Amazon Science

Ten years of NFL Next Gen Stats + the latest in reinforcement learning Find the latest news and research from Amazon's science community at Amazon Science. Overview of how Next Gen Stats uses data to make accurate predictions.

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

05.02.2026 21:36 — 👍 1    🔁 0    💬 0    📌 0

Institute for Assured Autonomy & Computer Science Seminar Series. Talk Trends in Safe and Reliable Reinforcement Learning. February 9, 2026, 1–2 p.m. Zoom. Alec Koppel, Johns Hopkins APL. Pratap Tokekar, UMD & Amazon.

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...

05.02.2026 20:14 — 👍 1    🔁 1    💬 0    📌 0

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

04.02.2026 22:15 — 👍 0    🔁 0    💬 0    📌 0

A decade of NFL Next Gen Stats innovation Every NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football ...

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:

02.02.2026 17:49 — 👍 1    🔁 1    💬 0    📌 0

A decade of NFL Next Gen Stats innovation Every NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football ...

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:

02.02.2026 17:49 — 👍 1    🔁 1    💬 0    📌 0

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

27.01.2026 23:20 — 👍 2    🔁 1    💬 0    📌 0

The unseen work of building reliable AI agents "Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.

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:

23.01.2026 19:29 — 👍 0    🔁 0    💬 0    📌 0

Customizing multiturn AI agents with reinforcement learning Leveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets.

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.

15.01.2026 18:27 — 👍 2    🔁 0    💬 0    📌 0

Fine-tuning vision-language models on memory-constrained devices A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques.

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

12.01.2026 20:33 — 👍 0    🔁 0    💬 0    📌 0

Why AI for good depends on good data New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.

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-...

14.10.2025 14:02 — 👍 10    🔁 5    💬 1    📌 0

Thanks, Danilo! We updated our username without the dash :)

08.01.2026 17:39 — 👍 1    🔁 0    💬 1    📌 0

The unseen work of building reliable AI agents "Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.

"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:

08.01.2026 17:27 — 👍 1    🔁 0    💬 0    📌 0