Humans-on-the-Loop (HOTL) is an extension of HITL, which involves humans providing feedback to the AI system to improve its performance over time. HOTL is typically used when the AI system has reached a certain level of performance but still requires human feedback and intervention to continue improving. In HOTL, humans act as trainers or teachers for the AI, providing labeled data, correcting mistakes, and guiding the AI toward better outcomes.
HOTL is often used in autonomous vehicles, fraud detection, and medical diagnosis applications.
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