The Rapid Rise of Neural Networks for Defense: A Cautionary Tale

Posted: April 03, 2019

No Longer in the Realm of Science Fiction, Artificial Intelligence Is Becoming Ubiquitous In The Civilian World As Well As the Military. But Are We Moving Too Quickly With a Technology We Still Don’t Fully Understand?

Fueled by rapid increases in computer storage capacity and processing power (principally through the use of graphical processing units) and the widespread availability of powerful software for designing and implementing neural networks (such as Google’s TensorFlow), the application of artificial neural networks to significant problems in the real world has seen tremendous growth over the past several years. During this time, neural networks have demonstrated successes on problems as varied as automatic recognition of handwritten digits, automated image captioning and indexing, and have even beaten human masters in the game of Go. In fact, the field has reached the state of maturity that a person with only casual knowledge of computer programming and equipped with a modest PC can implement a neural network for whatever problem he or she might have at hand.

Given this climate of success, there is growing interest in fielding neural networks in Department of Defense (DoD) systems. In this brief note, we will discuss the nature of neural networks in a language that can be broadly understood, especially in the context of the unique environment within DoD, so that Navy leaders can be better informed about the strengths and limitations of this technology as it impinges ever more frequently on the DoD. We will first attempt to explain to nonspecialists what an artificial neural network is. We will then discuss some of the inherent limitations of this class of machine learning tools and some of the ways that we and other members of the DoD are studying these limitations. Finally, we will discuss the consequences of these limitations.

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