Customer-obsessed science
Research areas
-
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 -
-
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.
-
EMNLP 2019 Workshop on DeepLo2019New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used
-
NeurIPS 20192019I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample regime. I first consider a multi-dimensional variable linearly influencing a target variable with some multi-dimensional unobserved common cause, where the confounding effect can be decreased by keep-ing the penalizing term in
-
ASRU 20192019Building a conversational speech recognition system for a new language is constrained by the availability of interaction style utterances. Data collection is often expensive and limited by the speed of manual transcription. In this work, we advocate the use of neural machine translation as a data augmentation technique for bootstrapping language models in factored speech recognition systems. Translation
-
EMNLP 2019 Workshop on TextGraphs2019Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph based semi-supervised learning models as well as their inductive variants for NLU. We evaluate
-
CCS 2019 12th ACM Workshop on Artificial Intelligence and Security2019Many forms of interaction between computer systems and users are recorded in the form of event records, such as login events, API call records, bank transaction records, etc. These records are often comprised of high-dimensional categorical variables, such as user name, zip code, autonomous system number, etc. In this work, we consider anomaly detection for such data sets, where each record consists of
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
