Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.
A new approach to reducing carbon emissions reveals previously hidden emission “hotspots” within value chains, helping organizations make more detailed and dynamic decisions about their future carbon footprints.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models. -
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Featured news
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
David Chang/Getty Images/iStockphoto
Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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NeurIPS 2022 Workshop on Human in the Loop Learning2022Recommender systems (RecSys) often require user-behavioral data to learn good preference patterns. However, the user data is often collected by a working RecSys in the first place. This creates a gap where we hope to establish general recommendation patterns without relying on user data first, while the performance is then evaluated by real human oracles. On top of that, we aim to introduce diversity in
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NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)2022We propose KNN-Kmeans MT, a sample efficient algorithm that improves retrieval based augmentation performance in low resource settings by adding an additional K-means filtering layer after the KNN step. KNN-Kmeans MT like its predecessor retrieval augmented machine translation approaches (Khandelwal et al. [2020]) doesn’t require any additional training and outperforms the existing methods in low resource
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EMNLP 20222022Multi-modality support has become an integral part of creating a seamless user experience with modern voice assistants with smart displays. Users refer to images, video thumbnails, or the accompanying text descriptions on the screen through voice communication with AI powered devices. This raises the need to either augment existing commercial voice only dialogue systems with state-of-the-art multimodal
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NeurIPS 2022 Workshop on Self-Supervised Learning - Theory and Practice2022Localizing defects in products is a critical component of industrial pipelines in manufacturing, retail, and many other industries to ensure consistent delivery of the highest quality products. Automated anomaly localization systems leveraging computer vision have the potential to replace laborious and subjective manual inspection of products. Recently, there have been tremendous efforts in the domain of
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WACV 2023 Workshop on Pretraining Large Vision and Multimodal Models2022Self-supervised pretraining has advanced the capabilities of many computer vision tasks without requiring additional labels. One drawback is this technique requires extensive datasets and computational resources. This requirement of large datasets to pretrain with has often precluded the use of smaller, more niche datasets. Recently a method of pretraining has been developed that uses several stages of
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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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.
