ChatGPT nickname and Trump tariffs nominated for Japan's buzzword of 2025 Women hold toys depicting Myaku-Myaku, the official character for the 2025 Osaka Expo, during a media day ahead of the event's public opening day in Osaka in April. A comment made by Prime Minister Sanae Takaichi immediately after she was elected president of the Liberal Democratic Party last month -- that she would "work, work, work, work and work," which triggered both praise for her determination and criticism amid efforts to improve work-life balance -- also made the list, along with (female prime minister). "There weren't many buzzwords in the first half of the year, but after (U.S.) President Donald Trump returned to office, many phrases went viral regarding tariffs," publisher Jiyukokuminsha, which hosts the award, said in a statement. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Scientists have found that artificial intelligence is reshaping the way people talk. Researchers at the University of Florida found that adults are increasingly weaving ChatGPT-style vocabulary into everyday speech, favoring words like'surpass,' 'boast,' 'meticulous,' 'strategically' and'garner.' The team analyzed 22.1 million words from unscripted and spontaneous spoken language, including conversational podcasts on science and technology. They discovered that nearly three-quarters of AI-associated words have surged in use since ChatGPT's release in 2022, with some more than doubling in frequency. Crucially, the increases were not mirrored in synonymous words, suggesting the change stems directly from AI influence rather than natural linguistic evolution.

The massive user-generated content (UGC) available in Chinese social media is giving rise to the possibility of studying internet buzzwords. In this paper, we study if large language models (LLMs) can generate accurate definitions for these buzzwords based on UGC as examples. Our work serves a threefold contribution. First, we introduce CHEER, the first dataset of Chinese internet buzzwords, each annotated with a definition and relevant UGC. Second, we propose a novel method, called RESS, to effectively steer the comprehending process of LLMs to produce more accurate buzzword definitions, mirroring the skills of human language learning. Third, with CHEER, we benchmark the strengths and weaknesses of various off-the-shelf definition generation methods and our RESS. Our benchmark demonstrates the effectiveness of RESS while revealing crucial shared challenges: over-reliance on prior exposure, underdeveloped inferential abilities, and difficulty identifying high-quality UGC to facilitate comprehension. We believe our work lays the groundwork for future advancements in LLM-based definition generation. Our dataset and code are available at https://github.com/SCUNLP/Buzzword.

The complexities of chats pose significant challenges for machine translation models. Recognizing the need for a precise evaluation metric to address the issues of chat translation, this study introduces Multidimensional Quality Metrics for Chat Translation (MQM-Chat). Through the experiments of five models using MQM-Chat, we observed that all models generated certain fundamental errors, while each of them has different shortcomings, such as omission, overly correcting ambiguous source content, and buzzword issues, resulting in the loss of stylized information. Our findings underscore the effectiveness of MQM-Chat in evaluating chat translation, emphasizing the importance of stylized content and dialogue consistency for future studies.

Fortunes are being made on that interest, in a way hauntingly reminiscent of the crypto boom and subsequent bust. And absolutely everyone wants to get in on the former and avoid the latter. "Everyone" in this case includes pretty much every possible technology company. While OpenAI is at the center of this particular gold rush, and Nvidia is selling the shovels to digital forty-niners, more conventional players aren't resting on their haunches. As happens with buzzwords, it's quickly becoming diluted -- every new PC is an "AI PC," which means very little for actual users.

Posts, as important containers of user-generated-content pieces on social media, are of tremendous social influence and commercial value. As an integral components of a post, the headline has a decisive contribution to the post's popularity. However, current mainstream method for headline generation is still manually writing, which is unstable and requires extensive human effort. This drives us to explore a novel research question: Can we automate the generation of popular headlines on social media? We collect more than 1 million posts of 42,447 celebrities from public data of Xiaohongshu, which is a well-known social media platform in China. We then conduct careful observations on the headlines of these posts. Observation results demonstrate that trends and personal styles are widespread in headlines on social medias and have significant contribution to posts's popularity. Motivated by these insights, we present MEBART, which combines Multiple preference-Extractors with Bidirectional and Auto-Regressive Transformers (BART), capturing trends and personal styles to generate popular headlines on social medias. We perform extensive experiments on real-world datasets and achieve state-of-the-art performance compared with several advanced baselines. In addition, ablation and case studies demonstrate that MEBART advances in capturing trends and personal styles.

Since the release of ChatGPT, we've seen a lot of disturbance in almost every field of our life and business. We've heard that ChatGPT can be a junior specialist killer (it passed the interview for Google's L3 entry-level software engineering position) and that it can replace the search engines we are used to (actually, the author personally sometimes suggests asking ChatGPT instead of googling). Tech enthusiasts across the globe are looking forward to putting their hands on the new Bing based on Prometheus AI (an improved version of ChatGPT). We even have heard fears about such models becoming sentient and causing certain trouble. Is at least something from the abstract above actual and possible?

Artificial intelligence has once again become a hot buzzword in tech, even somewhat knocking off the malaise the venture capital markets have been under in the last several quarters. However, this time around it may have real staying power as advancements in generative AI seem to be riding a wave of excitement some have compared to what cloud computing saw nearly two decades ago. "About two years ago, we realized (AI) had crossed a threshold," said Dave Rogenmoser, co-founder and CEO of Austin, Texas-based AI content platform Jasper. "It started producing better end results." Grow your revenue with all-in-one prospecting solutions powered by the leader in private-company data.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Platform as a service (PaaS) solutions allow for higher-level programming with dramatically reduced complexity; the application's overall development can be more efficient. This article shares two compelling examples showing how a PaaS solution developed in the cloud was transferred to the edge--involving a naval vessel and a golf cart!

In the words of former GE CEO, Jack Welch, "If you don't have a competitive advantage, don't compete." This CLE covers how technology has changed the practice of law and how we can (and should) use analytics to our transactional and litigation advantage. Examine how analytics have changed our application of model rules of professional responsibility. Understand how analytics and artificial intelligence are applied in both professional and legal world. Examine how to use and apply analytics in a legal case.