This is a Plain English Papers summary of a research paper called AI Black Box Breakthrough: Scientists Discover How Vision Models Actually "See" Images. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Overview
- Sparse Autoencoders (SAEs) extract interpretable features from Vision-Language Models (VLMs)
- SAEs discover monosemantic features representing single visual concepts
- Features show impressive disentanglement - activating for specific concepts across different contexts
- SAEs trained on CLIP models reveal specialized neurons for objects, patterns, and semantic concepts
- Method provides transparency into how VLMs process visual information
Plain English Explanation
When we look at an image, our brains break it down into meaningful components - like recognizing a cat, a chair, or the color blue. But AI systems that process images don't necessarily work this way. The inner workings of vision-language models like CLIP (which connects images ...
