Working Paper: NBER ID: w24301
Authors: Matt Taddy
Abstract: We have seen in the past decade 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. \n\nWe will define a framework for thinking about the ingredients of this new ML-driven AI. Having an understanding of the pieces that make up 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 remove ambiguity from 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.
Keywords: No keywords provided
JEL Codes: C01; C1; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| advancements in machine learning algorithms (C45) | AI systems (C45) |
| AI systems (C45) | efficiency in business processes (M11) |
| domain knowledge (D80) | AI performance (C45) |
| advancements in machine learning algorithms (C45) | AI performance (C45) |