TY  - JOUR
AU  - Taddy, Matt
TI  - The Technological Elements of Artificial Intelligence
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 24301
PY  - 2018
Y2  - February 2018
DO  - 10.3386/w24301
UR  - http://www.nber.org/papers/w24301
L1  - http://www.nber.org/papers/w24301.pdf
N1  - Author contact info:
Matt Taddy
Amazon
E-Mail: mataddy@gmail.com
M1  - published as Matt Taddy. "The Technological Elements of Artificial Intelligence," in Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors, "The Economics of Artificial Intelligence: An Agenda" University of Chicago Press (2019)

M3  - presented at "Economics of Artificial Intelligence", September 13-14, 2017
AB  - 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.  

We 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.
ER  -