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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April 27, 20264 min readA new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations. -
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April 7, 202613 min read
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Featured news
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.
Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is now May 13.
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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Journal of Physics Communications2024This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. We focus on the canonical problem of ball impact on laminated structures, which captures essential physics while maintaining computational tractability. Novel contributions include: (1) development of a temporal multi-resolution strategy for stable long-time
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Machine Learning for Health Symposium 20242024Generalist large language models (LLMs), not developed to do particular medical tasks, have achieved widespread use by the public. To avoid medical uses of these LLMs that have not been adequately tested and thus minimize any potential health risks, it is paramount that these models use adequate guardrails and safety measures. In this work, we propose a synthetic medical prompt generation method to evaluate
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ECIR 20252024Training sequential recommenders such as SASRec with uniform sample weights achieves good overall performance but can fall short on specific user groups. One such example is popularity bias, where mainstream users receive better recommendations than niche content viewers. To improve recommendation quality across diverse user groups, we explore three Distributionally Robust Optimization(DRO) methods: Group
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ASEE 20242024Many students choose to major in engineering to join the community of professional engineers and gain exposure to the field through their college experience [1]. However, research suggests that engineering graduates may not be adequately prepared for the workplace due to the complexities of engineering work [2]. Engineering work involves complexity, ambiguity, and contradictions [3], and developing innovation
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ICLR 2024 Workshop on AI4DifferentialEquations in Science2024Incomplete tabular datasets are ubiquitous in many applications for a number of reasons such as human error in data collection or privacy considerations. One would expect a natural solution for this is to utilize powerful generative models such as diffusion models, which have demonstrated great potential across image and continuous domains. However, vanilla diffusion models often exhibit sensitivity to
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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.
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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.
