🏈 How AWS changed the game with machine learning and what's next in agentic AI. The latest from Amazon Science:

Join us and @johnshopkinsiaa.bsky.social on Monday for a joint talk on trends in safe and reliable reinforcement learning, featuring @jhuapl.bsky.social’s Alec Koppel and @univofmaryland.bsky.social & Amazon Robotics’ Pratap Tokekar. Learn more here: www.cs.jhu.edu/event/iaa-cs...

Amazon and @stanford.edu researchers collaborated to develop cvc5, an open-source software tool that powers Automated Reasoning checks in Amazon Bedrock and other AWS services. The tool now processes ~1B solver calls daily to enhance security for customers: https://amzn.to/3OlTTry

The NFL introduced machine learning to football with Next Gen Stats, transforming how the game is measured. Learn how the league went from basic box scores to producing up to 1,000 stats per play in 10 years:

The NFL introduced machine learning to football with Next Gen Stats, transforming how the game is measured. Learn how the league went from basic box scores to producing up to 1,000 stats per play in 10 years:

Thanks to everyone who contributed to a productive @aaai.org conference in Singapore. Our team enjoyed the conversations with researchers and practitioners advancing AI. See you next year! #AAAI2026

Before an AI agent can book your vacation, it must learn to scroll, click, tab, and navigate other low-level tasks. Amazon's AGI Lab is building "reinforcement learning gyms" where agents practice atomic behaviors, mastering mundane interactions that underpin reliable software operation:

Reinforcement learning boosts AI agent task success two- to fourfold with small training sets. AWS research shows smaller models can match larger proprietary models at 1% to 2% the cost.

SharpZO enables edge AI fine-tuning using only forward passes. The approach achieves 7% higher accuracy than existing low-memory methods and converges in as little as one-tenth the time: https://amzn.to/4pLQek8

No data, no AI, no progress. My @AmazonScience article explores how multi-layered mapping + petabyte-scale cloud infrastructure helps save lives in time of crisis. Building AI without addressing the fundamental data divide means solving the wrong problems. amazon.science/blog/why-ai-...

"Normcore agents" are trained by Amazon's AGI Lab to chain together hundreds of micro-interactions to execute customer requests. In reinforcement learning gyms, agents practice atomic behaviors across dozens of application domains, learning to execute complex workflows with near-perfect reliability: