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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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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IJCNLP-AACL 20232023Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer in conversations such as task-oriented dialogues, existing approaches suffer from these limitations as context can play an important role and the style attributes are often difficult to define
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IJCNLP-AACL 20232023Regulations introduced General Data Protection Regulation (GDPR) in the EU or California Consumer Privacy Act (CCPA) in the US have included provisions on the right to be forgotten that mandates industry applications to remove data related to an individual from their systems. In several real world industry applications that use Machine Learning to build models on user data, such mandates require significant
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IJCNLP-AACL 20232023Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level
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ICCV 2023 Workshop on Closing the Loop Between Vision and Language2023Traditional image-to-image and text-to-image search struggle with comprehending complex user intentions, particularly in fashion e-commerce, where users search for similar products with text modifications to a reference image. This paper introduces Progressive Vision-Language Alignment and Multimodal Fusion (ProVLA), a novel approach which utilizes a transformer-based vision and language model to generate
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IEEE 2023 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)2023A large scale music catalog contains diverse types of sound recordings. In this paper, we present a methodology to identify instrumental music with high precision and high recall. Our method starts by separating a recording into a vocals track and a background track. Then, we process the vocals track with a singing voice detection model, to estimate the amount of singing voice in a song. Finally, we analyze
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.
