The evolution of machine learning: past, present, and future

Abstract The earliest computers were designed to perform complex calculations, and their architecture allowed for the storage of not only data but also the instructions as to how to manipulate that data. This evolved to the point where the computer processed data according to a structure model of the real world, expressible in mathematical terms. The computer did not learn but was merely following instructions. The next step was to create a set of instructions that would allow the computer to learn from experience, i.e., to extract its own rules from large amounts of data and use those rules for classification and prediction. This was the beginning of machine learning and has led to the field that is collectively defined as artificial intelligence (AI). A major breakthrough came with the implementation of algorithms that were loosely modeled on brain architecture, with multiple interconnecting units sharing weighted puts among them, organized in computational layers (deep learning). AI has already revolutionized many aspects of modern life and is finding application in biomedical research and clinical practice at an accelerating rate.