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AI policy sets boundaries on acceptable behavior for AI models, but this is challenging in the context of large language models (LLMs): how do you ensure coverage over a vast behavior space? We introduce policy maps, an approach to AI policy design inspired by the practice of physical mapmaking. Instead of aiming for full coverage, …
Read more “Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors”
In high-volume healthcare contact centers, every patient conversation carries both clinical and operational significance, making accurate real-time transcription necessary for automated workflows. Accurate, instant transcription enables intelligent automation without sacrificing clarity or care, so that teams can automate electronic medical record (EMR) record matching, streamline workflows, and eliminate manual data entry. By removing routine process …
Scientific inquiry has always been a journey of curiosity, meticulous effort, and groundbreaking discoveries. Today, that journey is being redefined, fueled by the incredible capabilities of AI. It’s moving beyond simply processing data to actively participating in every stage of discovery, and Google Cloud is at the forefront of this transformation, building the tools and …
Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at …
Read more “Build reliable AI systems with Automated Reasoning on Amazon Bedrock – Part 1”
Welcome to the second Cloud CISO Perspectives for October 2025. Today, Jeanette Manfra, senior director, Global Risk and Compliance, shares her thoughts on the role of AI in risk management. 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 …
Read more “Cloud CISO Perspectives: AI as a strategic imperative to manage risk”
Recommendation systems in multi-stakeholder environments often require optimizing for multiple objectives simultaneously to meet supplier and consumer demands. Serving recommendations in these settings relies on efficiently combining the objectives to address each stakeholder’s expectations, often through a scalarization function with pre-determined and fixed weights. In practice, selecting these weights becomes a consequent problem. Recent work …
Read more “SEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework”
AI agents need to browse the web on your behalf. When your agent visits a website to gather information, complete a form, or verify data, it encounters the same defenses designed to stop unwanted bots: CAPTCHAs, rate limits, and outright blocks. Today, we are excited to share that AWS has a solution. Amazon Bedrock AgentCore …
The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research.
Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human …
Read more “Toward Machine Interpreting: Lessons from Human Interpreting Studies”
Language models prompted with a user description or persona are being used to predict the user’s preferences and opinions. However, existing approaches to building personas mostly rely on a user’s demographic attributes and/or prior judgments, but not on any underlying reasoning behind a user’s judgments. We introduce PB&J (Psychology of Behavior and Judgments), a framework …
Read more “Improving Language Model Personas via Rationalization with Psychological Scaffolds”