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.
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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 Workshop on Artificial Intelligence-Enabled Cybersecurity Analytics2023Irrespective of the intent, malicious or benign, behind the origin of non-human traffic on sponsored advertising pages, failure to detect such unwanted traffic results in deterioration of advertiser performance metrics. Invalid (i.e., robotic) ad traffic is frequently driven by IP addresses (or address ranges) that are exclusively dedicated to VPNs, hosting or proxy services, Tor networks, as well as by
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SIGDIAL 20232023Automatic Evaluation (AE) and Response Selection (RS) models assign quality scores to various candidate responses and rank them in conversational setups. Prior response ranking research compares various models’ performance on synthetically generated test sets. In this work, we investigate the performance of model-based reference-free AE and RS models on our constructed response ranking datasets that mirror
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RecSys 20232023As spoken dialog systems like Siri, Alexa and Google Assistant become widespread, it becomes apparent that relying solely on global, one-size-fits-all models of Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Resolution (ER), is inadequate for delivering a friction-less customer experience. To address this issue, Query Reformulation (QR) has emerged as a crucial technique
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KDD 2023 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2023User activity sequence modeling has significantly improved performance across a range tasks in advertising spanning across supervised learning tasks like ad response prediction to unsupervised tasks like robot and ad fraud detection. Self-supervised learning using autoregressive generative models has garnered interest due to performance improvements on time series and natural language data. In this paper
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KDD 2023 Workshop on Artificial Intelligence-Enabled Cybersecurity Analytics2023Rapid growth of deep learning models in recent years for robot and fraud detection has led to significant improvement in precision and recall but has also created a challenge for explainability and trust in the model decisions. In this paper, we propose a scalable multitiered framework that generates explainable network request level signatures for crawler bots on a large e-commerce advertising program.
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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.
