Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
Customer-obsessed science
Research areas
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May 15, 20265 min readA new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy. -
May 14, 202616 min read -
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April 15, 20268 min read
Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ACL Findings 20232023Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora between English and other languages are available. We leverage parallel and non-parallel corpora to improve the pretrained multilingual language models’ cross-lingual
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IEEE ICIP 20232023Learning product similarity using distance metric learning from real world catalog needs to take care of large number of product categories and noisy labels. On one hand, large number of product categories makes online hard mining (OHM) less effective as hard triplets become sparse and thus difficult to find. On the other hand, the validity of the hard-triplets themselves is less certain in the case of
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IEEE ICIP 20232023People navigate a world that involves many different modalities and make decision on what they observe. Many of the classification problems that we face in the modern digital world are also multimodal in nature, where textual information on the web rarely occurs alone, and is often accompanied by images, sounds, or videos. The use of transformers in deep learning tasks has proven to be highly effective.
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IEEE RO-MAN 20232023This paper describes the development of algorithms that decide when to move, where to move, and how to look for people in a home environment. We introduce a design framework as a tool to guide the development of a social robot to proactively be with people for companionship and assistance in the home. Through a series of experiments ranging from simulations to longitudinal A/B studies, we demonstrate how
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KDD 2023 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2023This paper proposes a learning model of online ad auctions that allows for the following four key realistic characteristics of contemporary online auctions: (1) ad slots can have different values and click-through rates depending on users’ search queries, (2) the number and identity of competing advertisers are unobserved and change with each auction, (3) advertisers only receive partial, aggregated feedback
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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.
