Can you use ML to ground on existing knowledge?
Machine learning (ML) is fundamentally centered on the concept of using data to automatically learn patterns, relationships, or rules without being explicitly programmed for every task. When considering whether ML can be used to "ground on existing knowledge," one is essentially asking whether ML systems can leverage, build upon, or integrate established bodies of knowledge—such
Are the algorithms and predictions based on the inputs from the human side?
The relationship between human-provided inputs and machine learning algorithms, particularly in the domain of natural language generation (NLG), is deeply interconnected. This interaction reflects the foundational principles of how machine learning models are trained, evaluated, and deployed, especially within platforms such as Google Cloud Machine Learning. To address the question, it is necessary to distinguish