[Editor’s Note: In recent weeks, Mad Scientist Laboratory has featured a number of podcasts and associated blog posts exploring the democratization of Artificial Intelligence (AI) and its potential ramifications for Warfighters and the Operational Environment (OE). From generating better proposals from a broader array of defense contractors, exploring the future of warfare and OE trends, the convergence of neuroscience and AI, and the future of learning through emerging technologies — large language models (e.g., Open AI‘s ChatGPT) can augment how we learn, work, create, and — most importantly to the U.S. Army — compete and fight.Imagine a not-too-distant future when all of our Military Leaders (from platoon to echelons above corps) are able to harness the comprehensive thoughts and insights of the world’s military theorists and tacticians, from antiquity to the present, via a personal AI digital assistant — or as proclaimed Mad Scientist Juliane Gallina so eloquently stated — a “Patton in the Pocket.” Human-machine teaming has the potential to enable future Commanders to focus on the battle at hand with coup d’œil, or the “stroke of an eye,” maintaining situational awareness and processing inputs, generating potential courses of action, and down selecting the best way ahead — tailored to specific mission objectives and conditions at the bleeding edge of the fight — all at machine speed. Sustained Soldier overmatch indeed! The application of Soldier-enhancing human-machine teaming isn’t limited to tactical applications, however. In today’s episode of The Convergence podcast, we interview LtCol Joe Buffamante, USMC, about his experience in applying human-machine teaming to support Professional Military Education (PME), leveraging large language models as effective learning support tools, and establishing and maintaining trust in AI applications — Enjoy!] LtCol Joe Buffamante is a native of Great Valley, New York, and graduated from Miami (Ohio) University, receiving his commission in the United States Marine Corps in May 2003. Upon completion of The Basic School, he was designated an Armor Officer and graduated from the Armor Officer Basic Course in May 2004. He has commanded USMC units in combat tours to both Iraq and Afghanistan, and has served as a maneuver and fire support team instructor at 29 Palms, California. LtCol Buffamante assumed the duties as Chief of Readiness for Joint Task Force Civil Support (JTF-CS), Ft. Eustis, Virginia, in 2014, ultimately serving as the training chief for current operations where he was responsible for training all Joint Operations Center personnel. Following completion of this Joint assignment, LtCol Buffamante attended the Naval War College in Newport, Rhode Island, earning a Master’s Degree in Defense and Strategic Studies. He also attended the Maritime Advanced Warfighting School (MAWS) and received the additional MOS of 0505 (MAGTF Planner). LtCol Buffamante is currently a student at the United States Army War College (AWC) in Carlisle, Pennsylvania. Army Mad Scientist sat down with LtCol Buffamante to discuss his experience in applying human-machine teaming to support PME, leveraging large language models as effective learning support tools, and establishing and maintaining trust in AI applications. The following bullet points highlight key insights from our conversation: • As a student at the AWC, LtCol Buffamante, along with Dr. Billy Barry, used a large language model as a learning support tool to explore the effectiveness of human-machine teaming research, specifically the effectiveness of the system itself and in collaboration with a human vice a human alone.   • LtCol Buffamante employed the system to assist in answering research questions related to his coursework. He co