How does Watson incorporate continuous learning?

Watson incorporates continuous learning by adapting models based on new data inputs and patterns. This ability allows Watson to refine its algorithms and improve predictions in real-time as more data becomes available or as patterns change. Continuous learning is essential for maintaining the relevance and accuracy of models, especially in dynamic environments where user behavior and external factors can shift over time. By leveraging new information, Watson enhances its understanding and optimally processes data to deliver more accurate insights and results. This adaptability is a fundamental feature of advanced AI systems, enabling them to remain effective in various applications.