we evaluate 6 representative LLMs on RGB to diagnose the challenges of current LLMs when applying RAG. Evaluation reveals that while LLMs exhibit a certain degree of noise robustness, they still struggle significantly in terms of negative rejection, information integration, and dealing with false information. The aforementioned assessment outcomes indicate that there is still a considerable journey ahead to effectively apply RAG to LLMs.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Vice President Kamala Harris is emphasizing abortion as a key issue in the run-up to the 2024 election, preparing numerous rallies around the administration's pro-choice message. Harris rolled out the Fight for Our Reproductive Freedoms Tour this week as President Biden's team ramps up efforts for the upcoming election year. "I will continue to fight for our fundamental freedoms while bringing together those throughout America who agree that every woman should have the right to make decisions about her own body -- not the government," Harris said in a statement.

As a precocious teen looking to improve my college application, I sat in on a business studies class. I figured taking two extra A-Levels at night school alongside those I took during the day would make me irresistible to admissions tutors. The class I watched examined if it was worth a large factory keeping its own trucks and drivers in-house rather than outsourcing them. The data showed selling the trucks and firing the workers was more expensive in the long run, and yoked the company to the whims of any third-party logistics company in the local area. Not to mention, if you don't own a mission-critical component of your business, you're a lot less powerful when negotiating with your suppliers.

When Spike Jonze's Her came out in 2013, the film about a lonely man falling for an artificially intelligent operating system won widespread praise. Watching today, the qualities critics celebrated at the time are still there--it's a gentle, enjoyably melancholy story, twee but not damnably so--but something else stands out. Though set in the near-future, Her captures Obama-era techno-optimism better than any other movie. It's a time capsule, preserving dreams about the future that appear more naive the further we get from the 2010s. Her takes place in a highly-stylized version of Los Angeles from a future near enough that its protagonist is a former LA Weekly journalist but distant enough that the skyline rivals Shanghai.

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

Scientific research organizations that are developing and deploying Artificial Intelligence (AI) systems are at the intersection of technological progress and ethical considerations. The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development, championing core values like fairness, accountability, and transparency. For scientific research organizations, prioritizing these practices is paramount not just for mitigating biases and ensuring inclusivity, but also for fostering trust in AI systems among both users and broader stakeholders. In this paper, we explore the practices at a research organization concerning RAI practices, aiming to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development. We have adopted a mixed-method research approach, utilising a comprehensive survey combined with follow-up in-depth interviews with selected participants from AI-related projects. Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks. This revealed an overarching underestimation of the ethical risks that AI technologies can present, especially when implemented without proper guidelines and governance. Our findings reveal the need for a holistic and multi-tiered strategy to uplift capabilities and better support science research teams for responsible, ethical, and inclusive AI development and deployment.