AI investor Arnie Bellini predicted that future battles will be fought by robots, and that the U.S.'s cyber and AI capabilities might be able to prevent a war with China before it starts. From drone swarms to gene-edited soldiers, the United States and China are racing to integrate artificial intelligence into nearly every facet of their war machines -- and a potential conflict over Taiwan may be the world's first real test of who holds the technological edge. For millennia, victory in war was determined by manpower, firepower and the grit of battlefield commanders. However, in this ongoing technological revolution, algorithms and autonomy may matter more than conventional arms. "War will come down to who has the best AI," said Arnie Bellini, a tech entrepreneur and defense investor, in an interview with Fox News Digital.

Major tech firms should commit to fully powering data centers with renewable energy by 2030, United Nations Secretary-General Antonio Guterres has said. Big tech also must be responsible in its use of water for cooling, Guterres said Tuesday in New York City as he presented the U.N.'s new report on the energy transition, Seizing the Moment of Opportunity, together with the International Renewable Energy Agency. "AI can boost efficiency, innovation and resilience in energy systems, but it is also energy hungry," Guterres said in prepared remarks. "This is not sustainable -- unless we make it so."

A Ukrainian drone strike on a private bus killed three people in the Russian-occupied region of Kherson, Russian-appointed local official Vladimir Saldo said. "Three more civilians were injured and are in serious condition," Saldo added in a Telegram post. A Ukrainian attack killed a man in Russia's Belgorod border region, the local governor said. A Russian glide bomb attack killed a 10-year-old boy in the eastern Ukrainian city of Kramatorsk, the head of the city's military administration, Oleksandr Honcharenko, said. The bomb, which caused a fire in an apartment building, also wounded five others, Honcharenko added.

Discrete Flow-based Models (DFMs) are powerful generative models for high-quality discrete data but typically suffer from slow sampling speeds due to their reliance on iterative decoding processes. This reliance on a multi-step process originates from the factorization approximation of DFMs, which is necessary for handling high-dimensional data. In this paper, we rigorously characterize the approximation error from factorization using Conditional Total Correlation (TC), which depends on the coupling. To reduce the Conditional TC and enable efficient few-step generation, we propose Rectified Discrete Flow (ReDi), a novel iterative method that reduces factorization error by rectifying the coupling between source and target distributions. We theoretically prove that each ReDi step guarantees a monotonic decreasing Conditional TC, ensuring its convergence. Empirically, ReDi significantly reduces Conditional TC and enables few-step generation. Moreover, we demonstrate that the rectified couplings are well-suited for training efficient one-step models on image generation. ReDi offers a simple and theoretically grounded approach for tackling the few-step challenge, providing a new perspective on efficient discrete data synthesis. Code is available at https://github.com/Ugness/ReDi_discrete

The recent rise in youth suicide highlights the urgent need to understand how online experiences contribute to this public health issue. Our mixed-methods approach responds to this challenge by developing a set of themes focused on risk factors for suicide mortality in online spaces among youth ages 10-24, and a framework to model these themes at scale. Using 29,124 open text summaries of death investigations between 2013-2022, we conducted a thematic analysis to identify 12 types of online activities that were considered by investigators or next of kin to be relevant in contextualizing a given suicide death. We then develop a zero-shot learning framework to model these 12 themes at scale, and analyze variation in these themes by decedent characteristics and over time. Our work uncovers several online activities related to harm to self, harm to others, interpersonal interactions, activity levels online, and life events, which correspond to different phases of suicide risk from two prominent suicide theories. We find an association between these themes and decedent characteristics like age, means of death, and interpersonal problems, and many themes became more prevalent during the 2020 COVID-19 lockdowns. While digital spaces have taken some steps to address expressions of suicidality online, our work illustrates the opportunities for developing interventions related to less explicit indicators of suicide risk by combining suicide theories with computational research.

Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.

Combining an energy-efficient drone with a high-capacity truck for last-mile package delivery can benefit operators and customers by reducing delivery times and environmental impact. However, directly integrating drone flight dynamics into the combinatorially hard truck route planning problem is challenging. Simplified models that ignore drone flight physics can lead to suboptimal delivery plans. We propose an integrated formulation for the joint problem of truck route and drone trajectory planning and a new end-to-end solution approach that combines optimization and machine learning to generate high-quality solutions in practical online runtimes. Our solution method trains neural network predictors based on offline solutions to the drone trajectory optimization problem instances to approximate drone flight times, and uses these approximations to optimize the overall truck-and-drone delivery plan by augmenting an existing order-first-split-second heuristic. Our method explicitly incorporates key kinematics and energy equations in drone trajectory optimization, and thereby outperforms state-of-the-art benchmarks that ignore drone flight physics. Extensive experimentation using synthetic datasets and real-world case studies shows that the integration of drone trajectories into package delivery planning substantially improves system performance in terms of tour duration and drone energy consumption. Our modeling and computational framework can help delivery planners achieve annual savings worth millions of dollars while also benefiting the environment.

Early large-scale audio datasets, such as LibriSpeech, were built with hundreds of individual contributors whose voices were instrumental in the development of speech technologies, including audiobooks and voice assistants. Y et, a decade later, these same contributions have exposed voice actors to a range of risks. While existing ethical frameworks emphasize Consent, Credit, and Compensation (C), they do not adequately address the emergent risks involving vocal identities that are increasingly decoupled from context, authorship, and control. Drawing on qualitative interviews with 20 professional voice actors, this paper reveals how synthetic replication of voice without clear provenance or enforceable constraints exposes individuals to both reputational and security threats. Beyond reputational harm, such as re-purposing voice data in erotic content, offensive political messaging, and meme culture, we document concerns about accountability breakdowns when their voice is leveraged to clone voices that are deployed in high-stakes scenarios such as financial fraud, misinformation campaigns, or impersonation scams. In such cases, actors face social and legal fallout without recourse, while very few of them have a legal representative or union protection. To make sense of these shifting dynamics, we introduce the PRAC framework - an expansion of C that foregrounds Privacy, Reputation, Accountability, Consent, Credit, and Compensation as interdependent pillars of data used in the synthetic voice economy. This framework captures how privacy risks are amplified through non-consensual training, how reputational harm arises from decontextualized deployment, and how accountability can be reimagined AI Data ecosystems. We argue that voice, as both a biometric identifier and creative labor, demands governance models that restore creator agency, ensure traceability, and establish enforceable boundaries for ethical reuse.

Crystal structure prediction (CSP) is a useful tool in pharmaceutical development for identifying and assessing risks associated with polymorphism, yet widespread adoption has been hindered by high computational costs and the need for both manual specification and expert knowledge to achieve useful results. Here, we introduce a fully automated, high-throughput CSP protocol designed to overcome these barriers. The protocol's efficiency is driven by Lavo-NN, a novel neural network potential (NNP) architected and trained specifically for pharmaceutical crystal structure generation and ranking. This NNP-driven crystal generation phase is integrated into a scalable cloud-based workflow. We validate this CSP protocol on an extensive retrospective benchmark of 49 unique molecules, almost all of which are drug-like, successfully generating structures that match all 110 $Z' = 1$ experimental polymorphs. The average CSP in this benchmark is performed with approximately 8.4k CPU hours, which is a significant reduction compared to other protocols. The practical utility of the protocol is further demonstrated through case studies that resolve ambiguities in experimental data and a semi-blinded challenge that successfully identifies and ranks polymorphs of three modern drugs from powder X-ray diffraction patterns alone. By significantly reducing the required time and cost, the protocol enables CSP to be routinely deployed earlier in the drug discovery pipeline, such as during lead optimization. Rapid turnaround times and high throughput also enable CSP that can be run in parallel with experimental screening, providing chemists with real-time insights to guide their work in the lab.

Network Slicing (NS) realization requires AI-native orchestration architectures to e fficiently and intelligently handle heterogeneous user requirements. To achieve this, network slicing is evolving towards a more user-centric digital transformation, focusing on architectures that incorporate native intelligence to enable self-managed connectivity in an integrated and isolated manner. However, these initiatives face the challenge of validating their results in production environments, particularly those utilizing ML-enabled orchestration, as they are often tested in local networks or laboratory simulations. This paper proposes a large-scale validation method using a network slicing prediction model to forecast latency using Deep Neural Networks (DNNs) and basic ML algorithms embedded within an NS architecture evaluated in real large-scale production testbeds. It measures and compares the performance of di fferent DNNs and ML algorithms, considering a distributed database application deployed as a network slice over two large-scale production testbeds. The investigation highlights how AI-based prediction models can enhance network slicing orchestration architectures and presents a seamless, production-ready validation method as an alternative to fully controlled simulations or laboratory setups. Keywords: Network Slicing, Deep Neural Networks, Machine Learning, Service-Level Agreement, Distributed Database1. Introduction Modern applications require challenging behaviors from physical networks to satisfy stringent requirements such as ultra-reliability, low latency, and high throughput [1]. In addition to these quantifiable network requirements, it is necessary to incorporate seamless, intelligent, and pervasive network capabilities to satisfy user demands [2, 3]. Although network management, control planes, and data planes have evolved to address this issue, challenges remain and require further large-scale evaluation. Many approaches, technologies, and methods have been developed to build user-oriented network architectures that provide connectivity in an isolated and personalized manner [4]. One key technological enabler of this vision is network slicing, which establishes network connectivity on top of physical infrastructure while ensuring isolation, end-to-end connectivity, and application-driven requirements, with dedicated control and data planes [5]. With this service-tailoring capability, Machine Learning (ML) e ffectively addresses various management and orchestration challenges, thereby enabling intelligent and real-time insights for service provider managers. AI techniques, such as reinforcement learning, supervised learning, and unsupervised learning, have been e ff ectively integrated with network orchestrators to mitigate cybersecurity threats, enable intelligent resource allocation, and ensure Service-Level Agreement (SLA) assurance for network slicing [7, 8, 9, 10].