tldr - powered by Generative AI

• Data-centric AI is becoming more widespread and systematic in its approach
• Consistent labeling of data is crucial for learning algorithms to work effectively
• Error analysis and engineering examples are important for structured data
• Data augmentation and noise examples can be useful for unstructured data
• Focusing on subsets of data can improve performance