AI for Proactive Action – Leveraging Data to Drive Predictions


On the technical front[1], there are many approaches to building predictive models: Bayesian methods, classical loss minimization, etc. and there are numerous flavours of predictive models: classic decision trees and numerous deep learning neural nets for example. However, these models frequently underperform more advanced model types, such as Deep Learning models. The three key types of approaches discussed here – Distillation, Categorization, and Prediction – are a convenient framework to consider where and how business challenges can be impacted by AI, and I hope readers find it useful in their own endeavours.