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
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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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ICLR 20232023This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations of a small number of other time series. To mitigate the impact of such attack, we have
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ICLR 2023 Workshop on Deep Learning for Code (DL4C)2023Code understanding and generation require learning the mapping between human and programming languages. As human and programming languages are different in vocabulary, semantic, and, syntax, it is challenging for an autoregressive model to generate a sequence of tokens that is both semantically (i.e., carry the right meaning) and syntactically correct (i.e., in the right sequence order). Inspired by this
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Web Conference 2023 Workshop on Natural-Language Processing for Social Media2023Language model pre-training has led to state-of-the-art performance in text summarization. While a variety of pre-trained transformer models are available nowadays, they are mostly trained on documents. In this study we introduce self-supervised pre-training to enhance the BERT model’s semantic and structural understanding of dialog texts from social media. We also propose a semisupervised teacher-student
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ICASSP 20232023This work focuses on modelling a speaker’s accent that does not have a dedicated text-to-speech (TTS) frontend, includ-ing a grapheme-to-phoneme (G2P) module. Prior work on modelling accents assumes a phonetic transcription is avail-able for the target accent, which might not be the case for low-resource, regional accents. In our work, we propose an approach whereby we first augment the target accent data
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ECIR 20232023AI assistants are gradually becoming embedded in our lives, utilized for everyday tasks like shopping or music. In addition to the everyday utilization of AI assistants, many users engage them with playful shopping requests, gauging their ability to understand – or simply seeking amusement. However, these requests are often not being responded to in the same playful manner, causing dissatisfaction and even
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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.
