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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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. -
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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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NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following2023Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF) leverages human preference signals that are in the form of ranking of response pairs to perform this alignment. However, human preference on LLM outputs can come in much richer forms including natural language, which may provide
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NeurIPS 2023 Workshop on SyntheticData4ML2023Recently, diffusion models have demonstrated great potential for image synthesis due to their ability to generate high-quality synthetic data. However, when applied to sensitive data, privacy concerns have been raised about these models. In this paper, we evaluate the privacy risks of diffusion models through a membership inference (MI) attack, which aims to identify whether a target example is in the training
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NeurIPS 20232023We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In Gaussian bandits, we obtain O(cΔ log n) and O(ch log2n) bounds for an upper confidence bound algorithm, where ch and cΔ are constants depending on the prior distribution and the gaps of random bandit instances sampled from it, respectively. The latter bound asymptotically matches the lower bound of Lai (1987).
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NeurIPS 20232023Membership inference attacks are designed to determine, using black-box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis-testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model’s confidence on the true label) on points that were (and were not) used in
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NeurIPS 20232023We focus on the task of approximating the optimal value function in deep reinforcement learning. This iterative process is comprised of solving a sequence of optimization problems where the loss function changes per iteration. The common approach to solving this sequence of problems is to employ modern variants of the stochastic gradient descent algorithm such as Adam. These optimizers maintain their own
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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
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