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
-
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 -
-
April 15, 20268 min read
Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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.
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.
-
VLDB 20232023Information Extraction (IE) from semi-structured web-pages is a long studied problem. Training a model for this extraction task requires a large number of human-labeled samples. Prior works have proposed transferable models to improve the label-efficiency of this training process. Extraction performance of transferable models however, depends on the size of their fine-tuning corpus. This holds true for
-
IEEE 2023 Workshop on Machine Learning for Signal Processing (MLSP)2023Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low signal-to-noise ratios (when anomaly signatures are provably undetectable), and non-uniform performance (when average metrics are not representative of local
-
Interspeech 20232023We propose a methodology for information aggregation from the various transformer layer outputs of a generic speech Encoder (e.g. WavLM, HuBERT) for the downstream task of Speech Emotion Recognition (SER). The proposed methodology significantly reduces the dependency of model predictions on linguistic content, while leading to competitive performance without requiring costly Encoder re-training. The proposed
-
ACL 2023 Workshop on Trustworthy Natural Language Processing (TrustNLP)2023The issue of enhancing the robustness of Named Entity Recognition (NER) models against adversarial attacks has recently gained significant attention (Simoncini and Spanakis, 2021; Lin et al., 2021). The existing techniques for robustifying NER models rely on exhaustive perturbation of the input training data to generate adversarial examples, often resulting in adversarial examples that are not semantically
-
RecSys 2023 Industry Talk2023Personalization plays a critical role in helping customers discover the products and contents they prefer for e-commerce stores. Personalized recommendations differ in contents, target customers, and UI. However, they require a common core capability - the ability to deeply understand customers’ preferences and shopping intents. In this paper, we introduce the MCM (Multi-task pre-trained Customer Model)
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
