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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Research areas
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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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April 1, 20265 min read
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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ICLR 2024 Workshop on LLM Agents, LREC-COLING 2024 Workshop on e-Commerce and NLP2024The context of modern smart voice assistants are often multi-modal, where images, audio and video content are consumed by users simultaneously. In such a setup, co-reference resolution is especially challenging, and runs across modalities and dialogue turns. We explore the problem of multi-modal co-reference resolution in multi-turn dialogues and quantify the performance of multi-modal LLMs on a specially
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EMNLP 2024 Workshop on e-Commerce and NLP2024E-commerce faces persistent challenges with data quality issue of product listings. Recent advances in Large Language Models (LLMs) offer a promising avenue for automated product listing enrichment. However, LLMs are prone to hallucinations, which we define as the generation of content that is unfaithful to the source input. This poses significant risks in customer-facing applications. Hallucination detection
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ACM Transactions on Recommender Systems2024Offline data-driven evaluation is considered a low-cost and more accessible alternative to the online empirical method of assessing the quality of recommender systems. Despite their popularity and effectiveness, most data-driven approaches are unsuitable for evaluating interactive recommender systems. In this paper, we attempt to address this issue by simulating the user interactions with the system as
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ANNSIM 20242024This paper proposes a novel integration of simulation, machine learning and mathematical optimization to design a delivery network of autonomous delivery vehicles (ADVs). To obtain the desired scalability, the model is pre-solved using k-means clustering to batch orders based on proximity, then Capacitated Vehicle Routing Problem (CVRP) and Facility Location (FL) models are used to minimize the ADVs’ total
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SAE World Congress 20242024This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD
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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.
