As rapid advances in artificial intelligence (AI) continue to reshape the future of warfare, some question whether there are limits to its capacity when compared to human intelligence.
U.S. Army Research Lab (ARL) scientists continue to explore this question, pointing out that the limits of AI are still only beginning to emerge and are expected to yield new and unanticipated breakthroughs in coming years. Loosely speaking, the fundamental structure of how AI operates is analogous to the biological processing associated with the vision nerves of mammals. The processes through which signals and electrical impulses are transmitted through the brain of mammals conceptually mirror or align with how AI operates, senior ARL scientists explained. This means that a fundamental interpretive paradigm can be established, but also that scientists are now only beginning to scratch the surface of possibility when it comes to the kinds of performance characteristics AI might be able to replicate or even exceed.
For instance, could an advanced AI-capable computer be able to distinguish between a dance “ball” from a soccer “ball” in a sentence by analyzing the surrounding words to determine its context? This is precisely the kind of task AI is being developed to perform, essentially developing an ability to identify, organize, and integrate new incoming data not previously associated with its database.
Dr. Nicholas Waytowich, Army Research Laboratory, Army Futures Command, told The National Interest in an interview how humans can “interact” with the machines by offering timely input of great relevance to computerized decision-making, a dynamic which enables fast machine-learning and helps “tag” data.
“If you have millions and millions of data samples, well, you need a lot of effort to label that. So that is one of the reasons why that type of solution training AI doesn't scale the best, right? Because you need to spend a lot of human effort to label that data. Here, we're taking a different approach where we're trying to reduce the amount of data that it needs because we're not trying to learn everything beforehand. We're trying to get it to learn tasks that we want to do on the fly through just interacting with us,” Waytowich said.
Building upon this premise, industry and military developers are looking at ways through which AI-enabled machines can help perceive, understand, and organize more subjective phenomena such as intuition, personality, temperament, reasoning, speech, and other factors which inform human decision-making. The ineffable mix of variables informing human thought and decision-making is quite difficult to replicate, yet perhaps by recognizing speech patterns, behavior from history, or other influencers in relation to one another, machines can shed some light on subjective cognitive processes. Are there ways machines can learn to “tag” data autonomously? That is precisely what seems to be the point of the ARL initiatives. Their discoveries related to machine learning could lead to autonomous weapons platforms able to respond quickly and adjust in real-time to unanticipated developments with precision.
“If there's a new task that you want, we want the AI to be able to know, and understand what it needs to do in these new situations. But you know, AI isn't quite there yet. Right? It's brittle, it requires a lot of data…..and most of the time requires a team of engineers and computer scientists somewhere behind the scene, making sure it doesn't fail. What we want is to push that to where we can adapt it on the edge and just have the soldier be able to adapt that AI in the field,” Waytowich said.
Kris Osborn is the Defense Editor for the National Interest. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army—Acquisition, Logistics & Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Master's Degree in Comparative Literature from Columbia University.