Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.
Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads — demonstrating that systems can be provably correct, not just probably correct.
Customer-obsessed science
Research areas
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October 29, 20265 min readTwo optimization techniques, when combined, can significantly improve a model’s speed and cost effectiveness without sacrificing quality. -
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October 20, 20263 min read
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October 12, 202614 min read
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Featured news
The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Submissions open October 1 and close on November 11, 2026
The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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.
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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WACV 2023 Workshop on Pretraining Large Vision and Multimodal Models2023Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multimodal neural networks. However, existing large-scale video-text datasets and mining techniques suffer from several limitations, such as the scarcity of aligned data, the lack of diversity in the data, and the difficulty of collecting
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Interspeech 20232023Neural transducer ASR models achieve state of the art accuracy on many tasks, however rare word recognition poses a particular challenge as models often fail to recognise words that occur rarely, or not at all, in the training data. Methods of contextual biasing, where models are dynamically adapted to bias their outputs towards a given list of relevant words and phrases, have been shown to be effective
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Interspeech 20232023Conformer is an extension of transformer-based neural ASR models whose fundamental component is the self-attention module. In this paper, we show that we can remove the self-attention module from Conformer and achieve the same or even better recognition performance for utterances whose length is up to around 10 seconds. This is particularly important for streaming interactive voice assistants as input is
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Interspeech 20232023Contextual biasing (CB) is an effective approach for contextualising hidden features of neural transducer ASR models to improve rare word recognition. CB relies on relatively large quantities of relevant human annotated natural speech during training, limiting its effectiveness in low-resource scenarios. In this work, we propose a novel approach that reduces the reliance on real speech by using synthesised
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Interspeech 20232023Voice assistant accessibility is generally overlooked as today’s spoken dialogue systems are trained on huge corpora to help them understand the ‘average’ user. This raises frustrating barriers for certain user groups as their speech shifts from the average. People with dementia pause more frequently mid-sentence for example, and people with hearing impairments may mispronounce words learned post-diagnosis
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.
