Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
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.
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.
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November 28, 20254 min readLarge language models are increasing the accuracy, reliability, and consistency of the product catalogue at scale. -
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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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AACL 20222022Task-oriented dialog systems deployed in real-world applications are often challenged by out-of-distribution queries. These systems should not only reliably detect utterances with unsupported intents (semantic shift), but also generalize to covariate shift (supported intents from unseen distributions). However, none of the existing benchmarks for open-world intent classification focus on the second aspect
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ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data2022Accurate and rich representation of roads in a map is critical for safe and efficient navigation experience. Often, open source road data is incomplete and manually adding roads is labor intensive and consequently expensive. In this paper, we propose RING-Net, an approach for Road INference from GPS trajectories using a deep image segmentation Network. Previous work on road inference is either focused on
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ACM SIGSPATIAL 20222022Classifying trip modalities, i.e. driving, walking, etc., from GPS trajectories is one of the fundamental tasks for urban mobility analytics. It can be used for efficient route planning, human activity recognition, and public transportation design where understanding the time and location of transitioning to different modalities may provide additional insights. Informally, given a GPS trajectory consisting
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NeurIPS 20222022We study semi-supervised learning (SSL) for vision transformers (ViT), an underexplored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we use a SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised fine-tuning, and finally semi-supervised finetuning. At the semi-supervised fine-tuning stage, we adopt an exponential
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AACL-IJCNLP 20222022Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum
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.
