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
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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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CIKM 20232023In conversational AI assistants, SLU models are part of a complex pipeline composed of several modules working in harmony. Hence, an update to the SLU model needs to ensure improvements not only in the model specific metrics but also in the overall conversational assistant. Specifically, the impact on user interaction quality metrics must be factored in, while integrating interactions with distal modules
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CIKM 2023 Industry Day2023We introduce Robust Training with Trust Scores (RT2S), a framework to train machine learning classifiers with potentially noisy labels. RT2S calculates a trust score for each training sample, which indicates the quality of its corresponding label. These trust scores are employed as sample weights during training and optionally during threshold optimization. The trust scores are generated from two sources
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CIKM 20232023Query reformulation (QR) is a widely used technique in web and product search. In QR, we map a poorly formed or low coverage user query to a few semantically similar queries that are rich in product coverage, thereby enabling effective targeted searches with less cognitive load on the user. Recent QR approaches based on generative language models are superior to informational retrieval-based methods but
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RecSys 20232023As E-commerce and subscription services scale, personalized recommender systems are often needed to further drive long term business growth in acquisition, engagement, and retention of customers. However, long-term metrics associated with these goals can require several months to mature. Additionally, deep personalization also demands a large volume of training data that take a long time to collect. These
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RecSys 20232023Modern recommender systems usually include separate recommendation carousels such as ‘trending now’ to list trending items and further boost their popularity, thereby attracting active users. Though widely useful, such ‘trending now’ carousels typically generate item lists based on simple heuristics, e.g., the number of interactions within a time interval, and therefore still leave much room for improvement
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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.
