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, 20259 min readTo 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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September 2, 20253 min read
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August 21, 20257 min read
Featured news
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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EMNLP 20232023Generative models have been widely applied to solve extractive tasks, where parts of the input are extracted to form the desired output, and have achieved significant success. For example, in extractive question answering (QA), generative models have constantly yielded state-of-the-art results. In this work, we study the issue of tokenization inconsistency that is commonly neglected in training these models
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Graph meets LLM: A novel approach to collaborative filtering for robust conversational understandingEMNLP 20232023A Personalized Query Rewriting system aims to reduce defective queries to ensure robust conversational functionality by considering individual user behavior and preferences. It’s usually structured as a search-based system, maintaining a user history index of past successful interactions with the conversational AI. However, this approach encounters challenges when dealing with unseen interactions, which
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EMNLP 20232023An effective approach to design automated Question Answering (QA) systems is to efficiently retrieve answers from pre-computed databases containing question/answer pairs. One of the main challenges to this design is the lack of training/testing data. Existing resources are limited in size and topics and either do not consider answers (question-question similarity only) or their quality in the annotation
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FSDM 20232023Adopting AI in financial advisory is a challenging task as there exist multiple sources of information to digest and interpret. Such information consumption processes are very lengthy for financial advisors, reducing the efficiency and timeliness for the advice and recommendation given to their clients. In this work, we introduce a multi-step framework that consumes and combines news and industry-focused
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ICCV 20232023Semi-supervised semantic segmentation methods use a small amount of clean pixel-level annotations to guide the interpretation of a larger quantity of unlabelled image data. The challenges of providing pixel-accurate annotations at scale mean that the labels are typically noisy, and this contaminates the final results. In this work, we propose an approach that is robust to label noise in the annotated data
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.
