From military practitioners to data scientists to scholars of war, there is a virtually unanimous expectation that artificial intelligence will influence the changing character of war in the future. But the precise ways in which it will do so and the specific operational roles AI technology will play are much less clear, especially as we look years and even decades into the future. Much of what we learn about that future will come from direct experimentation, which will yield lessons about the tools that will come to characterize future military operations—as well as the processes by which militaries develop them. The US Army’s XVIII Airborne Corps has recently undertaken an innovative effort to operationalize an AI-enabled software called the Maven Smart System. It represents a valuable case study, one of those direct experimentation programs from which we can extract important lessons.
In this episode of the MWI Podcast, John Amble is joined by Emmy Probasco and Igor Mikolic-Torreira to explore those lessons. Probasco is a senior fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where Mikolic-Torreira is the director of analysis. Together, they had direct access to observe the XVIII Airborne Corps’s work on the Maven Smart System. They describe the software, discussing the advantages it offers its users in operational contexts, and share their insights on its development and how it might serve as a model for the Army to acquire tailored, advanced AI tools in the future. You can also read about their research in a recent report published by CSET.
The MWI Podcast is produced through an endowment generously funded by the West Point Class of 1974. You can listen to this episode of the podcast below, and if you aren’t already subscribed, be sure to find it on Apple Podcasts, Stitcher, or your favorite podcast app so you don’t miss an episode. While you’re there, please take just a moment to leave the podcast a rating or give it a review!
Image credit: XVIII Airborne Corps & Fort Liberty (adapted by MWI)