An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
Unique end-of-arm tools with three-dimensional force sensors and innovative control algorithms enable robotic arms to “pick” items from and “stow” items in fabric storage pods.
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”
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
How Amazon is helping transform plastics through innovation in materials, recycling technology, sortation, and more.
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Research areas
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July 18, 2025Novel graph-based, adversarial, agentic method for generating training examples helps identify — and mitigate — "overrefusal".
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Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
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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2025Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-latency processing systems, making it impractical to deploy models with billions of parameters due to latency constraints. As a result, practitioners often prefer smaller models with millions
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2025Effective customer support requires domain-specific solutions tailored to users’ issues. However, LLMs like ChatGPT, while excelling in open-domain tasks, often face challenges such as hallucinations, lack of domain compliance, and generic solutions when applied to specialized contexts. RAG-based systems, designed to combine domain context from unstructured knowledge bases (KBs) with LLMs, often struggle
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WACV 2025 Workshop on Physical Retail in AI2025This paper investigates multi-modal large language models (MLLMs) for predicting product features from images, comparing fine-tuned versus proprietary models. We introduce two domain-specific benchmarks: (1) Inductive Bias vs. Image Evidence (IBIE) Benchmark, which evaluates MLLMs’ ability to distinguish between image-derived features and latent knowledge, and (2) Catalog-bench, which assesses feature prediction
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2025General vision-language models (VLMs) trained on web data struggle to understand and converse about real-world e-commerce product images. We propose a cost-efficient approach for collecting training data to train a generative VLM for e-commerce product images. The key idea is to leverage large-scale, loosely-coupled image-text pairs from e-commerce stores, use a pre-trained LLM to generate multi-modal instruction-following
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2025Automated construction of shopping cart from medical prescriptions is a vital prerequisite for scaling up online pharmaceutical services in emerging markets due to the high prevalence of paper prescriptions that are challenging for customers to interpret. We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solution for automated pharmacy cart construction comprising multiple
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.
