als

GoogleDeepMind collaborated with global scientific experts to evaluate an AI system that identified new targets for liver fibrosis and fresh approaches to ALS, digesting decades of research.

This paper proposes an end-to-end Conformer-based neural decoder for intracortical speech decoding from a participant with ALS, achieving a 23.80% character error rate without any external language model. It demonstrates that meaningful character-level decoding is possible in a fully end-to-end framework.

DeepMind's Co-Scientist AI tool bridges the expertise of two researchers from different biological fields to accelerate ALS research by generating testable hypotheses and identifying RNA-based mechanisms for potential therapies.