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
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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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Recsys 2022 Workshop on Multi-Objective Recommender Systems2022Recommendations systems play a central role in improving customer experience on the Amazon retail website. Commonly, Learning-to-Rank (LTR) methods are employed to rank content, however these methods are subject to bias inherent in the observational data that they use for training. This paper studies a domain-specific self-selection bias, called Content Targeting Bias, introduced when content is generated
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Recsys 2022 Workshop on Knowledge-aware and Conversational Recommender Systems2022Amazon Customer Service (CS) provides real-time support for millions of customer contacts every year. While bot-resolver helps automate some traffic, we still see high demand for human agents, also called subject matter experts (SMEs). Customers outreach with questions in different domains (return policy, device troubleshooting, etc.). Depending on their training, not all SMEs are eligible to handle all
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IEEE International Conference on Software Maintenance and Evolution2022Automatic identification of coding best practices can scale the development of code and application analyzers. We present Doc2BP, a deep learning tool to identify coding best practices in software documentation. Natural language descriptions are mapped to an informative embedding space, optimized under the dual objectives of binary and few shot classification. The binary objective powers general classification
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ESEC/FSE 20222022As software development teams adopt DevSecOps practices, application security is increasingly the responsibility of development teams, who are required to set up their own Static Application Security Testing (SAST) infrastructure. Since development teams often do not have the necessary infrastructure and expertise to set up a custom SAST solution, there is an increased need for cloud-based SAST platforms
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COLING 20222022In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information COtraining
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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.
