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 23, 20266 min read“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent. -
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October 12, 202614 min read
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The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Submissions open October 1 and close on November 4, 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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NeurIPS 2020 Workshop on Human in the Loop Dialogue Systems2020Smart voice assistants have gained much popularity in the past years. People can leverage them to accomplish a variety of daily tasks nowadays. To provide great services and ensure satisfactory user experiences, it is crucial to continuously measure and monitor how the assistant performs. One metric for such purposes is called goal success rate (GSR), which measures how often the assistant successfully
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NeurIPS 2020 Machine Learning for Molecules Workshop2020Protein sequence modeling typically does not use randomized data augmentation procedures during training due to the unpredictable functional changes introduced by even simple sequence modifications. However, in this paper, we empirically explore a set of simple string manipulations, when fine-tuning semi-supervised protein models. We compare to the Tasks Assessing Protein Embeddings (TAPE) baseline models
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NeurIPS 2020 Workshop on Self-Supervised Learning2020Self-supervised representation learning has seen remarkable progress in the last few years. More recently, contrastive instance learning has shown impressive results compared to its supervised learning counterparts. However, even with the ever increased interest in contrastive instance learning, it is still largely unclear why these methods work so well. In this paper, we aim to unravel some of the mysteries
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NeurIPS 2020 Workshop on Deep Reinforcement Learning2020We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms. In tabular and linear approximation settings, the factored Markov decision process literature has shown exponential improvements in sample efficiency by leveraging factored environment
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COVID-19 knowledge graph: Accelerating information retrieval and discovery for scientific literatureAACL-IJCNLP 2020 Workshop on Integrating Structured Knowledge and Neural Networks for NLP (KNLP)2020The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID-19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations
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.
