Generative AI Errs Differently Than Classical AI

Generative AI models can produce errors in different categories compared to Classical AI models, including errors in input data, model training and fine-tuning, and output generation and consumption. Errors in input data can arise when there are variations not familiar to the model, while errors in models may occur due to problem formulation, wrong functional form, or overfitting. Errors in consumption can occur when models are used for tasks they are not specifically trained for, and Generative AI models can also experience hallucination errors, infringement errors, and obsolete responses.