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 into a sport where every movement is measured, modeled, and instantly analyzed.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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Hosted by Dr. Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2026Workflow automation is critical for reducing manual efforts in industries, yet existing pipelines fail to handle generative tasks like summarization and extraction without pre-built tools, forcing human intervention. While LLM-based agents offer solutions, their creation depends heavily on prompt engineering—a resource-intensive process often yielding sub-optimal results. Current automated approaches face
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Given an unfamiliar dataset without ground truth annotations or established taxonomies, how do we systematically discover meaningful patterns? Even with large language models providing initial categorization suggestions, it remains challenging to capture patterns and standardize them into consistent representations across unstructured data. This persistent challenge highlights the need for systematic discovery
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AAAI 2026 Workshop on the Bridge between Artificial Intelligence and Law2026Given the constant flux in the world of geopolitics, staying up to date and compliant with international trade issues is challenging. But exploring if LLMs can aid this task is a frontier hitherto unexplored in the LLM evaluation literature - primarily due to the lack of a dataset for benchmarking the capabilities of LLMs on questions regarding international trade subjects. To address this gap, we introduce
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HRI 20262026As autonomous social robots become more prevalent in home environments, they must decide where to position themselves within many different types of rooms or spaces, balancing accessibility with staying out of the way. This paper presents a machine learning approach to modeling user preferences for robot parking spots in the home using standard 2D occupancy maps. Our method learns spatial patterns from
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2026With recent advancements in video backbone architectures, combined with the remarkable achievements of large language models (LLMs), the analysis of long-form videos spanning tens of minutes has become both feasible and increasingly prevalent. However, the inherently redundant nature of video sequences poses significant challenges for contemporary state-of-the-art models. These challenges stem from two
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
