FOX News Media CEO Suzanne Scott participated in a fireside chat on Thursday with University of South Carolina students, discussing media operations and the business of journalism.

At an airport in South Carolina on Thursday, US representative Nancy Mace called police officers "fucking incompetent" and berated them repeatedly, according to an incident report. Nancy Mace, the South Carolina Republican congresswoman, unleashed a tirade against law enforcement at the Charleston International Airport on Thursday, WIRED has learned. According to an incident report obtained by WIRED under South Carolina's Freedom of Information Act, Mace cursed at police officers, making repeated derogatory comments toward them. The report says that a Transportation Security Administration (TSA) supervisor told officers that Mace had treated their staff similarly and that they would be reporting her to their superiors. According to the report, officers with the Charleston County Aviation Authority Police Department were tasked with meeting Mace at 6:30 am to escort her from the curb to her flight and had been told that she would be arriving in a white BMW at the ticketing curb area.

The growing use of FPGAs in reconfigurable systems introducessecurity risks through malicious bitstreams that could cause denial-of-service (DoS), data leakage, or covert attacks. We investigated chip-level hardware malicious payload in embedded systems and proposed a supervised machine learning method to detect malicious bitstreams via static byte-level features. Our approach diverges from existing methods by analyzing bitstreams directly at the binary level, enabling real-time detection without requiring access to source code or netlists. Bitstreams were sourced from state-of-the-art (SOTA) benchmarks and re-engineered to target the Xilinx PYNQ-Z1 FPGA Development Board. Our dataset included 122 samples of benign and malicious configurations. The data were vectorized using byte frequency analysis, compressed using TSVD, and balanced using SMOTE to address class imbalance. The evaluated classifiers demonstrated that Random Forest achieved a macro F1-score of 0.97, underscoring the viability of real-time Trojan detection on resource-constrained systems. The final model was serialized and successfully deployed via PYNQ to enable integrated bitstream analysis.

Breakthroughs, discoveries, and DIY tips sent every weekday. Safety workers recently encountered a scenario straight out of a sci-fi film while surveying a decommissioned nuclear weapons plant in South Carolina. According to the US Department of Energy, on July 3 a team at the Savannah River Site near the Georgia border, detected an irradiated wasp nest that exhibited a radiation level 10 times higher than the federal regulatory limit. The hazardous insect abode was located near a set of tanks filled with liquid nuclear waste, although the team didn't detect any leaks. Instead, experts believe the nest set off Geiger counters through what's known as "onsite legacy radioactive contamination."

The AI data center also promises to transform the state's energy future. Stretching in length for more than a mile, it will be Meta's largest in the world, and it will have an enormous appetite for electricity, requiring two gigawatts for computation alone (the electricity for cooling and other building needs will add to that). When it's up and running, it will be the equivalent of suddenly adding a decent-size city to the region's grid--one that never sleeps and needs a steady, uninterrupted flow of electricity. To power the data center, Entergy aims to spend 3.2 billion to build three large natural-gas power plants with a total capacity of 2.3 gigawatts and upgrade the grid to accommodate the huge jump in anticipated demand. In its filing to the state's power regulatory agency, Entergy acknowledged that natural-gas plants "emit significant amounts of CO2" but said the energy source was the only affordable choice given the need to quickly meet the 24-7 electricity demand from the huge data center.

Collaborative assistants, or chatbots, are data-driven decision support systems that enable natural interaction for task completion. While they can meet critical needs in modern society, concerns about their reliability and trustworthiness persist. In particular, Large Language Model (LLM)-based chatbots like ChatGPT, Gemini, and DeepSeek are becoming more accessible. However, such chatbots have limitations, including their inability to explain response generation, the risk of generating problematic content, the lack of standardized testing for reliability, and the need for deep AI expertise and extended development times. These issues make chatbots unsuitable for trust-sensitive applications like elections or healthcare. To address these concerns, we introduce SafeChat, a general architecture for building safe and trustworthy chatbots, with a focus on information retrieval use cases. Key features of SafeChat include: (a) safety, with a domain-agnostic design where responses are grounded and traceable to approved sources (provenance), and 'do-not-respond' strategies to prevent harmful answers; (b) usability, with automatic extractive summarization of long responses, traceable to their sources, and automated trust assessments to communicate expected chatbot behavior, such as sentiment; and (c) fast, scalable development, including a CSV-driven workflow, automated testing, and integration with various devices. We implemented SafeChat in an executable framework using the open-source chatbot platform Rasa. A case study demonstrates its application in building ElectionBot-SC, a chatbot designed to safely disseminate official election information. SafeChat is being used in many domains, validating its potential, and is available at: https://github.com/ai4society/trustworthy-chatbot.

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various prompting and fine-tuning techniques to guide both proprietary and open-source LLMs in adhering to these processes, and we evaluate the agreement between LLM-generated diagnostic outcomes and expert-validated ground truth. For fine-tuning, we utilize the Mentalllama and Llama models, while for prompting, we experiment with proprietary models like GPT-3.5 and GPT-4o, as well as open-source models such as llama-3.1-8b and mixtral-8x7b.

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits.

Former Chicago 911 dispatcher Keith Thornton Jr. joined "Fox & Friends First" to discuss how the crime surge is affecting law enforcement and communities nationwide. Understaffed 911 call centers across the country field non-emergency calls about stray animals or noise complaints on top of their workload of answering serious reports of medical emergencies, crimes and even death. Officials in Charleston County, South Carolina, however, are now leveraging artificial intelligence to streamline non-emergency calls in an effort to free up 911 operators to focus on getting first responders to the scene of emergency incidents as quickly as possible. "Our job is to serve the public the best way we can. So, I am not in any way demeaning anyone from the public, but someone who has their favorite cat stuck in a tree, that's an emergency for them as compared to someone's just been shot," Jim Lake, director of the Charleston County Consolidated Emergency Communications Center, told Fox News Digital in a recent phone interview.

Pawleys Island, South Carolina, Mayor Brian Henry tells "Your World" that Hurricane Ian was different and brought a significant storm surge to the island. A high-tech sailing drone was deployed onto the Atlantic Ocean near Charleston, South Carolina, this past weekend to collect weather data directly from wicked hurricanes. The autonomous ocean drone, known as a saildrone, was redeployed by California-based company Saildrone Inc., which designs and operates autonomous ocean drones, in partnership with the National Oceanic and Atmospheric Administration (NOAA) to assist the agency in data collection on hurricanes. The same saildrone made international headlines in 2021 when it captured the "first-ever video from inside a major hurricane at sea" when Hurricane Sam barreled across the Atlantic. NOAA has previously incorporated drones into its research of hurricanes and 2023 will see an even larger and more high-tech fleet.