The language AI agents might speak, sharing context without compromising privacy, modeling agentic negotiations, and understanding users’ commonsense policies are some of the open scientific questions that researchers in agentic AI will need to grapple with.
Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users.
With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups.
From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.
Amazon VP and distinguished scientist Byron Cook explains how AWS's new Automated Reasoning checks address key challenges in automated reasoning: translating natural to structured language, defining truth, and definitive reasoning.
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August 11, 2025Trained on millions of hours of data from Amazon fulfillment centers and sortation centers, Amazon’s new DeepFleet models predict future traffic patterns for fleets of mobile robots. -
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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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Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation. Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ideal for omnisupervised
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2024End-to-end (E2E) automatic speech recognition (ASR) systems often exploited pre-trained hidden Markov model (HMM) systems for word timing estimation (WTE), due to their inability to predict word boundaries. However, training an HMM is difficult for low-resource languages due to the lack of phonetic transcriptions, leading to a high demand for HMM-free WTE methods, particularly for multilingual ASR systems
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ACL Findings 20242024Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative-AI applications often require multiple responses, and how to perform speculative decoding in a batched setting while preserving its latency benefits poses non-trivial challenges. This
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ACM SIGCOMM 20242024The primary objective of adaptive bitrate (ABR) streaming is to enhance users’ quality of experience (QoE) by dynamically adjust-ing the video bitrate in response to changing network conditions. However, users often find frequent bitrate switching frustrating due to the resulting inconsistency in visual quality over time, es-pecially during live streaming when buffer lengths are short. In this paper, we
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HFES 20242024Traditional tools for ergonomic assessments of workstation designs often involve ergonomists using Digital Human Modeling (DHM) software to simulate worker motions. However, these tools can be limited by posture prediction algorithms that fail to capture the full range and variability of human behavior. Virtual Reality (VR) offers an alternative by enabling workers to perform tasks within simulated workspaces
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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.
