The "Kill Them All" Edition US officials debate who to blame for the military killing of shipwrecked alleged drug smugglers; Democrats celebrate despite losing a special election in Tennessee; and the future of self-driving cars. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.

Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work data, historical event retrieval, and risk analysis to proactively identify potential hazards. A human-in-the-loop mechanism allows subject matter experts (SMEs) to refine predictions, creating an adaptive learning loop that enhances performance over time. By combining SME collaboration with iterative agentic reasoning, HARNESS improves the reliability and efficiency of predictive safety systems. Preliminary deployment shows promising results, with future work focusing on quantitative evaluation of accuracy, SME agreement, and decision latency reduction.

Transportation remains a major contributor to greenhouse gas emissions, highlighting the urgency of transitioning toward sustainable alternatives such as Electric Vehicles (EVs). Yet, uneven spatial distribution and irregular utilization of charging infrastructure create challenges for both power grid stability and investment planning. This study introduces Traffic-Weather Graph Convolutional Network (TW-GCN), a spatio-temporal forecasting framework that combines Graph Convolutional Networks with temporal architectures to predict EV charging demand in Tennessee, United States. We utilize real-world traffic flows, weather conditions, and proprietary data provided by one of the largest U.S.-based EV infrastructure companies to capture both spatial dependencies and temporal dynamics. Extensive experiments across varying forecasting horizons, clustering strategies, and sequence lengths reveal that mid-horizon (3-hour) forecasts achieve the best balance between responsiveness and stability, with One-dimensional convo-lutional neural networks consistently outperforming other temporal models. Regional analysis shows disparities in predictive accuracy across East, Middle, and West Tennessee, reflecting how station density, Points of Interest and local demand variability shape model capabilities. The proposed TW-GCN framework advances the integration of data-driven intelligence into EV infrastructure planning while supporting sustainable mobility transitions.

Organizations increasingly adopt Retrieval-Augmented Generation (RAG) to enhance Large Language Models with enterprise-specific knowledge. However, current data quality (DQ) frameworks have been primarily developed for static datasets, and only inadequately address the dynamic, multi-stage nature of RAG systems. This study aims to develop DQ dimensions for this new type of AI-based systems. We conduct 16 semi-structured interviews with practitioners of leading IT service companies. Through a qualitative content analysis, we inductively derive 15 distinct DQ dimensions across the four processing stages of RAG systems: data extraction, data transformation, prompt & search, and generation. Our findings reveal that (1) new dimensions have to be added to traditional DQ frameworks to also cover RAG contexts; (2) these new dimensions are concentrated in early RAG steps, suggesting the need for front-loaded quality management strategies, and (3) DQ issues transform and propagate through the RAG pipeline, necessitating a dynamic, step-aware approach to quality management.

Effective prompt engineering is critical to realizing the promised productivity gains of large language models (LLMs) in knowledge-intensive tasks. Yet, many users struggle to craft prompts that yield high-quality outputs, limiting the practical benefits of LLMs. Existing approaches, such as prompt handbooks or automated optimization pipelines, either require substantial effort, expert knowledge, or lack interactive guidance. To address this gap, we design and evaluate PromptPilot, an interactive prompting assistant grounded in four empirically derived design objectives for LLM-enhanced prompt engineering. We conducted a randomized controlled experiment with 80 participants completing three realistic, work-related writing tasks. Participants supported by PromptPilot achieved significantly higher performance (median: 78.3 vs. 61.7; p = .045, d = 0.56), and reported enhanced efficiency, ease-of-use, and autonomy during interaction. These findings empirically validate the effectiveness of our proposed design objectives, establishing LLM-enhanced prompt engineering as a viable technique for improving human-AI collaboration.

As Congress races to pass President Donald Trump's "Big Beautiful Bill," it's also sprinting to placate the many haters of the bill's "AI moratorium" provision which originally required a 10-year pause on state AI regulations. The provision, which was championed by White House AI czar and venture capitalist David Sacks, has proved remarkably unpopular with a diverse contingent of lawmakers ranging from 40 state attorneys general to the ultra-MAGA Representative Marjorie Taylor Greene. Sunday night, Senator Marsha Blackburn and Senator Ted Cruz announced a new version of the AI moratorium, knocking the pause from a full decade down to five years and adding a variety of carve-outs. But after critics attacked the watered-down version of the bill as a "get-out-of-jail free card" for Big Tech, Blackburn reversed course Monday evening. "While I appreciate Chairman Cruz's efforts to find acceptable language that allows states to protect their citizens from the abuses of AI, the current language is not acceptable to those who need these protections the most," Blackburn said in a statement to WIRED.

The site of the secret Manhattan Project in Oak Ridge, Tennessee has a new mission to help achieve an A.I. advantage over China. A new uranium enrichment facility in Oak Ridge will supply nuclear fuel to the reactors that power A.I. data centers. Over 80 years after scientists of the'Manhattan Project' harnessed the power of the atom to end World War II, the top-secret worksite has a new mission to help dominate AI before China does. The first phase of the United States' latest uranium enrichment facility opened in Oak Ridge, Tennessee in May. Uranium powers the nuclear reactors the AI data centers are turning to for reliable energy.

Parents spoke after the Antioch High School shooting on Wednesday, Jan. 22, outside of Nashville, Tennessee. Antioch High School in Nashville, Tennessee, where a deadly shooting took place last Wednesday, did not have metal detectors due to some administrators' concerns about racism, the New York Post reported. "I knew this day was gonna happen," Fran Bush, a former Metro Nashville Public Schools (MNPS) board member, told the New York Post. "I knew it was gonna happen just because it's like a free open door, everybody coming in." The shooting, which left 16-year-old student Josselin Corea Escalante and the suspect dead, has parents calling for the school to bring in metal detectors after the AI security system failed to detect the 17-year-old gunman's weapon.

At the beginning of 2024, the American Cancer Society predicted that 2,001,140 new cancer cases and 611,720 cancer deaths would occur in the United States. Now, as the year draws to a close, experts are looking back and reflecting on the discoveries and advances that have been made in the field of cancer treatment and prevention. Fox News Digital spoke with four oncologists from the Sarah Cannon Research Institute in Nashville, Tennessee, about the most notable accomplishments of 2024 and what they see on the horizon for 2025. See the answers and questions below. Krish Patel, M.D., is director of lymphoma research at Sarah Cannon Research Institute in Nashville, Tennessee.

John Oates, of Hall & Oates, is wary of the future represented by artificial intelligence in the music industry. "Look at what's coming in with AI, the possibility that AI is going to be replacing songwriters and artists for that matter," Oates told Fox News Digital. "The idea that there could be a new… David Bowie album. AI could take David Bowie's voice and extrapolate and sample his music for his entire career and write new David Bowie songs, and the record company could put it out." He added, "A younger generation might not even know. They might not even know he's dead for that matter. So there's a lot going on and you have to pay attention."