Distinguishing generative AI from other AI models

Again, the critical distinction between discriminative and generative models lies in their objectives. Discriminative models aim to predict target outputs given input data. Classification algorithms, such as logistic regression or support vector machines, find decision boundaries in data to categorize inputs as belonging to one or more class. Neural networks learn input-output mappings by optimizing weights through backpropagation (or tracing back to resolve errors) to make accurate predictions. Advanced gradient boosting models, such as XGBoost or LightGBM, further enhance these discriminative models by employing decision trees and incorporating the principles of gradient boosting (or the strategic ensembling of models) to make highly accurate predictions.

Generative methods learn complex relationships through expansive training in order to generate new data sequences enabling many downstream applications. Effectively, these models...