Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
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April 27, 20264 min readA new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations. -
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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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International Journal of Computer Vision2024Matching algorithms predict relationships between items in a collection. For example, in 1:1 face verification, a matching algorithm predicts whether two face images depict the same per-son. Accurately assessing the uncertainty of the error rates of such algorithms can be challenging when test data are dependent and error rates are low, two aspects that have been often over-looked in the literature. In
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EUSIPCO 20242024Loudspeaker array beamforming is a common sig-nal processing technique for acoustic directivity control and robust audio reproduction. Unlike their microphone counterpart, loudspeaker constraints are often heterogeneous due to arrayed transducers with varying operating ranges in frequency, acoustic-electrical sensitivity, efficiency, and directivity. This work pro-poses a frequency-regularization method
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2024Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task weights are dynamically adjusted based on their respective losses to prioritize difficult tasks. However, these algorithms face a great challenge whenever label noise is
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LREC-COLING 2024 Workshop on e-Commerce and NLP2024In the dynamic marketplace, vendors continuously seek innovative ideas for new products and ways to improve existing ones. These ideas can be uncovered by analyzing text data, such as product descriptions and customer reviews. However, the ever-increasing volume of text data poses a challenge in extracting meaningful insights. Therefore, this study addresses the challenge of extracting actionable insights
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KDD 2024 Workshop on GenAI Evaluation2024Large language models (LLMs) have achieved remarkable progress in recent years. These models have the capability to answer complex questions about medical disorders, their pathophysiology, etiology and corresponding interventions. However, when providing information about medical products and treatments, it is important to ensure that models respond reliably with factually correct information that adheres
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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.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
