A new approach to reducing carbon emissions reveals previously hidden emission “hotspots” within value chains, helping organizations make more detailed and dynamic decisions about their future carbon footprints.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
"Kaputt" features 238,000+ images that advance the state of the art in detecting visual defects across complex retail applications.
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September 26, 2025To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor. -
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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NeurIPS 20232023This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to
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NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following2023Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF) leverages human preference signals that are in the form of ranking of response pairs to perform this alignment. However, human preference on LLM outputs can come in much richer forms including natural language, which may provide
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NeurIPS 2023 Workshop on SyntheticData4ML2023Recently, diffusion models have demonstrated great potential for image synthesis due to their ability to generate high-quality synthetic data. However, when applied to sensitive data, privacy concerns have been raised about these models. In this paper, we evaluate the privacy risks of diffusion models through a membership inference (MI) attack, which aims to identify whether a target example is in the training
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NeurIPS 20232023We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In Gaussian bandits, we obtain O(cΔ log n) and O(ch log2n) bounds for an upper confidence bound algorithm, where ch and cΔ are constants depending on the prior distribution and the gaps of random bandit instances sampled from it, respectively. The latter bound asymptotically matches the lower bound of Lai (1987).
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NeurIPS 20232023Membership inference attacks are designed to determine, using black-box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis-testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model’s confidence on the true label) on points that were (and were not) used in
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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.
