An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
Unique end-of-arm tools with three-dimensional force sensors and innovative control algorithms enable robotic arms to “pick” items from and “stow” items in fabric storage pods.
In 2017, IEEE Internet Computing identified a single paper from its publication history that had best withstood the test of time: a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
How Amazon is helping transform plastics through innovation in materials, recycling technology, sortation, and more.
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Research areas
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July 18, 2025Novel graph-based, adversarial, agentic method for generating training examples helps identify — and mitigate — "overrefusal".
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Top eight university teams move on to head-to-head finals focused on AI security for code generation.
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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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2024We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algorithm that is agnostic to this parameter-space equivalence. Instead, the LR algorithm
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Query Auto-Completion (QAC) is a fundamental component of user search experience on e-commerce websites. It assists in finding userintended products, by automatically presenting search queries as users typing in the search bar. Traditional QAC systems build upon query popularity to suggest a list of potential completions, but they fall short for unforeseen search prefixes. A generative Large Language Model
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IEEE RO-MAN 20242024For social robots operating in home environments, identifying appropriate parking locations which are “out of the way” is a challenging and multi-faceted problem. This paper proposes a solution to one core aspect of that problem, specifically a model for estimating locations where the robot may block walking paths through narrow spaces. For generality, this model assumes no a priori knowledge about user
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2024Recently, several scholars have contributed to the growth of a new theoretical framework in NLP called perspectivism. This approach aims to leverage data annotated by different individuals to model diverse perspectives that affect their opinions on subjective phenomena such as irony. In this context, we propose MultiPICo, a multilingual perspectivist corpus of ironic short conversations in different languages
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Vision-language transformer models play a pivotal role in e-commerce product search. When using product description (e.g. product title) and product image pairs to train such models, there are often non-visual-descriptive text attributes in the product description, which makes the visual textual alignment challenging. We introduce MultiModal Learning with online Token Pruning (MML-TP). MML-TP leverages
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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.
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We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
