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 1, 202610 min readAugmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures. -
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August 21, 20269 min read
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July 30, 20268 min read
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July 29, 20266 min read
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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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MLSys 2021 Workshop on Neural Networks and Systems2021The recent emergence of demand for running Graph Neural Networks (GNNs) on giant real world graphs requires more scalable system designs. Due to the sparse and irregular connections a graph has, parallel GNN training encounters the problem of load imbalance among workers. In this paper, we show that previous techniques based on graph partitioning is insufficient to address the load imbalance caused by GNN
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ASRU 20212021RNN-T has received a lot of attention recently since it achieves state-of-art WER in automatic speech recognition. To run the RNN-T model in real-time on resource-limited edge devices, model compression is often required. However, typical compression methods are still challenging to apply to RNN-T. First, it takes a lot of fine-tuning time and computing resources (e.g., up to several weeks even with multiple
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NeurIPS 2021 Workshop on Databases and AI (DBAI)2021While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks. In this paper, we implement an efficient, data-programming technique that enriches training data with KB-derived context and improves
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NeurIPS 2021 Workshop on Efficient Natural Language and Speech Processing2021Pretraining and then finetuning of large language models is one of the commonly used approaches to achieve good performance in natural language processing (NLP) tasks. However most pre-trained models have large memory footprint and low inference speed. Deploying such large models to applications with latency constraint is challenging. In this work, we focus on accelerating the inference via conditional
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ICCV 20212021We introduce Video Transformer (VidTr) with separable attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatiotemporal information via stacked attentions and provide better performance with higher efficiency. We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels,
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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.
