As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
How do you match AI warnings about future events with what actually happens? A KDD test-of-time award recognizes a 2014 approach that modern systems still need.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.
Customer-obsessed science
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February 2, 202610 min readEvery NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed. -
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January 8, 20264 min read
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December 29, 20256 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2024Amazon MemoryDB for Redis is a database service designed for 11 9s of durability with in-memory performance. In this paper, we describe the architecture of MemoryDB and how we leverage open-source Redis, a popular data structure store, to build an enterprise-grade cloud database. MemoryDB offloads durability concerns to a separate low-latency, durable transaction log service, allowing us to scale performance
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2024Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations. However, a major drawback of most object-centric models, including slot attention
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2024Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts, its exploration in multimodal settings remains underexplored. A recent work has sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces
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CHI 20242024The potential of Generative AI, especially Large Language Models (LLMs), to transform software development is remarkable. In this paper, we focus on one area in software development called “code migration”. We define code migration as the process of transitioning the language version of a code repository by converting both the source code and its dependencies. Carefully designing an effective human-AI partnership
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2024Analytic data systems typically use data layouts to improve the performance of scanning and filtering data. Common data layout techniques include single-column sort keys, compound sort keys, and more complex multidimensional data layouts such as the Z-order. An appropriately-selected data layout over a table, in combination with metadata such as zone maps, enables the system to skip irrele-vant data blocks
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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.
