Understanding Difficult Examples

Daniel and Sara discuss the concept of difficult examples in machine learning and the need to clean up datasets. They explore different techniques used to handle difficult examples and highlight the importance of treating them within the same framework. They also touch on the limitations of current techniques and the need for a more nuanced approach to different types of errors.