Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
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.
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
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Research areas
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models. -
October 2, 20253 min read -
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September 2, 20253 min read
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
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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 2023 Workshop on Instruction Tuning and Instruction Following2023In recent years, the field of natural language processing (NLP) has witnessed remarkable advancements driven by the development of large language models (LLMs). Various techniques, such as instruction tuning, have emerged as crucial approaches, enhancing LLMs’ adaptability to new tasks guided by instructional prompts. Meanwhile, the phenomenon of memorization within LLMs has garnered considerable attention
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EMNLP 20232023Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship. A key step in significance testing is the estimation of confidence interval which is a function of sample variance. Sample variance calculation is straightforward when evaluating against ground truth
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NeurIPS 2023 Workshop on Self-Supervised Learning — Theory and Practice2023Pre-training has been an important ingredient in developing strong monocular depth estimation models in recent years. For instance, self-supervised learning (SSL) is particularly effective by alleviating the need for large datasets with dense ground-truth depth maps. However, despite these improvements, our study reveals that the later layers of the SOTA SSL method are actually suboptimal. By examining
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2023 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)2023To boost training and adaptation of end to end (E2E) automatic speech recognition (ASR) models, several approaches to use paired speech-text input together with unpaired text input have emerged. They aim at improving the model performance on rare words, personalisation, and long tail. In this work, we present a systematic study of the impact of such training/adaptation and compare it to training with synthetic
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EMNLP 20232023While recent studies have looked into the abilities of large language models in various benchmark tasks, few studies have looked into the controllability of large language models on generation tasks. We present a systematic and extensive analysis of the controllability of large language models on ten benchmarks, including a new simple yet challenging numerical planning benchmark with different granularities
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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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.
