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
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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AutoML 20242024AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities
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Conference on the Foundations of Software Engineering (FSE) 20242024Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug localization and repair utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug locations, rely on line-level localization tools, or address bug prediction and fixing in one
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ECIR 20242024Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to
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ISCA 20242024Bosonic quantum computing, based on the infinite dimensional qumodes, has shown promise for various practical applications that are classically hard. However, the lack of compiler optimizations has hindered its full potential. This paper introduces Bosehedral, an efficient compiler optimization framework for (Gaussian) Boson sampling on Bosonic quantum hardware. Bosehedral overcomes the challenge of handling
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ICLR 2024 Tiny Papers2024NER (named entity recognition) model aims to recognize the named entities in the keywords. However, when the entities are extremely knowledge intensive, traditional NER model cannot encode all the knowledge in its parameters, thus fails to recognize those entities with high accuracy. In this paper, we propose retrieval augmented NER model (RA-NER) to address this issue. RA-NER retrieves the most relevant
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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.
