ABOUT THE SERIES
The ‘Models’ collection interrogates how to visualize and understand large language models and machine learning embedding spaces. I used these creatures as metaphors to better understand how to look at a collection of information that feels similar to nature in its unpredictability and unknowability. These forms are aspirational representations of how transparency and explainability for machine learning could work. They are multidimensional, but their inner folds and workings still are viewable. These pieces inhabit a space between two and three dimensions, where the viewer can see what they were, and are, and get a sense of how they might work.
ABOUT THE SERIES
The ‘Models’ collection interrogates how to visualize and understand large language models and machine learning embedding spaces. I used these creatures as metaphors to better understand how to look at a collection of information that feels similar to nature in its unpredictability and unknowability. These forms are aspirational representations of how transparency and explainability for machine learning could work. They are multidimensional, but their inner folds and workings still are viewable. These pieces inhabit a space between two and three dimensions, where the viewer can see what they were, and are, and get a sense of how they might work.
