AI Chronicles

Amazon is developing precise, sector-specific approaches to measuring decarbonization progress — starting with emissions per unit shipped.

Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a “miniature containment system”.

A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI workloads.

Splitting the “separation kernel” off from the rest of the Nitro security system and using only a subset of the Rust programming language to code it enabled its formal verification.

Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments.

The harnesses that mediate between models and tools in agentic systems are becoming their own performance bottleneck, but a few simple design principles can fix what ails them.

Automatically fact-checking long, AI-generated research reports poses new challenges — including benchmarking.

“Quasi-random” network topologies and new passive optical components called ShuffleBoxes make more-efficient flat networks as practical as traditional “fat-tree” networks.

Awardees represent more than 49 universities in 11 countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.

How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.

A 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.

By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.

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.

A new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations.

Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.

A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.

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 antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.

Amazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods.

How automated reasoning reconciles the demands of security, performance, and maintainability.

Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.

Simplifying and clarifying the assembly code for core operations enabled automated optimization and verification.

Ablation study clarifies trade-offs between accuracy and efficiency when using low-rank adaptation (LoRA) to fine-tune AI models.