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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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
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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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Voice conversion for Lombard speaking style with implicit and explicit acoustic feature conditioningInterspeech 2023 Workshop on Machine Learning Challenges for Hearing Aids2023Text-to-Speech (TTS) systems in Lombard speaking style can im-prove the overall intelligibility of speech, useful for hearing loss and noisy conditions. However, training those models requires a large amount of data and the Lombard effect is challenging to record due to speaker and noise variability and tiring recording conditions. Voice conversion (VC) has been shown to be a useful augmentation technique
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CIKM 20232023In e-commerce sites, customer questions on the product detail page express the customers’ information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey
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SIGIR 20232023Immersive technologies such as virtual reality (VR) and head-mounted displays (HMD) have seen increased adoption in recent years. In this work, we study two factors that influence users’ experience when shopping in VR through voice queries: (1) context alignment of the search environment and (2) the level of detail on the Search Engine Results Page (SERP). To this end, we developed a search system for VR
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SIGIR 20232023Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods in the literature primarily focus on disentangling multi-party conversations involving three or more speakers, which enables their models to explicitly or implicitly incorporate speaker-related feature signals while disentangling. Most existing models require a large amount of human
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ACL 20232023Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained lan-guage models on large datasets. However, such models can fail to produce output faithful to the input data, particularly on out-of-domain data. Sufficient annotated data is often not avail-able for specific domains, leading us to seek an unsupervised approach to improve
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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.
