Customer-obsessed science
Research areas
-
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. -
August 21, 20269 min read -
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.
-
2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023Network interference, where observed outcomes are influenced by interaction with nearby units, is a fundamental issue in A/B testing and experimentation in social and economic networks. Clustered randomization is a frequently-used strategy that aims to prevent confounding by limiting interaction between treated and untreated units. We study a model of least-squares estimation under network interference,
-
NeurIPS 20232023A large body of NLP research has documented the ways gender biases manifest and amplify within large language models (LLMs), though this research has pre- dominantly operated within a gender binary-centric context. A growing body of work has identified the harmful limitations of this gender-exclusive framing; many LLMs cannot correctly and consistently refer to persons outside the gender binary, especially
-
NeurIPS 20232023Research on recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common
-
AAAI 20242023Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have proposed varieties of machine learning (ML) approaches to train Language Models (LMs) for toxic content detection. However, both their accuracy and transferability across datasets are limited. Recently, Large Language Models (LLMs)
-
2023 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)2023In this paper, we propose the first successful implementation of associated learning (AL) to automatic speech recognition (ASR). AL has been shown to provide better label noise robustness, faster training convergence, and flexibility on model complexity than back-propagation (BP) in classification tasks. However, extending the learning approach to autoregressive models such as ASR, where model outputs are
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.
