What is Ground Truth in AI?
AI Summary
Summary of Video: Let Me Tell You the Truth About Ground Truth Data
- Importance of Ground Truth Data: Evaluates AI model performance by comparing AI answers to correct ones in the ground truth data.
- Supervised Learning Models: Used in image recognition, predictive analytics, spam detection, etc. Learn through labeled training data.
- Training Data Essentials: Ground truth data includes labels that describe each data component. For example, training an AI model to recognize cats would involve:
- Data: Images of cats.
- Labels: Features such as eyes, ears, and whiskers labeled correctly.
- Consequences of Incorrect Labels: Incorrect labels (e.g., dog paws labeled as cat paws) result in the model failing to learn correctly, leading to false predictions.