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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July 9, 202610 min read
Featured news
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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CloudCom 20202020Telecommunication networks, especially 5G, typify high performance network infrastructure with very high data rates and throughput. Traditionally, high data rates meant line rate in telecom networks. As link capacities increase to 100 Gbit/s, 64byte packet rates will need to reach 150 MPPS. Technologies that attempt to deliver this high packet rate take liberties with proprietary hardware and application-enhanced
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ICML 2020 Workshop on ML4MD2020We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model. We represent music as a sequence of edit events, each of which denotes either the addition or removal of a note—even a note previously generated by the model. During inference, we generate one edit event at a time using direct ancestral sampling. Our method allows the model to fix previous
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AIOPS 2020 International Workshop on Artificial Intelligence for IT Operations2020This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors). This extension to time
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IA^3 2020 10th Workshop2020Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large, containing hundreds of millions of nodes and several billions of edges. To tackle this challenge, we develop DistDGL, a system for training GNNs in a mini-batch fashion
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ACM Multimedia 20202020In recent times, video content encoded at High-Definition (HD) and Ultra-High-Definition (UHD) resolution dominates internet traffic. The significantly increased data rate and growing expectations of video quality from users create great challenges in video compression and quality assessment, especially for higher-resolution, higher-quality content. The development of robust video quality assessment metrics
Collaborations
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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.
