Video Above: Army Research Lab Advances AI to Land Drones on Tanks
By Kris Osborn – President & Editor-In-Chief, Warrior Maven
(Aberdeen Proving Ground, Md) Warfare in the future is likely to involve a dangerous and unpredictable mixture of air-sea-land-space-cyber weapons, strategies and methods of attack, creating a complex interwoven picture of variables likely to confuse even the most elite commanders.
Cutting Edge AI
This anticipated “mix” is a key reason why futurists and weapons developers are working to quickly evolve cutting edge applications of AI, so that vast and seemingly incomprehensible pools of data from disparate sources can be gathered, organized, analyzed and transmitted in real time to human decision makers.
In this respect, advanced algorithms can increasingly bounce incoming sensor and battlefield information off of a seemingly limitless database to draw comparisons, solve problems and make critical, time sensitive decisions for human commanders in war.
Many procedural tasks, such as finding moments of combat relevance amid hours of video feeds or ISR data, can be performed exponentially faster by AI-enabled computers. At the same time, there are certainly many traits, abilities and characteristics unique to human cognition and less able to be replicated or performed by machines. This apparent dichotomy is perhaps why the Pentagon and the military services are fast pursuing an integrated approach combining human faculties with advanced AI-enabled computer algorithms.
Human-machine interface, manned-unmanned teaming and AI-enabled “machine learning” are all terms referring to a series of cutting edge emerging technologies already redefining the future of warfare and introducing new tactics and concepts of operation.
Just how can a mathematically-oriented machine using advanced computer algorithms truly learn things? What about more subjective variables less digestible or analyzable to machines such as feeling, intuition or certain elements of human decision-making faculties. Can a machine integrate a wide range of otherwise disconnected variables and analyze them in relation to one another?
A drone without labeled data is referred to by ARL scientists as unsupervised learning, meaning it may not be able to “know” or contextualize what it is looking at. In effect, the data itself needs to be “tagged,” “labeled” and “identified” for the machine such that it can quickly integrate into its database as a point of reference for comparison and analysis.
“If you want AI to learn the difference between cats and dogs, you have to show it images…but I also need to tell it which images are cats and which images are dogs, so I have to “tag” that for the AI,” Dr. Nicholas Waytowich, Machine Learning Research Scientist with DEVCOM Army Research Laboratory, told Warrior in an interview. DEVCOM is part of Army Futures Command.
— Kris Osborn is the President and Editor-in-Chief of Warrior Maven and The Defense Editor of The National Interest ––
Kris Osborn is the defense editor for the National Interest and President of Warrior Maven -the Center for Military Modernization. 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 Masters Degree in Comparative Literature from Columbia University.
