How mechanism design theory helps optimize Amazon-vendor collaboration
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
Cloud & Big Tech 398 stories
Building trust into AI
Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
Preserving the privacy of AI training data
How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
How catastrophic is your LLM?
A new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations.
Isabelle/HOL: The proof assistant behind the Nitro Isolation Engine
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
Customized Amazon Nova models improve molecular-property prediction in drug discovery
A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.
AWS and Hopkins Engineering announce groundbreaking database for AI/ML antibody design
Built in collaboration with the Gray Lab at Johns Hopkins Whiting School of Engineering, the Antibody Developability Benchmark is powered by one of the most diverse…
How Amazon uses agentic AI for vulnerability detection at global scale
Amazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods.
Verifying and optimizing post-quantum cryptography at Amazon
How automated reasoning reconciles the demands of security, performance, and maintainability.
Improving quality and robustness in LLM-based text-to-speech systems
Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
Formally verified AES-XTS: The first AES algorithm to join s2n-bignum
Simplifying and clarifying the assembly code for core operations enabled automated optimization and verification.
Optimizing LoRA target module selection for efficient fine tuning
Ablation study clarifies trade-offs between accuracy and efficiency when using low-rank adaptation (LoRA) to fine-tune AI models.
How agentic AI helps heal the systems we can’t replace
By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
Designing AI agents that know when to step back
As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.