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

44 Pages Posted: 8 Feb 2018 Last revised: 11 Jun 2019

See all articles by Yogesh Malhotra

Yogesh Malhotra

Global Risk Management Network, LLC

Date Written: January 28, 2018

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 "The Neuromorphic Chip's Make-or-Break Moment" observes that "Neuromorphic Chips Are Destined for Deep Learning—or Obscurity" given that the Neuromorphic 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.

Keywords: Process Engineering; AI; Machine Learning; Deep Learning; Cognitive Computing; Neuromorphic Computing; Model Risk Management; Anticipatory Risk Analytics; Risk Management; Uncertainty Management; Intelligence, Surveillance, & Reconnaissance; Project Maven; Algorithmic Warfare Cross-Functional Team

Suggested Citation

Malhotra, Yogesh, Cognitive-Neuromorphic Computing for Anticipatory Risk Analytics in Intelligence, Surveillance & Reconnaissance (ISR): Model Risk Management in Artificial Intelligence & Machine Learning (Presentation Slides) (January 28, 2018). Available at SSRN: https://ssrn.com/abstract=3111837 or http://dx.doi.org/10.2139/ssrn.3111837

Yogesh Malhotra (Contact Author)

Global Risk Management Network, LLC ( email )

Cornell Business and Technology Park
Ithaca, NY 14852-4892
United States

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