Hamed Karimi wins ECML PKDD Test-of-Time Award
Amazon senior applied scientist and coauthors showed that an overlooked mathematical condition from 1963 proves why frequently used machine-learning optimization methods work well.
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Speeding up language models for travel applications
Two optimization techniques, when combined, can significantly improve a model’s speed and cost effectiveness without sacrificing quality.
Better speech synthesis for audiobooks
“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent.
The science behind TorchNeuron, Amazon’s new PyTorch backend for AWS Trainium chips
Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.
Strengthening AI safety evaluation through independent third-party review
How Amazon partnered with AVERI to independently assess — and improve — our internal safety testing of Amazon models.
Can we change an AI model’s architecture without retraining it from scratch?
“Priming” reuses pretrained Transformer weights to build models with more efficient architectures at less than 1% of the original training cost and with throughputs up to twice as high.
Graph-centric agentic intelligence
Augmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures.
A kernel-centric path to real-time video generation on Trainium
Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Amazon launches research initiative with Stanford University to advance AI and science
The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Advancing AI for biology: Teaching models to design and characterize antibodies
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.