  In a landmark paper published in 2001, Leo Breiman described the tense
standoff between two cultures of data modeling: parametric statistical and
algorithmic machine learning. The cultural division between these two
statistical learning frameworks has been growing at a steady pace in recent
years. What is the way forward? It has become blatantly obvious that this
widening gap between "the two cultures" cannot be averted unless we find a way
to blend them into a coherent whole. This article presents a solution by
establishing a link between the two cultures. Through examples, we describe the
challenges and potential gains of this new integrated statistical thinking.
