Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work has focused on single-turn safety, while adaptability and diversity remain among the key challenges of multi-turn red-teaming. To address these challenges, we present X-Teaming, a scalable framework that systematically explores how seemingly harmless interactions escalate into harmful outcomes and generates corresponding attack scenarios. X-Teaming employs collaborative agents for planning, attack optimization, and verification, achieving state-of-the-art multi-turn jailbreak effectiveness and diversity with success rates up to 98.1% across representative leading open-weight and closed-source models. In particular, X-Teaming achieves a 96.2% attack success rate against the latest Claude 3.7 Sonnet model, which has been considered nearly immune to single-turn attacks. Building on X-Teaming, we introduce XGuard-Train, an open-source multi-turn safety training dataset that is 20x larger than the previous best resource, comprising 30K interactive jailbreaks, designed to enable robust multi-turn safety alignment for LMs. Our work offers essential tools and insights for mitigating sophisticated conversational attacks, advancing the multi-turn safety of LMs.

Fox News senior medical analyst Dr. Marc Siegel shares his perspective on whether the mosquito-borne virus in China will spread to the United States and how AI can be detrimental to children's and young adults' mental health on'Fox Report.' The first case of a travel-associated human screwworm infection has been detected in Maryland. Andrew Nixon, spokesperson for the Department of Health and Human Services, confirmed to Fox News Digital that the patient had recently returned from a trip to El Salvador, a country affected by a screwworm outbreak. The Centers for Disease Control and Prevention (CDC) worked in conjunction with the Maryland Department of Health to investigate the case. The CDC confirmed the diagnosis on Aug. 4 after experts reviewed larvae images. "The risk to public health in the United States from this introduction is very low," Nixon said.

In early May, Sam Altman traveled to Washington to tell a story about America. Appearing before a Senate committee, Altman described how he came of age as the internet took off, how he stayed up late in his family's attic and learned to code on products that were invented in the United States--a personal computer, its silicon chips and accompanying software. That early experience with the "spirit of American innovation," Altman told the senators, put him on a path to found OpenAI, launch ChatGPT, and set off the AI boom. "I think America is just an incredible and special thing," he said, "and it will not only be the place where the AI revolution happens but all the revolutions after." Altman's written testimony, which was submitted to the Senate, added an important asterisk that he did not speak aloud that day.

The Sun's most complex mysteries could soon be solved thanks to artificial intelligence. On August 20, IBM and NASA announced the launch of Surya, a foundation model for the sun. Having been trained on large datasets of solar activity, this AI tool aims to deepen humanity's understanding of solar weather and accurately predict solar flares--bursts of electromagnetic radiation emitted by our star that threaten both astronauts in orbit and communications infrastructure on Earth. Surya was trained with nine years of data collected by NASA's Solar Dynamics Observatory (SDO), an instrument that has orbited the sun since 2010, taking high-resolution images every 12 seconds. The SDO captures observations of the sun at various different electromagnetic wavelengths to estimate the temperature of the star's layers.

Large gray mushroom clouds form the backdrop of the Tokyo skyline as the capital becomes engulfed in smog. Pedestrians walk through the familiar streets of the capital's Shibuya Ward -- except it is blanketed in ash. It is all part of an artificial intelligence-generated video the Tokyo Metropolitan Government released last week to raise awareness of what could happen to the capital if Mount Fuji erupted. This was the first time for AI to be used to encourage further understanding of a potential Mount Fuji eruption and to call for better preparation among Tokyoites.

Lt. Gen. Keith Kellogg discusses the latest with the Ukraine and Russia war after a deadly Russian attack on'America Reports.' Russian officials said Ukrainian drones ignited an overnight fire at a nuclear plant in Russia's Kursk region. The strikes coincided with Ukraine's 34th Independence Day, marking its 1991 break from the Soviet Union. Russia said the strikes hit several power facilities. The plant fire was quickly extinguished. A transformer was damaged, but radiation levels remained normal, and no injuries were reported.

In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.

Non-emergency municipal services such as city 311 systems have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfaction, and increase the number of follow-up inquiries. Predicting the service time for municipal service requests is challenging due to several complex factors: dynamic spatial-temporal correlations, underlying interactions among heterogeneous service request types, and high variation in service duration even within the same request category. In this work, we propose MuST2-Learn: a Multi-view Spatial-Temporal-Type Learning framework designed to address the aforementioned challenges by jointly modeling spatial, temporal, and service type dimensions. In detail, it incorporates an inter-type encoder to capture relationships among heterogeneous service request types and an intra-type variation encoder to model service time variation within homogeneous types. In addition, a spatiotemporal encoder is integrated to capture spatial and temporal correlations in each request type. The proposed framework is evaluated with extensive experiments using two real-world datasets. The results show that MuST2-Learn reduces mean absolute error by at least 32.5%, which outperforms state-of-the-art methods.

Entities in discourse vary broadly in salience: main participants, objects and locations are noticeable and memorable, while tangential ones are less important and quickly forgotten, raising questions about how humans signal and infer relative salience. Using a graded operationalization of salience based on summary-worthiness in multiple summaries of a discourse, this paper explores data from 24 spoken and written genres of English to extract a multifactorial complex of overt and implicit linguistic cues, such as recurring subjecthood or definiteness, discourse relations and hierarchy across utterances, as well as pragmatic functional inferences based on genre and communicative intent. Tackling the question 'how is the degree of salience expressed for each and every entity mentioned?' our results show that while previous approaches to salience all correlate with our salience scores to some extent, no single generalization is without exceptions, and the phenomenon cuts across all levels of linguistic representation.

Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments. However, UAM faces critical operational challenges, particularly the balance between minimizing noise exposure and maintaining safe separation in low-altitude urban airspace, two objectives that are often addressed separately. We propose a reinforcement learning (RL)-based air traffic management system that integrates both noise and safety considerations within a unified, decentralized framework. Under this scalable air traffic coordination solution, agents operate in a structured, multi-layered airspace and learn altitude adjustment policies to jointly manage noise impact and separation constraints. The system demonstrates strong performance across both objectives and reveals tradeoffs among separation, noise exposure, and energy efficiency under high traffic density. The findings highlight the potential of RL and multi-objective coordination strategies in enhancing the safety, quietness, and efficiency of UAM operations.