Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, & Reconnaissance (ISR): Model Risk Management in Artificial Intelligence & Machine Learning (Presentation Slides)

Abstract

Drawing upon insights shared in the MIT: AI & Machine Learning: Management and Leadership learning community of practice, the current Intelligence, Surveillance, Reconnaissance (ISR) presentation advances the focus on collective intelligence of people and computers in the context of Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, & Reconnaissance (ISR). It defines as well as distinguishes multi-level Cognitive Computing process engineering frameworks of Artificial Intelligence (AI) & Machine Learning as applied in KM practices of US and worldwide firms, governments, and ISR agencies from Cognitive-Neuromorphic Chips. A recent IEEE Spectrum report also published as Chip's Make-or-Break Moment observes that Neuromorphic Chips Are Destined for Deep Learning—or Obscurity given that the Chip researchers have hitched their wagon to deep learning's star. Drawing upon insights on Model Risk Management and Anticipatory Risk Analytics focus of top Wall Street investment banks and hedge funds beyond the Global Financial Crisis currently guiding national and global Cyber Risk Insurance industry practices, we demonstrate how Model Risk Management (MRM) and Anticipatory Risk Analytics are even more critical in the global and national domains of Intelligence, Surveillance, Reconnaissance (ISR). The first operational use of AI and Deep Learning AI technologies in the Defense Intelligence Enterprise led by Project Maven and Algorithmic Warfare Cross-Functional Team is used as a case study for illustrating how Anticipatory Risk Analytics & MRM assume even greater significance at the intersections of Space and Cyberspace wherein Offensive and Defensive Cybersecurity strategies, such as discussed in the recent 2015 and 2016 presentations at the Princeton Quant Trading Conference, are deployed.