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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
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Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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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IAAI 20232023Detecting robotic traffc at scale on online ads needs an approach that is scalable, comprehensive, precise, and can rapidly respond to changing traffic patterns. In this paper we describe SLIDR or SLIce-Level Detection of Robots, a realtime deep neural network model trained with weak supervision to identify invalid clicks on online ads. We ensure fairness across different traffc slices by formulating a
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SLT 20222023Regional accents of the same language affect not only how words are pronounced (i.e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intonation. This paper investigates a novel flow-based approach to accent conversion using normalizing flows. The proposed approach revolves around three steps: remapping the phonetic conditioning, to better match the target accent,
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AAAI 2023 Workshop on Artificial Intelligence Safety2023Gradient boosting decision trees (GBDTs) are widely applied on tabular data in real-world ML systems. Quantifying uncertainty in GBDT models is thus essential for decision making and for avoiding costly mistakes to ensure an interpretable and safe deployment of tree-based models. Recently, Bayesian ensemble of GBDT models is used to measure uncertainty by leveraging an algorithm called stochastic gradient
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WSDM 20232023Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, the majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in non-attributed directed graphs. Specifically, given a directed graph and query node as input, the goal is to
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AAAI 20232023Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical posteriors have been argued to be one of the main drivers of performance. In this work we revisit Gaussian variational posteriors for latent-action RL and show that they can yield
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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.
