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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ACL 2020 Workshop on NLP for Medical Conversations2020Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain. ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalise awkward and explicit punctuation commands, such as “period”, “add comma” or “exclamation
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ACM L@S 20202020Online learning systems that provide actionable and personalized guidance can help learners make better decisions during learning. Bayesian Knowledge Tracing (BKT) extensions [2] and deep learning based approaches have demonstrated improved mastery prediction accuracy compared to the basic BKT model; however, neither set of models provides actionable guidance on learning activities beyond mastery prediction
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KDD 20202020Personalized real-time recommendation has had a profound impact on retail, media, entertainment and other industries. However, developing recommender systems for every use case is costly, time consuming, and resource-intensive. To fill this gap, we present a black-box recommender system that can adapt to a diverse set of scenarios without the need for manual tuning. We build on techniques that go beyond
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ACM L@S 20202020Online learning systems with open navigation allow learners to select the next learning activity in order to achieve desired mastery. To help learners make an informed choice regarding the next learning activity, we propose to represent and communicate the learner’s knowledge state as the average success rate in the course for each skill, rather than as the probability of correctly answering the next question
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CD-MAKE 20202020Transparent Machine Learning (ML) is often argued to increase trust into predictions of algorithms however the growth of new interpretability approaches is not accompanied by a growth in studies investigating how interaction of humans and Artificial Intelligence (AI) systems benefits from transparency. The right level of transparency can increase trust in an AI system, while inappropriate levels of transparency
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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.
