Factors Drawing World war-III due to the evolution of Artificial Intelligence
Artificial intelligence is among the many hot technologies that promise to change the face of warfare for years to come. Articles abound that describe its possibilities and warn those who fall behind in the AI race. The Department of Defense has duly created the Joint Artificial Intelligence Center in the hopes of winning the AI battle. Visions exist of AI enabling autonomous systems to conduct missions, achieving sensor fusion, automating tasks, and making better, quicker decisions than humans. AI is improving rapidly and some day in the future those goals may be achieved. In the meantime, AI’s impact will be in the more mundane, dull, and monotonous tasks performed by our military in uncontested environments.
Artificial intelligence is a rapidly developing capability. Extensive research by academia and industry is resulting in shorter training time for systems and increasingly better results. AI is effective at certain tasks, such as image recognition, recommendation systems, and language translation. Many systems designed for these tasks are fielded today and producing very good results. In other areas, AI is very short of human-level achievement. Some of these areas include working with scenarios not seen previously by the AI; understanding the context of text (understanding sarcasm, for example) and objects; and multi-tasking (i.e., being able to solve problems of multiple type). Most AI systems today are trained to do one task, and to do so only under very specific circumstances. Unlike humans, they do not adapt well to new environments and new tasks.
AI’s Shortfalls for Military Applications
As the military looks to incorporate AI’s success in these tasks into its systems, some challenges must be acknowledged. The first is that developers need access to data. Many AI systems are trained using data that has been labeled by some expert system (e.g., labeling scenes that include an air defense battery), usually a human. Large datasets are often labeled by companies who employ manual methods. Obtaining this data and sharing it is a challenge, especially for an organization that prefers to classify data and restrict access to it. An example military dataset may be one with images produced by thermal-imaging systems and labeled by experts to describe the weapon systems found in the image, if any. Without sharing this with preprocessors and developers, an AI that uses that set effectively cannot be created. AI systems are also vulnerable to becoming very large (and thus slow), and consequently susceptible to “dimensionality issues.” For example, training a system to recognize images of every possible weapon system in existence would involve thousands of categories. Such systems will require an enormous amount of computing power and lots of dedicated time on those resources. And because we are training a model, the best model requires an infinite amount of these images to be completely accurate. That is something we cannot achieve. Furthermore, as we train these AI systems, we often attempt to force them to follow “human” rules such as the rules of grammar. However, humans often ignore these rules, which makes developing successful AI systems for things like sentiment analysis and speech recognition challenging. Finally, AI systems can work well in uncontested, controlled domains. However, research is demonstrating that under adversarial conditions, AI systems can easily be fooled, resulting in errors. Certainly, many DoD AI applications will operate in contested spaces, like the cyber domain, and thus, we should be wary of their results.
Ignoring the enemy’s efforts to defeat the AI systems that we may employ, there are limitations to these seemingly super-human models. An AI’s image-processing capability is not very robust when given images that are different from its training set—for example, images where lighting conditions are poor, that are at an obtuse angle, or that are partially obscured. Unless these types of images were in the training set, the model may struggle (or fail) to accurately identify the content. Chat bots that might aid our information-operations missions are limited to hundreds of words and thus cannot completely replace a humanwho can write pages at a time. Prediction systems, such as IBM’s Watson weather-prediction tool, struggle with dimensionality issues and the availability of input data due to the complexity of the systems they are trying to model. Research may solve some of these problems but few of them will be solved as quickly as predicted or desired.
The Path Forward for Military AI Usage
Artificial intelligence will certainly have a role in future military applications. It has many application areas where it will enhance productivity, reduce user workload, and operate more quickly than humans. Ongoing research will continue to improve its capability, explainability, and resilience. The military cannot ignore this technology. Even if we do not embrace it, certainly our opponents will, and we must be able to attack and defeat their AIs. However, we must resist the allure of this resurgent technology. Placing vulnerable AI systems in contested domains and making them responsible for critical decisions opens the opportunity for disastrous results. At this time, humans must remain responsible for key decisions.