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.
PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facing agent actually has to do.
A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI workloads.
Customer-obsessed science
Research areas
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September 21, 202611 min readThree new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design. -
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August 21, 20269 min read
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July 30, 20268 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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SIGIR 20232023Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. ProQANS is a neural search approach that leverages unlabeled data
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CVPR 2023 Workshop on Biometrics2023Low quality capture and obstruction on fingers often re-sult in partially visible fingerprint images, which imposes challenge for fingerprint recognition. In this work, mo-tivated from the practical use cases, we first systemati-cally studied different types of partial occlusion. Specif-ically, two major types of partial occlusion, including six granular types, and the corresponding methods to simulate each
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IISE Expo 20232023Construction auditing is a vital step in designing new sites in fulfillment centers (FCs). The audits include inspection processes for newly launched buildings which ensure that the buildings meet workplace standards. In current practice the process of scheduling construction audits and prioritizing the checklists when launching new buildings is highly manual, and requires going over lengthy textual data
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SIGIR 20232023The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing re-search typically uses customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute
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ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models2023Despite their high levels of robustness, Contrastive Language-Image Models (CLIP) still require some form of downstream adaptation when applied to tasks sufficiently out-of-domain with respect to their training set. Recent methods propose light-weight adapters on the model features, primarily focused on the few-shot domain. All such approaches however, require per-task hyperparameter tuning which necessitates
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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.
