How does AI model training occur in Watson?

The process of AI model training in Watson involves utilizing historical data in conjunction with continuous learning techniques. This approach allows the model to build on previously gathered knowledge while also adapting to new information as it becomes available.

By leveraging historical data, Watson can identify patterns, trends, and insights that form the basis of its learning. This foundational data serves as a context and grounding for the model's understanding. Continuous learning techniques then enable the model to update its knowledge base with current, real-time data, allowing it to improve over time and remain relevant to current scenarios.

This combination of historical data and ongoing learning is crucial for developing AI systems that not only perform well initially but also evolve in response to changing conditions and new information. Such adaptability is essential in many applications of AI, where environments and data trends shift frequently.