Feedback loops in artificial intelligence (AI) are significant because they allow systems to learn from outcomes and improve their performance over time. These loops enable AI models to incorporate real-world data, refine their algorithms, and correct previous errors based on user interactions. As the AI receives continuous feedback, its accuracy and efficiency in making predictions or decisions increase. This iterative learning process is essential for enhancing functionalities, allowing AI to evolve based on user needs and environmental changes, leading to smarter applications and systems.