Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a…
*=Authors contributed equally Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to prioritize their model debugging and improvement efforts. Through interviews with 13 ML practitioners at Apple, we found…
Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks.…
2024 was the year machine learning (ML) and artificial intelligence (AI) went mainstream, affecting peoples' lives in ways they never before could have.
Model weights: https://huggingface.co/Comfy-Org/Lens PR: https://github.com/Comfy-Org/ComfyUI/pull/14077 You'll need to git the merge pull request if you're…