This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
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.
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.
