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.
Customer-obsessed science
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
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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Interspeech 20212021We present a training scheme for streaming automatic speech recognition (ASR) based on recurrent neural network transducers (RNN-T) which allows the encoder network to learn to exploit context audio from a stream, using segmented or partially labeled sequences of the stream during training. We show that the use of context audio during training and inference can lead to word error rate reductions of more
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Interspeech 20212021Recent studies have shown that it may be possible to determine if a machine learning model was trained on a given data sample, using Membership Inference Attacks (MIA). In this paper we evaluate the vulnerability of state-of-the-art speech recognition models to MIA under black-box access. Using models trained with standard methods and public datasets, we demonstrate that without any knowledge of the target
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Interspeech 20212021Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. Even for the cues that are labeled as the same emotion category, the variability of associated expressions can be high depending on the elicitation context e.g., emotion elicited during improvised conversations vs. acted sessions with predefined scripts. In
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*SEM 20212021Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant
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Interspeech 20212021Automatically dubbed speech of a video involves: (i) segmenting the target sentences into phrases to reflect the speech-pause arrangement used by the original speaker, and (ii) adjusting the speaking rate of the synthetic voice at the phrase-level to match the exact timing of each corresponding source phrase. In this work, we investigate a post-segmentation approach to control the speaking rate of neural
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.
