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In 2016 a small research team at Amazon announced our presence to the world with the launch of the Automated Reasoning Group (ARG). Our vision was bold: use mathematical logic to not just test AWS systems but to prove, with mathematical certainty, that they work correctly. In the intervening decade, we’ve gone from exploring whether advances in formal verification could mitigate...

Modern LLM architectures have co-evolved within a single hardware family. The shapes of our attention mechanisms, the structure of our multilayer perceptrons (MLPs), the choice of numerical formats, and even the granularity of parallelism strategies have all been shaped by hardware constraints: warp sizes, tensor core geometries, memory hierarchies, and the kernel abstractions t...

Build on Trainium is a $110 million credit program focused on AI research and university education aimed to support the next generation of innovation and development on AWS Trainium. The program provides compute credits to novel AI research on Trainium, investing in leading academic teams to build innovations in critical areas including new model architectures, ML libraries, opt...

Training a machine learning model to handle multiple objectives simultaneously is a bit like trying to follow GPS directions to several destinations at once: the routes often conflict, and compromising between them can leave you farther from every destination. A paper we presented at this year’s International Conference on Machine Learning (ICML) addresses this problem in the co...

When a virtual patient in a benchmark scenario tells an AI agent they're feeling feverish, or hopeless, the difference between a safe response and an unsafe one often comes down to clinical design — not just medical knowledge. As healthcare AI shifts from answering questions to completing tasks on behalf of patients — scheduling visits, managing prescriptions, triaging symptoms ...