The United States Army wants to develop a system that can be quickly integrated and deployed into its weaponized drone fleet to automatically Detect, Recognize, Classify, Identify (DRCI) and target enemy combatants and vehicles using artificial intelligence (AI). This is an impressive leap forward, whereas humans still operate current military drones, this technology could foster a new era of autonomous drones conducting operations in hybrid wars — without human oversight.

The project is called "Automatic Target Recognition of Personnel and Vehicles from an Unmanned Aerial System Using Learning Algorithms," — a very original name, which the details were recently released on the Small Business Technology Transfer (STTR)website. In other words, the Department of Defense (DoD) via the Army is requesting private and research institutions that have developed image targeting AI platforms to form partnerships with them for the eventual technology transfer.

Once the technology transfer is complete, these drones will use machine-learning algorithms, such as neural networks blended with artificial intelligence to create the ultimate militarization of AI. Currently, military drones have little onboard intelligence, besides sending a downlink of high definition video to a military analyst who manually decides whom to kill.

Here is the program's objective:

"Develop a system that can be integrated and deployed in a class 1 or class 2 Unmanned Aerial System (UAS) to automatically Detect, Recognize, Classify, Identify (DRCI) and target personnel and ground platforms or other targets of interest. The system should implement learning algorithms that provide operational flexibility by allowing the target set and DRCI taxonomy to be quickly adjusted and to operate in different environments."

A full description of the program:

"The use of UASs in military applications is an area of increasing interest and growth. This coupled with the ongoing resurgence in the research, development, and implementation of different types of learning algorithms such as Artificial Neural Networks (ANNs) provide the potential to develop small, rugged, low cost, and flexible systems capable of Automatic Target Recognition (ATR) and other DRCI capabilities that can be integrated in class 1 or class 2 UASs. Implementation of a solution is expected to potentially require independent development in the areas of sensors, communication systems, and algorithms for DRCI and data integration. Additional development in the areas of payload integration and Human-Machine Interface (HMI) may be required to develop a complete system solution. One of the desired characteristics of the system is to use the flexibility afforded by the learning algorithms to allow for the quick adjustment of the target set or the taxonomy of the target set DRCI categories or classes. This could allow for the expansion of the system into a Homeland Security environment. "