A new approach to reducing carbon emissions reveals previously hidden emission “hotspots” within value chains, helping organizations make more detailed and dynamic decisions about their future carbon footprints.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
"Kaputt" features 238,000+ images that advance the state of the art in detecting visual defects across complex retail applications.
Customer-obsessed science
Research areas
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September 26, 2025To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor. -
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
David Chang/Getty Images/iStockphoto
Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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ASEE 20242024Many students choose to major in engineering to join the community of professional engineers and gain exposure to the field through their college experience [1]. However, research suggests that engineering graduates may not be adequately prepared for the workplace due to the complexities of engineering work [2]. Engineering work involves complexity, ambiguity, and contradictions [3], and developing innovation
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Incomplete tabular datasets are ubiquitous in many applications for a number of reasons such as human error in data collection or privacy considerations. One would expect a natural solution for this is to utilize powerful generative models such as diffusion models, which have demonstrated great potential across image and continuous domains. However, vanilla diffusion models often exhibit sensitivity to
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IGARSS 20242024The ever-increasing demand for digital maps in various do-mains amplifies the importance of having accurate and up-to-date maps. To address this, the proposed system pervasively conflates large volume of sign detections recorded by a transportation fleet of vehicles into map database. Detected and geo-localized sign objects collected from the fleet over a time period are passed through a context-aware clustering
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IWSM 20242024In this paper several mathematical models for end-to-end network delay are derived, where exponential wait times at intermediate network routers are assumed. The feasibility of using these models to extract parameters related to the routers is investigated by performing closure tests using synthetic data generated from the models themselves. Data from an experimental test bed is used to compare the different
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2024Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for the QA models is still under-explored. In this work, we propose BMBI, an approach to mitigate the bias of
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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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
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
