As the Department of Defense (DoD) looks to exploit and scale Artificial Intelligence (AI) capabilities across the warfighting domains, the Army plans to integrate advanced features into many of its combat systems. The benefits of cloud technologies offer promising solutions to these needs. While cloud-based AI-enabled capabilities leverage flexibility, common interfaces, and virtually infinite scale of resources, they suffer from their lack of proximity to the tactical edge. Tactical AI-enabled systems cannot reliably leverage advantages provided by cloud resources due to limited standardized practices for integration of on-premise/edge systems required by the deployed military. Future high-intensity conflict will be fought in a degraded, denied, intermittent, and lowbandwidth (DDIL) digital environment. As a result, tactical AI-enabled systems will be required to operate in a scenario where high speed, reliable cloud access is unavailable. This paper proposes a hybrid-cloud architecture that leverages resources of the cloud, when available, while also maintaining the capability to retrain tactical AI models in the field environment, using on-site computation and storage. The hybrid cloud construct consists of tactical cloud nodes that reside in closer proximity to AI-enabled systems at the edge. They may retain connectivity to the enterprise cloud yet have the ability to provide the common AI development platform and tool sets to support continuous integration, delivery, and deployment. Thus, its ultimate objective is to enable the seamless and expeditious operation of a distributed AI development environment for the Army and DoD that bridges the tactical edge and enterprise cloud.
KEYWORDS: Artificial intelligence, Data storage, Defense and security, Machine learning, Software development, Algorithm development, Information technology, Data processing
Artificial Intelligence’s (AI) potential to augment or auto-mate human decision making has caught the attention of many within the Department of Defense (DoD). Effective AI implementations, originating from well-scoped use cases, require the alignment of people, processes, and technologies. While many focus on the opportunities that these systems provide for leap ahead improvements to the future state of DoD business processes and warfighting capabilities, the challenges that must be overcome must not be under-estimated and expectations should be man-aged appropriately with an understanding that achieving the desired end state will be a long term endeavor. This paper provides a brief overview of some of the challenges that must be overcome in order to realize the opportunities presented by AI systems.
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