The US Army, in partnership with US Air Force and US Navy, is announcing its first international tri-service competition, xTechGlobal Artificial Intelligence (AI) Challenge. The xTechGlobal AI Challenge aims to engage with eligible international small to medium enterprises in the US Army Combat Capabilities Development Command (DEVCOM) Atlantic Area of Responsibility (AOR), including Europe, the Middle East and Africa (EMEA), across the broad spectrum of science and technology to identify capabilities that provide robust, AI-enabled capabilities to manage, integrate, and process disparate data/information sources for rapid decision making.
The US Army Futures Command (AFC)-DEVCOM Atlantic has partnered with Assistant Secretary of the Army (Acquisition, Logistics, and Technology) (ASA(ALT)), the US Air Force’s AFWERX program and the US Navy Office of Naval Research-Global (ONR-G) to deliver this tri-service competition to an international audience for the first time. The services recognize that the US Department of Defense (DoD) must enhance engagements with eligible international small to medium enterprises, by: (1) understanding the spectrum of ‘world-class’ technologies being developed commercially that may benefit the DoD; (2) integrating the sector of small business innovators into the DoD Science and Technology (S&T) ecosystem; and (3) providing mentorship and expertise to accelerate, mature, and transition technologies of interest to the DoD.
Topic: AI-enabled Multi-modal Analytics in Resource Constrained Environments
Challenge: Our world is facing a rapid proliferation of data–with more and more data generated from an ever-growing number of data sources. With all of this data comes the promise of better, faster decision making but only if we can properly collect, process, interpret and present that data to those making decisions. The DoD is seeking robust, AI-enabled capabilities to manage, integrate, and process, and reason on disparate data/information sources for rapid decision making. This is particularly challenging due to severe constraints of resources such as computing power and bandwidth at the point of need. Of particular interest are the following AI/Machine Learning (ML) algorithms and software tools to enable data analytic capabilities in resource-constrained environments:
- AI-enabled algorithms and services to rapidly assess availability of data and information sources and match the most relevant sources to end users based on user needs and available resources.
- Multi-modal reinforcement learning algorithms and AI-enabled analytic software tools for disparate data types (e.g., video, over-the-air signals, passive sensor data, and open-source multimedia)