👉 Hu computing, also known as Human-in-the-Loop (HITL) or Human-in-the-Loop Machine Learning, is a collaborative approach in artificial intelligence where human input plays a critical role in the machine learning process. It involves humans actively participating in tasks such as data labeling, model validation, and decision-making to improve the accuracy, reliability, and ethical considerations of AI systems. By integrating human judgment and expertise, hu computing helps address the limitations of purely automated systems, especially in complex or ambiguous scenarios where human intuition and contextual understanding are essential. This method is particularly valuable in applications like natural language processing, image recognition, and autonomous systems, where human oversight ensures that AI models are not only technically proficient but also socially unbiased and aligned with human values.
