Gemma Scope is an open-source, multi-scale, high-throughput microscope system that combines brightfield, fluorescence, and confocal microscopy, designed for imaging large samples like brain tissue.
DeepMind's Gemma Scope provides researchers with tools to better understand how Gemma 2 language models work through a collection of sparse autoencoders. This helps in understanding the inner workings of these models and addressing concerns like hallucinations and potential manipulation.
This post discusses a study that finds that refusal behavior in language models is mediated by a single direction in the residual stream of the model. The study presents an intervention that bypasses refusal by ablating this direction, and shows that adding in this direction induces refusal. The study is part of a scholars program and provides more details in a forthcoming paper.
An article discussing the importance of explainability in machine learning and the challenges posed by neural networks. It highlights the difficulties in understanding the decision-making process of complex models and the need for more transparency in AI development.
