Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

It's been just over a week since Kate Middleton, the internet's favorite "missing person," claimed that a photoshopped image of her with her children on U.K. Mother's Day was edited by her, for unspecified reasons. Then, on Monday, we had our first recorded sighting of the princess, out shopping with Prince William at the Royal Farms Windsor Farm Shop, near Windsor Castle. The video was released by TMZ and the Sun, and stills from it were plastered on the front pages of all the British tabloids Tuesday. Supposedly, it was taken by a 40-year-man, Nelson Silva, who lives nearby and was quoted in TMZ as saying: "Kate looked happy and relaxed. They look happy just to be able to go to a shop and mingle.

The Department of Homeland Security is announcing a new artificial intelligence road map that includes multiple programs to better train immigration officers, plan for hazards and tackle child exploitation and fentanyl smuggling -- as the use of the technology grows within the federal government. The agency announced on Monday three pilot programs that will use AI in three agencies: U.S. Citizenship and Immigration Services (USCIS), the Federal Emergency Management Agency (FEMA) and Immigration and Customs Enforcement (ICE). The New York Times reported that the programs will be launched in partnership with OpenAI, Meta and Anthropic -- an American AI startup. Homeland Security Secretary Alejandro Mayorkas is expected to face a House impeachment vote. ICE's Homeland Security Investigation (HSI), which primarily deals with transnational crime, will develop a system to improve summaries that investigators rely on.

HOUSTON, Texas – The Johnson Space Center welcomed 12 new astronauts – 10 Americans and two from the United Arab Emirates – after the class completed a two-year training program through NASA. These astronauts will be assigned missions to the International Space Station and future commercial space stations, and will also focus on missions to the moon in preparation for Mars. Luke Delaney, a retired United States Marine Corps major from DeBary, Florida, said graduating from the program was a dream – for some, a dream that was decades in the making. Ten American astronauts and two United Arab Emirates astronauts recently graduated after completing a two-year training through NASA. When putting on his spacesuit, Delaney said he felt like he made it.

The politics of AI regulation became a little clearer this weekend, after an influential Labour thinktank laid out its framework for how the party should approach the topic in its manifesto. The policy paper, produced by the centre-left Labour Together thinktank, proposes a legal ban on dedicated nudification tools that allow users to generate explicit content by uploading images of real people. It would also create an obligation for developers of general-purpose AI tools and web hosting companies to take reasonable steps to ensure they are not involved in the production of such images, or other harmful deepfakes. Labour Together's suggestions aren't party policy yet, but they point at the sort of issues Westminster wonks think a campaign can be built on. For the last few decades, technology has been a curiously apolitical realm in the UK, with all parties agreeing on the vague idea that it's important to support British technology as a driver of growth and soft power, and little active campaigning beyond that.

In January, 1999, the Washington Post reported that the National Security Agency had issued a memo on its intranet with the subject "Furby Alert." According to the Post, the memo decreed that employees were prohibited from bringing to work any recording devices, including "toys, such as'Furbys,' with built-in recorders that repeat the audio with synthesized sound." That holiday season, the Furby, an animatronic toy resembling a small owl, had been a retail sensation; nearly two million were sold by year's end. They were now banned from N.S.A. headquarters. A worry, according to one source for the Post, was that the toy might "start talking classified." Tiger Electronics, the makers of the Furby, was perplexed.

YouTube has updated its rulebook for the era of deepfakes. Starting today, anyone uploading video to the platform must disclose certain uses of synthetic media, including generative AI, so viewers know what they're seeing isn't real. YouTube says it applies to "realistic" altered media such as "making it appear as if a real building caught fire" or swapping "the face of one individual with another's." The new policy shows YouTube taking steps that could help curb the spread of AI-generated misinformation as the US presidential election approaches. It is also striking for what it permits: AI-generated animations aimed at kids are not subject to the new synthetic content disclosure rules.

In the approximately century-long journey of robotics, humanoid robots made their debut around six decades ago. The rapid advancements in generative AI, large language models (LLMs), and large multimodal models (LMMs) have reignited interest in humanoids, steering them towards real-time, interactive, and multimodal designs and applications. This resurgence unveils boundless opportunities for AI robotics and novel applications, paving the way for automated, real-time and humane interactions with humanoid advisers, educators, medical professionals, caregivers, and receptionists. However, while current humanoid robots boast human-like appearances, they have yet to embody true humaneness, remaining distant from achieving human-like intelligence. In our comprehensive review, we delve into the intricate landscape of AI robotics and AI humanoid robots in particular, exploring the challenges, perspectives and directions in transitioning from human-looking to humane humanoids and fostering human-like robotics. This endeavour synergizes the advancements in LLMs, LMMs, generative AI, and human-level AI with humanoid robotics, omniverse, and decentralized AI, ushering in the era of AI humanoids and humanoid AI.

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping.

Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with the varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information, and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations, and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design, and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance, and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.