Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised machine learning workflows can be deployed in existing pathology workflows to begin learning autonomously through exploration and without the need for extensive direction. Although still in its infancy, this type of machine learning, which more closely mirrors human intelligence, stands to add another exciting layer of innovation to computational pathology and accelerate the transition to autonomous pathologic tissue analysis.