The Technological Elements of Artificial Intelligence

In the past decade there has been a sharp increase in the extent that companies use data to optimize their businesses. Variously called the "Big Data" or "Data Science" revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of an Artificial Intelligence (AI) that works by combining many ML algorithms together—each targeting a straightforward prediction task—to solve complex problems.
We will define a framework for thinking about the ingredients of this new ML-driven AI. Understanding the components of these systems and how they fit together is important for those who will be building businesses around this technology. Those studying the economics of AI can use these definitions to clarify the conversation on AI's projected productivity impacts and data requirements. Finally, this framework should help clarify the role for AI in the practice of modern business analytics and economic measurement.