In 2017, IEEE Internet Computing identified a single paper from its publication history that had best withstood the test of time: a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”
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How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.
Top eight university teams move on to head-to-head finals focused on AI security for code generation.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
A new multimodal foundation model that unifies speech and text processing in a single architecture, delivering frontier voice intelligence and industry-leading price performance.
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Research areas
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June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
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Top eight university teams move on to head-to-head finals focused on AI security for code generation.
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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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Physical Review E2024A hypergraph is a generalization of a graph that arises naturally when attribute-sharing among entities is considered. Compared to graphs, hypergraphs have the distinct advantage that they contain explicit communities and are more convenient to manipulate. An open problem in hypergraph research is how to accurately and efficiently calculate node distances on hypergraphs. Estimating node distances enables
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MMSP 20242024In live video streaming, the size of manifest grows linearly as the overall time duration of manifest increases in many scenarios. Such a behavior exists across streaming technologies, e.g. Dynamic Adaptive Streaming through HTTP (DASH), HTTP Live Streaming (HLS) and Microsoft Smooth Streaming (MSS). It introduces significant overhead for manifest generation on cloud services, download latency and network
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We present a novel approach to automatically generate task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, where the generation step integrates a hallucination pattern guidance module and a language style alignment module. Hallucination pattern guidance makes it possible to curate synthetic datasets covering the most important hallucination
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MERLIN: Multimodal & multilingual embedding for recommendations at large-scale via item associations2024Product recommendations incentivize customers to make multiunit purchases by surfacing relevant products, leading to lower cost per unit for e-commerce stores and lower prices for their customers. However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose
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NIST 2024 Workshop on Formal Methods within Certification Programs2024It appears that the winds-of-change are blowing for formal verification of cryptographic software and, furthermore, those winds are blowing in the same direction. We perceive progress on several fronts: - Researchers in the mathematics of cryptography now publish formal specifications and proofs of security properties as a matter of course in their papers using language like EasyCrypt[10]. - Formal (yet
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.
