A family of four foundation models designed to meet diverse enterprise needs across reasoning, multimodal processing, and real-time conversational AI.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
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.
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Research areas
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December 5, 20256 min readA multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-world inputs. -
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November 20, 20254 min read
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October 20, 20254 min read
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
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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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ECML-PKDD 20212021In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. We model
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ICML 2021 Workshop on Machine Learning for Data: Automated Creation, Privacy, Bias2021With the use of personal devices connected to the Internet for tasks such as searches and shopping becoming ubiquitous, ensuring the privacy of the users of such services has become a requirement in order to build and maintain customer trust. While text privatization methods exist, they require the existence of a trusted party that collects user data before applying a privatization method to preserve users
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IROS 20212021Localization is an essential module that supports many intelligent functions of a mobile robot such as transportation or inspection. However, justifying that a localization module is sufficiently accurate for supporting all downstream tasks is one of the most difficult questions to answer in practice. To overcome this problem, we move away from the traditional calculation of pose errors and propose a new
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KDD 2021 Workshop on Data-Efficient Machine Learning2021Virtual assistants enable users to interact with a large number of services in natural language. Third-party developers building new applications for virtual assistants often have limited annotation resources and find it challenging to procure large amounts of suitable training data, opting instead for limited collections of sample utterance templates, annotated with their semantics. We can enrich such
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Interspeech 2021 Workshop on Speech Synthesis (SSW11)2021We describe a heterophone homograph (simply ’homograph’ henceforth) disambiguation system based on per-case classifiers, trained on a small amount of labelled data. These classifiers use contextual word embeddings as input features and achieve state-of-the-art accuracy of 0.991 on the English homographs on a publicly available dataset, without any additional rule system being necessary. We show that as
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.
