We'll explore the evolution of machine learning from expert systems to shallow learners to and deep learners. We look at the types of algorithms and problems which are addressed by each of these areas. Explore what they mean by artificial intelligence and make a case for a new type of machine learning algorithm embodied in adaptive exploratory systems. The talk will then address how those systems would work in the future to include training sets and adaptation to events in real time. It will address the need for uncertainty quantification and talk to the need for better models and estimation techniques need to do truly predictive analytics.