Better speech synthesis for audiobooks
“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent.
Sources / Amazon Science
Amazon Science
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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.
Why don’t machine learning research agents overfit?
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
Developing provably correct Rust code with Verus
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
When LLM judges agree, should we believe them?
Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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