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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CIDR 20242024Debugging a performance issue in databases is notoriously hard. Wouldn’t it be convenient if there exists an oracle or a co-pilot for every database system which users can query in natural language (NL) — ‘what’s wrong?’, or even better— ‘How to fix it?’. Large Language Models (LLMs), like ChatGPT, seem to be a natural surrogate to this oracle given their ability to answer a wide range of questions by efficiently
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EACL 20242024Large language models can accumulate incorrect or outdated knowledge as the real world evolves. Compared to typical solutions such as retraining, retrieval augmented generation, model editing offers an effective yet low cost solution to address this issue. However, existing model editing algorithms employ manual selection of edit layers, which requires prior domain knowledge or expensive architecturespecific
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EACL 20242024Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling tasks, such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models. Furthermore, there
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WSDM 20242024Anomaly detection on graphs focuses on identifying irregular patterns or anomalous nodes within graph-structured data, which deviate significantly from the norm. This domain gains paramount importance due to its wide applicability in various fields such as spam detection, anti-money laundering, and network security. In the application of anomaly detection on graphs, tackling the challenges posed by label
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2024Modern Automatic Speech Recognition (ASR) systems are evaluated with respect to Word Error Rate (WER). While WER is a useful metric for training and evaluation of speech models, it does not fully reflect the difference in semantics between predicted and ground truth transcriptions. In conversational voice assistants, the ability to sufficiently understand the semantic meaning of the user request is often
Collaborations
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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.
