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.
Translating from natural to structured language, defining truth, and definitive reasoning remain topics of central concern in automated reasoning, but Amazon Web Services’ new Automated Reasoning checks help address all of them.
The language AI agents might speak, sharing context without compromising privacy, modeling agentic negotiations, and understanding users’ commonsense policies are some of the open scientific questions that researchers in agentic AI will need to grapple with.
A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.
Customer-obsessed science
Research areas
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September 2, 2025Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users. -
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Amazon Research Awards have opened their Fall call for proposals, which closes November 5, 2025.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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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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AI-ML Systems 20242024While we can customize large language models (LLMs) on specific domains by finetuning using the domain specific labeled data, performance of the customized models is highly dependent on the quality of the labeled data. Obtaining high-quality labeled data for custom domains often requires considerable human effort and associated costs. However, in many cases, unlabeled data is readily available at little
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2024Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a one-time completely random selection of the data. However, by employing tailored sampling and estimation strategies, one can obtain more precise estimates and reduce annotation costs. In this paper, we propose a statistical framework
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2024Bin-packing is an important problem in the robotic warehouse domain. Traditionally, this problem has been studied only for rigid packages (e.g., boxes or rigid objects). In this work, we tackle the problem of bin-packing with deformable packages that have become a popular choice for fulfillment needs. We present a system that incorporates a dual robot arm bimanual setup, uniquely combining suction and sweeping
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AI-ML Systems 20242024We present Video-to-Product Ads curation system for MiniTV to identify visually relevant products ads corresponding to objects of interest in video. This retrieval task is significantly challenging due to domain gap and peculiarity in images extracted from videos. Traditionally, images to product retrieval problems are solved using contrastive models with extensive labelled image data. In this paper, we
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ACM SIGSPATIAL 20242024A de-duplicated and complete address catalog is essential for any application or business which needs to manage large volumes of address data such as delivery logistics, first-responder services and government databases. For catalog creation, address data is usually procured from disparate sources, which often vary in quality, coverage, and introduce duplicates or variations of the same physical address
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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.
