Artificial intelligence company Anthropic announced a $50bn investment in computing infrastructure on Wednesday that will include new datacenters in Texas and New York . "We're getting closer to AI that can accelerate scientific discovery and help solve complex problems in ways that weren't possible before," Anthropic's CEO, Dario Amodei, said in a press release. Building the massive information warehouses takes an average of two years in the US and requires copious amounts of energy to fuel the facilities. The company, maker of the AI chatbot Claude, popular with businesses adopting AI, said in a statement that the "scale of this investment is necessary to meet the growing demand for Claude from hundreds of thousands of businesses while keeping our research at the frontier". Anthropic said its projects will create about 800 permanent jobs and 2,400 construction jobs.

Machine Learning (ML) is profoundly reshaping the way researchers create, implement, and operate data-intensive software. Its adoption, however, introduces notable challenges for computing infrastructures, particularly when it comes to coordinating access to hardware accelerators across development, testing, and production environments. The INFN initiative AI_INFN (Artificial Intelligence at INFN) seeks to promote the use of ML methods across various INFN research scenarios by offering comprehensive technical support, including access to AI-focused computational resources. Leveraging the INFN Cloud ecosystem and cloud-native technologies, the project emphasizes efficient sharing of accelerator hardware while maintaining the breadth of the Institute's research activities. This contribution describes the deployment and commissioning of a Kubernetes-based platform designed to simplify GPU-powered data analysis workflows and enable their scalable execution on heterogeneous distributed resources. By integrating offload-ing mechanisms through Virtual Kubelet and the InterLink API, the platform allows workflows to span multiple resource providers, from Worldwide LHC Computing Grid sites to high-performance computing centers like CINECA Leonardo. We will present preliminary benchmarks, functional tests, and case studies, demonstrating both performance and integration outcomes.

B) Details of the employed hyperparameters. For all fine-tuning, the optimizer is set as SGD with momentum of 0.9 and initial learning rate of 30 following [ When fine-tuning for linear separability performance, we train for 30 epochs and decrease the learning rate by 10 times at epochs 10 and 20. The initial lr is set as 0.02 and employing cosine learning rate decay without

The emergence of agentic Artificial Intelligence (AI), which can operate autonomously, demonstrate goal-directed behavior, and adaptively learn, indicates the onset of a massive change in today's computing infrastructure. This study investigates how agentic AI models' multiple characteristics may impact the architecture, governance, and operation under which computing environments function. Agentic AI has the potential to reduce reliance on extremely large (public) cloud environments due to resource efficiency, especially with processing and/or storage. The aforementioned characteristics provide us with an opportunity to canvas the likelihood of strategic migration in computing infrastructures away from massive public cloud services, towards more locally distributed architectures: edge computing and on-premises computing infrastructures. Many of these likely migrations will be spurred by factors like on-premises processing needs, diminished data consumption footprints, and cost savings. This study examines how a solution for implementing AI's autonomy could result in a re-architecture of the systems and model a departure from today's governance models to help us manage these increasingly autonomous agents, and an operational overhaul of processes over a very diverse computing systems landscape that bring together computing via cloud, edge, and on-premises computing solutions. To enable us to explore these intertwined decisions, it will be fundamentally important to understand how to best position agentic AI, and to navigate the future state of computing infrastructures.

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load-derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence at INFN") aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as e ffec-tively as possible, ensuring the diversity of the Institute's research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.

The Israeli military's reliance on Microsoft's cloud technology and artificial intelligence systems surged during the most intensive phase of its bombardment of Gaza, leaked documents reveal. The files offer an inside view of how Microsoft deepened its relationship with Israel's defence establishment after 7 October 2023, supplying the military with greater computing and storage services and striking at least 10m in deals to provide thousands of hours of technical support. Microsoft's deep ties with Israel's military are revealed in an investigation by the Guardian with the Israeli-Palestinian publication 972 Magazine and a Hebrew-language outlet, Local Call. It is based in part on documents obtained by Drop Site News, which has published its own story. The investigation, which also draws on interviews with sources from across Israel's defence and intelligence establishment, sheds new light on how the Israel Defense Forces (IDF) turned to major US tech companies to meet the technological demands of war. After launching its offensive in Gaza in October 2023, the IDF faced a sudden rush in demand for storage and computing power, leading it to swiftly expand its computing infrastructure and embrace what one commander described as "the wonderful world of cloud providers".