Mr. Shane Shaneman Profile

Shane Shaneman

Strategic Director, National Security & Defense

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Proceedings Article | 6 June 2022 Presentation + Paper

Scaling distributed artificial intelligence/machine learning for decision dominance in all-domain operations

Shane Shaneman, Jemin George, Carl Busart

Proceedings Volume 12113, 1211306 (2022) https://doi.org/10.1117/12.2621199

KEYWORDS: Artificial intelligence, Data modeling, Data communications, Computer architecture, Evolutionary algorithms, Sensors, Data processing, Network architectures, Data storage, Machine learning, Decision support systems

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The pace of innovation in Artificial Intelligence (AI) is completely unprecedented, enabling AI to augment humans and increase productivity and efficiency for critical tasks and operations across the Department of Defense. AI is envisioned to support Command and Control in Joint All-Domain Operations by significantly enhancing situational awareness from heterogenous platforms, systems, and sensors deployed across multiple operational domains, and enabling more rapid and improved decision-making. To achieve these benefits, new AI architectures and capabilities are rapidly evolving and being developed that are transforming the AI landscape – with core functions and technology layers of the AI Stack being distributed between the enterprise, the edge, and embedded on-platform. This concept paper will analyze and compare centralized vs. distributed AI architectures in support of all-domain operations and explore key attributes and capabilities to directly impact the resiliency and adaptability of the AI, and its ability to provide insights and decision-support at a speed and scale of relevance to and to converge effects across all warfighting domains to overwhelm the adversary and present them with multiple dilemmas. By overcoming the traditional dependency of Centralized AI architectures on human supervision to aggregated and engineer data for algorithmic processing, Distributed AI can drastically accelerate AI processing and integrate AI capabilities and insights from the enterprise to the edge of the battlefield that will maximize mission effectiveness, reduce risk, and save lives.

Proceedings Article | 4 May 2018 Paper

The AI stack: a blueprint for developing and deploying artificial intelligence

Andrew Moore, Martial Hebert, Shane Shaneman

Proceedings Volume 10635, 106350C (2018) https://doi.org/10.1117/12.2309483

KEYWORDS: Artificial intelligence, Machine learning, Sensors, Defense and security, Machine vision, Visual process modeling

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This paper provides an abstract technology model called the AI Stack for the development and deployment of Artificial Intelligence, and the strategic investment in research, technology, and organizational resources required to achieve asymmetric capability. Over the past five years, there has been a drastic acceleration in the development of artificial intelligence fueled by exponential increases in computational power and machine learning. This has resulted in corporations, institutions, and nation-states vastly accelerating their investment in AI to (a) perceive and synthesize massive amounts of data, (b) understand the contextual importance of the data and potential tactical/strategic impacts, (c) accelerate and optimize decision-making, and (d) enable human augmentation and deploy autonomous systems. From a national security and defense perspective, AI is a crucial technology to enhance situational awareness and accelerate the realization of timely and actionable intelligence that can save lives. For many current defense applications, this often requires the processing of visual data, images, or full motion video from legacy platforms and sensors designed decades before recent advances in machine learning, computer vision, and AI. The AI Stack - and the fusion of the interdependent technology layers contained within it - provides a streamlined approach to visualize, plan, and prioritize strategic investments in commercial technologies and transformational research to leverage and continuously advance AI across operational domains, and achieve asymmetric capability through human augmentation and autonomous systems. One application of AI for the Department of Defense is to provide automation and human augmentation for analyzing full motion video to drastically enhance the safety of our deployed soldiers by enhancing their situational awareness and enabling them to make faster decisions on more timely information to save lives.

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