BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design

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We propose a general-purpose approach for improving the ability of Large Language Models (LLMs) to intelligently and adaptively gather information from a user or other external source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-turn conversational agents and interactively interface with external environments. Our approach, which …

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A New Era of Defense Innovation

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Leading the Charge on Procurement Reform America’s defense industrial base stands on the precipice of massive, historic change. Given its access to a flourishing and dynamic private sector, the US defense industrial base should be a nimble, powerful engine of both creativity and lethality, capable of churning out the kinds of innovations that guarantee dominance …

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Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads

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Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. SageMaker Studio serves as a web Integrated Development Environment (IDE) for end-to-end machine learning (ML) development, …

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Cloud CISO Perspectives: 2025 in review: Cloud security basics and evolving AI

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Welcome to the second Cloud CISO Perspectives for December 2025. Today, Google Cloud’s Nick Godfrey, senior director, and Anton Chuvakin, security advisor, look back at the year that was. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and …

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Korrektur: Wie das Online-Magazin Die Republik einen Regierungsbericht zu Palantir verdrehte

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(Scroll down for English translation below) Einleitung In einem im Dezember 2025 erschienenen Artikel des Magazins Republik wird auf einen Bericht des Armeestabs der Schweizer Armee aus dem Jahr 2024 aufmerksam gemacht, in dem eine mögliche Einführung der Software unseres Unternehmens Palantir Technologies Inc. (Palantir) bewertet wird. Der Artikel zeichnet ein falsches wie irreführendes Bild …

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Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents

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This post is co-written with Ranjit Rajan, Abdullahi Olaoye, and Abhishek Sawarkar from NVIDIA. AI’s next frontier isn’t merely smarter chat-based assistants, it’s autonomous agents that reason, plan, and execute across entire systems. But to accomplish this, enterprise developers need to move from prototypes to production-ready AI agents that scale securely. This challenge grows as …

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AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities

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Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly rely on pretrained language models’ parametric knowledge, pre-constructed queries, or single-step retrieval, typically requiring substantial supervision and training data. Even so, they often fail to capture …

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Tracking and managing assets used in AI development with Amazon SageMaker AI 

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Building custom foundation models requires coordinating multiple assets across the development lifecycle such as data assets, compute infrastructure, model architecture and frameworks, lineage, and production deployments. Data scientists create and refine training datasets, develop custom evaluators to assess model quality and safety, and iterate through fine-tuning configurations to optimize performance. As these workflows scale across …

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Automate AI and HPC clusters with Cluster Director, now generally available

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The complexity of the infrastructure behind AI training and high performance computing (HPC) workloads can really slow teams down. At Google Cloud, where we work with some of the world’s largest AI research teams, we see it everywhere we go: researchers hampered by complex configuration files, platform teams struggling to manage GPUs with home-grown scripts, …

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