Definition
Feedback mechanisms allow AI systems to learn from their interactions with users or environments, refining future predictions or actions. This includes reinforcement learning signals, human-in-the-loop corrections, or automated error monitoring. By analyzing discrepancies between expected and actual outcomes, models can update their parameters or decision logic, leading to enhanced accuracy and adaptability over time in dynamic settings.
Summary
Feedback involves using output results to adjust and improve the performance of an AI model or system iteratively.
Key Concepts
- Reinforcement Learning
- Human-in-the-loop
- Error Correction
- Iterative Improvement
Use Cases
- Recommendation system tuning
- Chatbot conversation refinement
- Robotics control adjustment