The growing and evolving landscape of cybersecurity threats necessitates the development of supporting tools and platforms that allow for the creation of realistic IT environments operating within virtual, controlled settings as Cyber Ranges (CRs). CRs can be exploited for analyzing vulnerabilities and experimenting with the effectiveness of devised countermeasures, as well as serving as training environments for building cyber security skills and abilities for IT operators. This paper proposes ARCeR as an innovative solution for the automatic generation and deployment of CRs, starting from user-provided descriptions in a natural language. ARCeR relies on the Agentic RAG paradigm, which allows it to fully exploit state-of-art AI technologies. Experimental results show that ARCeR is able to successfully process prompts even in cases that LLMs or basic RAG systems are not able to cope with. Furthermore, ARCeR is able to target any CR framework provided that specific knowledge is made available to it.

AI Agents powered by Large Language Models are transforming the world through enormous applications. A super agent has the potential to fulfill diverse user needs, such as summarization, coding, and research, by accurately understanding user intent and leveraging the appropriate tools to solve tasks. However, to make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required to ensure high efficiency and low cost. This position paper presents a design of the Super Agent System powered by the hybrid AI routers. Upon receiving a user prompt, the system first detects the intent of the user, then routes the request to specialized task agents with the necessary tools or automatically generates agentic workflows. In practice, most applications directly serve as AI assistants on edge devices such as phones and robots. As different language models vary in capability and cloud-based models often entail high computational costs, latency, and privacy concerns, we then explore the hybrid mode where the router dynamically selects between local and cloud models based on task complexity. Finally, we introduce the blueprint of an on-device super agent enhanced with cloud. With advances in multi-modality models and edge hardware, we envision that most computations can be handled locally, with cloud collaboration only as needed. Such architecture paves the way for super agents to be seamlessly integrated into everyday life in the near future.

Making new friends is hard. Finding out what trousers exist in the world other than black ones is also, apparently, hard. Fortunately, for an AI-enabled generation of students, help with the complexities of campus life is just a prompt away. If you are really stuck on an essay or can't decide between management consulting or a legal career, or need suggestions on what you can cook with tomatoes, mushrooms, beetroot, mozzarella, olive oil and rice, then ChatGPT is there. It will to listen to you, analyse your inputs, and offer up a perfectly structured paper, a convincing cover letter, or a workable recipe for tomato and mushroom risotto with roasted beetroot and mozzarella. I know this because three undergraduates have given me permission to eavesdrop on every conversation they have had with ChatGPT over the past 18 months.

MikeroweWorks Foundation founder Mike Rowe joins'The Brian Kilmeade Show' to discuss how AI and robots threaten white-collar jobs, as the nation faces a need for blue-collar workers. 'UNDENIABLE': Mike Rowe is sounding the alarm about the future of white and blue-collar jobs, and is urging young Americans to rethink their career choices due to threats from artificial intelligence. 'ALL IN': President Donald Trump is going all in on artificial intelligence, with a top Meta executive voicing strong support for his bold strategy. Speaking at a tech summit in Washington, Trump outlined his vision for a future driven by American innovation and secured by global artificial intelligence leadership. INNOVATION BOOST: Nvidia CEO Jensen Huang said in an interview Wednesday that the Trump administration's artificial intelligence plan is poised to boost innovation and AI deployment in the U.S. IMMINENT CRISIS: OpenAI CEO Sam Altman warned Wall Street executives that bad actors could exploit digital voice ID authentication to defraud consumers by enabling large money transfers, creating what he describes as an imminent fraud crisis. STARGATE OPENS: Oracle and OpenAI have inked an agreement to further develop the Stargate project as part of a broader pledge to expand Artificial Intelligence (AI) infrastructure in the United States.

Computers have taken the crown in chess, Go and poker, but when it comes to competitive coding, humans still have the edge – just. Earlier this month Przemysław Dębiak, a Polish coder and mind sports champion, narrowly clinched a victory over OpenAI's entrant in the AtCoder World Tour Finals 2025, in Tokyo. However, the elite coder, who goes by the online name Psyho, predicts he may be the last human to win the prestigious title due to the incredible pace of technological progress. "That's probable," said Psyho, 41, who previously worked at OpenAI before retiring five years ago. "I would prefer not, mostly because I like these competitions and knowing there's this magical entity that can do it better than me would be a little bit frustrating."

Amazon has acquired Bee, a relatively new AI company that debuted an always-listening wearable earlier this year. Bee cofounder Maria de Lourdes Zollo shared the news via LinkedIn. We covered Bee AI at CES 2025--it was one of many new wearables that promised to listen to everything around you. It didn't save audio recordings, but it used the power of third-party and in-house large language models to transcribe words and create a journal of sorts, offering insights into your day, crafting takeaways, actionable tasks, and summaries of conversations. Despite being one of the first on the scene with its Alexa voice assistant, Amazon has been trailing behind the likes of OpenAI and Google's Gemini in the AI space, only recently debuting the upgraded version of Alexa, powered by LLMs.

Now Microsoft Copilot has a face, with reactions to what you tell it. Microsoft is showing off how Copilot could "look": as an anthropomorphic teardrop of sorts, with expressions that react to your interactions in real time. The discovery was reported Friday by The Verge. Right now, the new look of Copilot is being presented to a limited number of users. Microsoft's web page shows how to discover if you have it: by opening Copilot, clicking the "mic" icon to launch verbal interactions with Copilot, then clicking the "gear," or settings, icon.

On November 2, 2022, I attended a Google AI event in New York City. One of the themes was responsible AI. As I listened to executives talk about how they aligned their technology with human values, I realized that the malleability of AI models was a double-edged sword. Models could be tweaked to, say, minimize biases, but also to enforce a specific point of view. Governments could demand manipulation to censor unwelcome facts and promote propaganda.

Star founders, Beijing officials and deep-pocketed financiers converge on Shanghai by the thousands this weekend to attend China's most important artificial intelligence summit. At the top of the agenda: how to propel Beijing's ambitions to leapfrog the U.S. in AI -- and profit off that drive. The World Artificial Intelligence Conference (WAIC), which has featured billionaire entrepreneurs Elon Musk and Jack Ma in years past, was devised to showcase the cutting-edge of Chinese technology. This year's attendance may hit a record as it's taking place at a critical juncture in the U.S.-Chinese tech rivalry. This week, U.S. President Donald Trump unveiled his so-called AI Action Plan -- a sort of call to arms to ensure the country keeps its lead in the post-ChatGPT epoch.

Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adoption of Bayesian and ensemble-based methods. However, such methods typically necessitate computationally expensive sampling, involving multiple forward passes to effectively estimate predictive uncertainty. In this paper, we introduce a novel approach enabling efficient and effective uncertainty estimation in LLMs without sacrificing performance. Specifically, we distill uncertainty-aware teacher models - originally requiring multiple forward passes - into compact student models sharing the same architecture but fine-tuned using Low-Rank Adaptation (LoRA). We compare two distinct distillation strategies: one in which the student employs traditional softmax-based outputs, and another in which the student leverages Dirichlet-distributed outputs to explicitly model epistemic uncertainty via evidential learning. Empirical evaluations on classification datasets demonstrate that such students can achieve comparable or superior predictive and uncertainty quantification performance relative to their teacher models, while critically requiring only a single forward pass. To our knowledge, this is the first demonstration that immediate and robust uncertainty quantification can be achieved in LLMs through evidential distillation.