The submission opens October 1 and closes on November 12, 2025
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
Amazon vice president and distinguished engineer Marc Brooker explains how agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
Customer-obsessed science
Amazon's unique approach to research is characterized by its customer-obsessed science philosophy, which drives innovation in artificial intelligence, machine learning, and other cutting-edge technologies.
Research areas
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January 26, 20262 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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September 23, 20256 min readMachine learning
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Featured news
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
David Chang/Getty Images/iStockphoto
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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SIGIR-AP 20232023Counterfactual evaluation plays a crucial role in learning-to-rank problems, as it addresses the discrepancy between the data logging policy and the policy being evaluated, due to the presence of presentation bias. Existing counterfactual methods, which are based on the empirical risk minimization framework, aim to evaluate the ability of a ranking policy to produce optimal results for a single query using
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IJCNLP-AACL 20232023Prior work in the field of text summarization mostly focuses on generating summaries that are a sentence or two long. In this work, we introduce the task of abstractive short-phrase summarization (PhraseSumm), which aims at capturing the central theme of a document through a generated short phrase. We explore BART & T5-based neural summarization models, and measure their effectiveness for the task using
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ICDM 20232023There are several algorithms for measuring fairness of ML models. A fundamental assumption in these approaches is that the ground truth is fair or unbiased. In real-world datasets, however, the ground truth often contains data that is a result of historical and societal biases and discrimination. Models trained on these datasets will inherit and propagate the biases to the model outputs. We propose FAIRLABEL
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ASRU 20232023Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained information for better rescoring of ASR hypothesis. Discriminative training, directly optimizing the minimum word-errorrate (MWER) criterion typically improves rescoring. In this study, we propose and explore several discriminative
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NeurIPS 20232023This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification
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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.
