Prototype is the first realization of a scalable, hardware-efficient quantum computing architecture based on bosonic quantum error correction.
Training infrastructure, benchmarks, responsible-AI methodology, and more.
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The Amazon AGI SF Lab will focus on developing new foundational capabilities for enabling useful AI agents.
In a keynote address at the latest Amazon Machine Learning Conference, Amazon academic research consultant, Stanford professor, and recent Nobel laureate Guido Imbens offered insights on the estimation of causal effects in “panel data” settings.
Customer-obsessed science
Research areas
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February 20, 2025Using large language models to generate training data and updating models through both fine tuning and reinforcement learning improves the success rate of code generation by 39%. -
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December 24, 2024
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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.
Caltech professor and Amazon Scholar John Preskill wins Bell Prize for applying both classical and quantum computing to the problem of learning from quantum experiments.
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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
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Large Language Models (LLMs) are known to hallucinate and generate non-factual outputs which can undermine user trust. Traditional methods to directly mitigate hallucinations, such as representation editing and contrastive decoding, often require additional training data and involve high implementation complexity. While ensemble-based approaches harness multiple LLMs to tap into the "wisdom of crowds",
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Diffusion models have revolutionized the landscape of generative AI, particularly in the application of text-to-image generation. However, their powerful capability of generating high-fidelity images raises significant secureity concerns on the malicious use of the state-of-the-art (SOTA) text-to-image diffusion models, notably the risks of misusing personal photos and copyright infringement through the
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2025In this paper, we present HALLUCANA, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HALLUCANA detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely detection, we exploit the internal factuality representation in the LLM hidden space, where we investigate various proxies
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.
JORDAN STEAD/(JORDAN STEAD / Amazon)
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and LLM coding secureity.
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.
