Many in the industrial and defense communities are expecting current artificial intelligence technologies (deep learning and deep neural networks) to solve a wide array of problems. Others are deeply concerned that adversaries investing heavily in these technologies will produce highly autonomous and adaptive weapons that will overmatch any known defenses. This reaction is not surprising given that deep neural networks and deep learning systems have been remarkably successful at tasks long believed to require high levels of (human) intelligence. These technologies are enjoying great success because of two enabling developments. The availability of large amounts of appropriately labeled training data and the continued growth in sheer computing power permit the decades-old neural network technologies to reach surprising performance levels. These success stories beg answers to questions about the limits of performance and potential. This paper describes artificial intelligence in its historical context of boom and bust cycles. The AI discipline has a 60-year record of heightened expectations fueled by remarkable achievement that were soon followed by disillusionment (“AI Wintersâ€�) when the technologies failed to generalize to wider application. The paper also develops parallels between the current deep neural network requirements for success and those of previous intelligent technologies that were once inspiring but have now been largely retired. Finally, deep neural network technologies have known limitations that should be publicized along with their success stories to frame and temper expectations. The paper promotes awareness of these limitations to foster a rational appreciation for potential. These artificial intelligence technologies can certainly contribute to advancing automated capabilities, but their contribution is not without limit, so careful planning and preparation should precede action.