Topic: mechanistic interpretability

NeurReps, Symmetry and Geometry in Neural Representations, is a NeurIPS workshop that brings together mathematicians, machine learning researchers and neuroscientists working to uncover the geometric principles shared by brains and machines. In neuroscience, mounting evidence suggests that neural circuits encode task and environmental structure through low-dimensional manifolds, conserved symmetries and structured transformations; in deep learning, sparsity, equivariance and compositionality are guiding the development of more generalisable and interpretable models. Following editions from 202

EMBO | EMBL Symposium bringing machine-learning and life-science researchers together across sequencing, imaging and structural biology. Topics include foundation models, cross-modality learning, causal explanation, mechanistic interpretability, generative models and AI agents. Onsite workshops and tutorials take place on the first day; the following three days offer hybrid talks and posters. Registration is not yet open; the official page accepts expressions of interest.

The Cognitive Computational Neuroscience (CCN) conference connects cognitive science, neuroscience and artificial intelligence through computational explanations of complex behaviour. Its scope includes mechanistic interpretability, representation learning and alignment, biological and artificial intelligence, neural dynamics, and models of perception, cognition and action. The primarily single-track format combines keynotes, research talks and posters with some parallel community sessions. Generative Adversarial Collaborations examine competing theories; keynote-and-tutorial sessions connect

UKRI and EPSRC are funding speculative, high-risk fundamental research with the potential to produce a step change in the explainability of future AI systems. The call welcomes work on mechanistic interpretability, output reasoning, process examination, imposed explainability, uncertainty quantification, and novel approaches beyond those examples.