Human-led Machine Learning & Advanced Threats: Case Method Inquiry and Visual Analytics Applied to COVID-19

In a pervasive and complex information environment, analytics are vital to understanding advanced threats. As we rely more on machine-learnt results, asking the right questions and visualizing deep analysis are key to grasping and solving problems. These skills are also vital 21st century leadership tools that can forge a common focus among otherwise stove-piped specialists.

The COVID-19 pandemic presents an urgent threat that requires both scientific understanding and decisive leadership. The cause of the disease is SARS CoV-2, a mutating virus that thrives in conditions difficult to control at scale. To counter this threat, this article demonstrates in detail the potential of human-led case method and machine-provided visual analytics.

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Joint All Domain Effects Convergence: Evolving C2 Teams

 By: Ernest Nisperos Approximate Reading Time: 25 Minutes Abstract This paper aims to generate a tangible construct to complement the

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Artificial Intelligence in the Operational Information Environment: The Need for Proactive Doctrine

Joint operations doctrine omits the agency of artificial intelligence (AI) in the operational information environment, which is a problem. This commentary discusses why, and recommends effective changes. Key points are as follows. AI is becoming an autonomous cause of unanticipated effects. Humans are not the most effectively intelligent actors in all environments, yet our doctrine draws lessons from the past rather than anticipating emergent futures. Machines currently excel in experience-based learning and can discover relationships in data that we cannot discern. Humans can intuit, deceive, somewhat control, and manufacture and destroy machines. In time, AI will be able to perform those cognitive, informational and physical functions as well. As out-thought becomes out-fought, we need proactive doctrine now.

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