Trained on millions of hours of data from Amazon fulfillment centers and sortation centers, Amazon’s new DeepFleet models predict future traffic patterns for fleets of mobile robots.
A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
Amazon VP and distinguished scientist Byron Cook explains how AWS's new Automated Reasoning checks address key challenges in automated reasoning: translating natural to structured language, defining truth, and definitive reasoning.
Using ensembles of agents to generate and refine interactions annotated with chains of thought improves performance on a battery of benchmarks by an average of 29%.
Language models inspired by specialized processing regions in the brain offer significant time and cost savings.
Customer-obsessed science
Research areas
-
July 29, 2025New cost-to-serve-software metric that accounts for the full software development lifecycle helps determine which software development innovations provide quantifiable value.
Featured news
Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
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.
-
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
-
ACM SIGSPATIAL 20242024Determining the precise location of customers is important for an efficient and reliable delivery experience, both for customers and delivery associates. Address text is a primary source of information provided by customers about their location. In this paper, we study the important and challenging task of matching free-form customer address text to determine if two addresses represent the same physical
-
Nature Communications2024While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance
-
2024We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challenge of centering the object within the robot’s camera view. Our method builds a visual language pose graph (VLPG) that functions as a spatial map of VL embeddings
-
Large language models (LLMs) can be prone to hallucinations —generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. In this work, we address several challenges for post-hoc hallucination detection in production settings. Our pipeline for hallucination detection entails: first, producing a confidence score representing the likelihood that a generated
Academia
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.
