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.
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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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AISTATS 20242024The synthetic control method (SCM) has become a popular tool for estimating causal effects in policy evaluation, where a single treated unit is observed. However, SCM faces challenges in accurately predicting postintervention potential outcomes had, contrary to fact, the treatment been withheld, when the pre-intervention period is short or the post-intervention period is long. To address these issues, we
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2024Despite being widely spoken, dialectal variants of languages are frequently considered low in resources due to lack of writing standards and orthographic inconsistencies. As a result, training natural language understanding (NLU) systems relies primarily on standard language resources leading to biased and inequitable NLU technology that underserves dialectal speakers. In this paper, we propose to address
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Transactions of the Association for Computational Linguistics (TACL)2024Answering factual questions from heterogenous sources, such as graphs and text, is a key capacity of intelligent systems. Current approaches either (i) perform question answering over text and structured sources as separate pipelines followed by a merge step or (ii) provide an early integration giving up the strengths of particular information sources. To solve this problem, we present "HumanIQ", a method
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2024In recent years, deep neural networks (DNNs) have become integral to many real-world applications. A pressing concern in these deployments pertains to their vulnerability to adversarial attacks. In this work, we focus on the transferability of adversarial examples in a real-world deployment setting involving both a cloud model and an edge model. The cloud model is a black-box victim model, while the edge
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2024Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a single unsupervised task is used for pre-training followed
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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.
