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
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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Machine Learning for Healthcare 20242024Given a particular claim about a specific document, the fact checking problem is to determine if the claim is true and, if so, provide corroborating evidence. The problem is motivated by contexts where a document is too lengthy to quickly read and find an answer. This paper focuses on electronic health records, or a medical dossier, where a physician has a pointed claim to make about the record. Prior methods
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2024We present Event-focused Search, an automated and scalable pipeline designed to facilitate event discovery and enhance event-based search. This is done by leveraging large language models (LLMs) to populate event datasets, perform temporal search based on selected dates, and aggregate search results based on appropriate events based on those searches. We illustrate this pipeline through proof-of-concept
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2024NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models. It is designed to avoid catastrophic forgetting while achieving guaranteed convergence to an entropy-maximized closed-form optimal solution with reasonable modeling capacity. Despite the success, several challenges arise when apply NADO to a wide range of scenarios. Vanilla NADO suffers from
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CVPR 2024 Workshop on Open-Vocabulary 3D Scene Understanding2024Affordance grounding refers to the task of finding the area of an object with which one can interact. It is a fundamental but challenging task, as a successful solution requires the comprehensive understanding of a scene in multiple aspects including detection, localization, and recognition of objects with their parts, of geospatial configuration/layout of the scene, of 3D shapes and physics, as well as
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SIGIR 2024 Workshop on Generative Information Retrieval2024Large Language Models (LLMs) can be leveraged to improve performance in various stages of the search pipeline – the indexing stage, the query understanding stage, and the ranking or re-ranking stage. The latter two stages involve invoking a LLM during inference, adding latency in fetching the final ranked list of documents. Index enhancement, on the other hand, can be done in the indexing stage, in near
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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.
