Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling. This approach understands each annotator's view as valid, which can be highly suitable for tasks that embed subjectivity, e.g., sentiment analysis. However, this construction may be inappropriate for tasks such as hate speech detection, as it affords equal validity to all positions on e.g., sexism or racism. We argue that the conflation of hate and offence can invalidate findings on hate speech, and call for future work to be situated in theory, disentangling hate from its orthogonal concept, offence.

Conversational information-seeking (CIS) is an emerging paradigm for knowledge acquisition and exploratory search. Traditional web search interfaces enable easy exploration of entities, but this is limited in conversational settings due to the limited-bandwidth interface. This paper explore ways to rewrite answers in CIS, so that users can understand them without having to resort to external services or sources. Specifically, we focus on salient entities -- entities that are central to understanding the answer. As our first contribution, we create a dataset of conversations annotated with entities for saliency. Our analysis of the collected data reveals that the majority of answers contain salient entities. As our second contribution, we propose two answer rewriting strategies aimed at improving the overall user experience in CIS. One approach expands answers with inline definitions of salient entities, making the answer self-contained. The other approach complements answers with follow-up questions, offering users the possibility to learn more about specific entities. Results of a crowdsourcing-based study indicate that rewritten answers are clearly preferred over the original ones. We also find that inline definitions tend to be favored over follow-up questions, but this choice is highly subjective, thereby providing a promising future direction for personalization.

This book aims to provide a working introduction to the emerging fusion of modern statistical inference - aka machine learning (ML) or artificial intelligence (AI) - and causal inference methods. The book is aimed at upper level undergraduates and master's-level students as well as doctoral students focusing on applied empirical research. A sufficient background for the core material is one semester of introductory econometrics and one semester of machine learning. We hope the book is also useful to empirical researchers looking to apply modern methods in their work. The book provides an overview of key ideas in both predictive inference and causal inference and shows how predictive tools are key ingredients to answering many causal questions.

Since Robert F. Kennedy Jr. first announced his longshot presidential bid, his campaign has leaned into a variety of unorthodox digital strategies. He's appeared on countless podcasts and has collabed with popular influencers to reach voters online. More recently, the Kennedy campaign has experimented with an AI chatbot that used an apparent loophole to get around OpenAI's restrictions on political use. On Sunday, after inquiries from WIRED, the chatbot disappeared. The loophole in question is an apparent result of the tight relationship between Microsoft and OpenAI.

The death toll from a Russian drone strike that destroyed an apartment block in Ukraine's southern port city of Odesa on Saturday has risen to 10. Ukraine's interior ministry reported that rescue workers on Sunday morning retrieved the remains of an infant and the baby's mother, raising the number of children killed in the attack to three. "The mother tried to cover the 8-month-old child with her own [body]. She tried to save them. They were found in a firm embrace," the ministry said in a Telegram post. On Saturday, Ukrainian authorities reported that a baby was among those killed after falling debris from an Iranian-made Shahed drone hit the apartment building – one of eight Russian-launched drones reported by officials.

TV personality Dr. Phil McGraw suggested on Friday that President Biden should take a cognitive exam because, "people that have nothing to hide, hide nothing." Phil, do you think President Biden should take a cognitive exam?" Maher asked Dr. Phil during the "Overtime" portion of his "Real Time" show on Friday. "People that have nothing to hide, hide nothing. So, why not?" Phil responded. Maher closed his show on Friday by encouraging Biden to "lean into" his age and said, "Don't try to deny the age thing, lean into it.

More than a year has passed since the public first gained access to OpenAI's ChatGPT, giving various industries the chance to experiment with artificial intelligence (AI) and a sense of the transformational potential -- or lack of it. "Any industry that has large amounts of data that needs to be indexed and processed quickly is ripe for AI integration," Reema Khan, founder and CEO at Green Sands Equity, told Fox News Digital. "In technology for example, writing thousands of lines of code is much easier to do with an AI co-pilot and can do ten times a software engineer's productivity." The U.S. economy added over 353,000 jobs in January 2024, but the tech sector continues to suffer significant layoffs. Google laid off several hundred employees from its sales and advertising teams in the same month as part of multiple rounds of cost-cutting measures.

Fox News contributor Mike Pompeo weighs in on Hungary's parliament approving Sweden's bid to join NATO and a resurfaced clip of Russian President Vladimir Putin's warning about NATO expansion on'The Story.' A drone crashed into an apartment building in St. Petersburg Saturday morning, according to Russian state news agency RIA Novosti. The local state news agency said that Ukrainian forces had damaged the apartment building. Two buildings were damaged in St. Petersburg's Krasnogvardeisky district following the alleged attack. Photos from the dilapidated-looking apartment complex showed large craters on the building's exterior.

House Judiciary Committee Chairman Jim Jordan wrote a letter to Alphabet, Google's parent company, on Saturday, demanding the company explain what influence the Biden administration may have had on its controversial Gemini AI program. The Judiciary Committee asked for documents on the creation and deployment of the artificial intelligence chatbot. "The Committee is investigating how and to what extent the Executive Branch has coerced or colluded with Big Tech and other intermediaries to censor Americans' speech," the House Judiciary Committee said in a Saturday news release. Gemini has faced backlash after it reportedly showed historical figures like George Washington appearing wrongfully as Black and a search for a "pope" prompting a Black woman in Vatican garb. White Supremacist Nazis also were not White.

Aviation safety is paramount in the modern world, with a continuous commitment to reducing accidents and improving safety standards. Central to this endeavor is the analysis of aviation accident reports, rich textual resources that hold insights into the causes and contributing factors behind aviation mishaps. This paper compares two prominent topic modeling techniques, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), in the context of aviation accident report analysis. The study leverages the National Transportation Safety Board (NTSB) Dataset with the primary objective of automating and streamlining the process of identifying latent themes and patterns within accident reports. The Coherence Value (C_v) metric was used to evaluate the quality of generated topics. LDA demonstrates higher topic coherence, indicating stronger semantic relevance among words within topics. At the same time, NMF excelled in producing distinct and granular topics, enabling a more focused analysis of specific aspects of aviation accidents.