Customer-obsessed science
Research areas
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks. -
July 30, 20268 min read -
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July 9, 202610 min read
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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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WSDM 20202020Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dχ-privacy designed to achieve geo-indistinguishability in location data. Our approach applies carefully calibrated noise to vector
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ASRU 2019, ICASSP 20202019Most recent neural semi-supervised learning algorithms rely on adding small perturbations to either the input vectors or their representations. These methods have been successful on computer vision tasks, as the images form a continuous manifold, but they are not appropriate for discrete inputs such as sentences. To adapt these methods to text input, we propose to decompose a neural network M into two components
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ASRU 20192019Building conversational speech recognition systems for new languages is constrained by the availability of utterances capturing user-device interactions. Data collection is expensive and limited by speed of manual transcription. In order to address this, we advocate the use of neural machine translation as a data augmentation technique for bootstrapping language models. Machine translation (MT) offers a
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NeurIPS 2019 Workshop on Conversational AI2019Many businesses and consumers are extending the capabilities of voice-based services such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri to create custom voice experiences (also known as skills). As the number of these experiences increases, a key problem is the discovery of skills that can be used to address a user’s request. In this paper, we focus on conversational skill discovery and
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NeurIPS 2019 Workshop on Bayesian Learning2019Search engine performance is usually good on head (high-frequency) queries due to the rich availability of historical behavioral signals on these queries. An over-reliance on past behavioral signals can potentially impact the performance on the tail (low-frequency) queries, where there is a lack of behavioral data. One way to address this issue is to reformulate a tail query into an appropriate head query
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.
