September 2025
Amazon Science
The latest news and research from Amazon’s science community. #AmazonScience
Our latest newsletter features new insights on foundation models in scientific computing, ML-powered book discovery at Audible, the scientific frontiers of agentic AI research, along with the new Nova Act extension that brings AI agent development directly into your IDE.
Deep dives
Science in the age of foundation models: Foundation models show promise for scientific computing, but adoption lags behind language and vision applications. Amazon researchers reveal what’s needed: physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques.
Simplifying book discovery with ML-powered visual autocomplete suggestions: Audible's new ML-powered visual autocomplete system displays book covers as you type, reducing the number of steps to purchase. Learn how it's turning partial keystrokes into personalized book discoveries.
Scientific frontiers of agentic AI: Agentic AI presents new scientific frontiers, like deciding what language agents should speak and modeling agentic negotiations. In this analysis, Amazon Scholar Michael Kearns examines how researchers must address context sharing, privacy, and users' commonsense policies.
News and updates
Nova Act extension: Build and test AI agents without leaving your IDE: Introducing a new tool that helps developers build AI agents fast with Nova Act by bringing the entire agent development experience directly into the integrated developer environment (IDE). By integrating the experience into a single view, the tool enables more intuitive iteration, removes the need to constantly switch between different windows, and speeds up the overall development process and creative flow.
Conference roundup
- COLM 2025, October 7-10
- ICCV 2025, October 19-25
- INFORMS 2025, October 26-29
Featured publications
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Senior Operations Manager | Fulfillment Center Leadership | P&L Management | Lean Process Improvement
4dFascinating edition. The application of foundation models for scientific computing is a powerful concept. The need for "physical constraint satisfaction" and "uncertainty quantification" resonates deeply from an operations perspective, these are the same principles we apply to optimize complex supply chains and fulfillment logistics. The bridge from AI theory to robust, real world systems is where the true transformation happens.