Artificial Intelligence: Algorithms, Operational Environments and Hyperbole

Citation
MAJ Donald W. Griesmyer, Artificial Intelligence: Algorithms, Operational Environments and Hyperbole (2018)

Overview
In the past two decades, artificial intelligence (AI) gained a lot of attention and inspired innovation across many fields of science. U.S. military forecasters created numerous predictions of future operating environments with AI as a central feature. This paper reports on the historical trend of AI innovations leading to periods of high expectations for the emergence of a truly artificial general intelligence (AGI). These inflated expectations of continued innovation outpaced actual capabilities leading to disillusionment.

Artificial intelligence goes through cycles of new innovations, over expectations, and disillusionment followed by modest advancement. The cyclical nature of AI innovation follows cycles of extreme hyperbole which, in past cycles, resulted in a loss of funding and the slowing of future innovations. To avoid future disillusionment and loss of progress, seen in the cycle of hyperbole, leaders need a realistic understanding of machine learning technology and what it will mean for future AI development. This paper presents a functional framework for understanding artificial intelligence's interaction with the operational environment.