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.
Trained 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.
Customer-obsessed science
Research areas
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August 8, 2025A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
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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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In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this
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CVPR 2024 Workshop on "What is Next in Multimodal Foundation Models?"2024This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs ex-cel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs’ consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show
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CVPR 2024 Workshop on Computer Vision for Fashion, Art, and Design2024Virtual try-on and product personalization have become increasingly important in modern online shopping, high-lighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions
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The context of modern smart voice assistants are often multi-modal, where images, audio and video content are consumed by users simultaneously. In such a setup, co-reference resolution is especially challenging, and runs across modalities and dialogue turns. We explore the problem of multi-modal co-reference resolution in multi-turn dialogues and quantify the performance of multi-modal LLMs on a specially
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E-commerce faces persistent challenges with data quality issue of product listings. Recent advances in Large Language Models (LLMs) offer a promising avenue for automated product listing enrichment. However, LLMs are prone to hallucinations, which we define as the generation of content that is unfaithful to the source input. This poses significant risks in customer-facing applications. Hallucination detection
Academia
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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.
