Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
Customer-obsessed science
Research areas
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May 15, 20265 min readA new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy. -
May 14, 202616 min read -
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April 15, 20268 min read
Featured news
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is now May 13.
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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KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023Ensembles have long been a cornerstone in improving performance by integrating black-box base learners. The primary approach, “stacked generalization” is inherently static, lacking adaptability to changes in input data post-training. This limitation is more pronounced in time series forecasting, where these methods struggle to manage temporal correlations or non-stationarity. Given base learners may perform
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ICML 2023 Workshop on Interactive Learning with Implicit Human Feedback2023In this work, we show how to collect and use human feedback to improve complex models in information retrieval systems. Human feedback often improves model performance, yet little has been shown to combine human feedback and model tuning in an end-to-end setup with public resources. To this end, we develop a system called Crowd-Coachable Retriever (CCR),1 where we use crowd-sourced workers and open-source
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KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023Predictive maintenance (PdM) is one of the most important machine learning (ML) use cases in a wide range of businesses that own or manufacture machinery. Many PdM applications utilize event logs/sequences that record machine operating conditions and health data. However, industrial scale PdM use cases bring a variety of challenges which makes it difficult to directly apply state-of-the-art models. In this
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KDD 2023 Workshop on Online and Adaptive Recommender Systems (OARS)2023Rogue actors employ sophisticated automation techniques to mimic human browsing/click patterns and generate invalid (i.e., fraudulent or robotic) traffic on retail marketplaces to artificially inflate their key performance metrics at the expense of their legitimate competitors. To maintain a clean and fair advertising system, it is essential to identify and mitigate ad traffic that is invalid, i.e., fraudulent
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KDD 2023 Workshop on Machine Learning in Finance (MLF)2023Subledgers maintain detailed information about specific accounts or transactions in order to substantiate the general ledger. Subledgers provide a granular level of detail for financial reporting and analysis, which is especially essential for accounts receivables and payables. The size of subledgers can vary greatly depending on the complexity and volume of transactions and their size can also increase
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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.
