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
-
September 2, 2025Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users. -
Featured news
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
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.
-
RecSys 20242024Improving search functionality poses challenges such as data scarcity for model training, metadata enrichment for comprehensive document indexing, and the labor-intensive manual annotation for evaluation. Traditionally, iterative methods relying on human annotators and customer feedback have been used. However, recent advancements in Large Language Models (LLMs) offer new solutions. This paper focuses on
-
E-commerce stores enable multilingual product discovery which require accurate product title translation. Multilingual large language models (LLMs) have shown promising capacity to perform machine translation tasks, and it can also enhance and translate product titles cross-lingually in one step. However, product title translation often requires more than just language conversion because titles are short
-
CIKM 2024 Workshop on Generative AI for E-commerce2024Large Language Models (LLMs) have been employed as crowd-sourced annotators to alleviate the burden of human labeling. However, the broader adoption of LLM-based automated labeling systems encounters two main challenges: 1) LLMs are prone to producing unexpected and unreliable predictions, and 2) no single LLM excels at all labeling tasks. To address these challenges, we first develop fast and effective
-
CIKM 2024 Workshop on Data-Centric AI, EMNLP 2024 Workshop on Multilingual Representation Learning2024Dense retrieval systems are commonly used for information retrieval (IR). They rely on learning text representations through an encoder and usually require supervised modeling via labelled data which can be costly to obtain or simply unavailable. In this study, we introduce a novel unsupervised text representation learning technique via instruction-tuning the pre-trained encoder-decoder large language models
-
ICDM 20242024In e-commerce recommender systems, providing product suggestions to customers that are often bought together, which is called “complementary recommendation,” not only improves customer experience but also boosts business impact. However, in practice, it is highly challenging to efficiently extract the complementary relations between the items due to noisy and low coverage of the co-purchased records in
Conferences
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
