Network-Centric Meaning-Driven Human-Centric AI-Cyber Computing Beyond Data-Driven to Event-Driven Architectures for Quantum Uncertainty, 1995-2023:Building upon the contextual focus of current global worldwide discussions on GPT, ChatGPT, GenAI, Generative AI, Large Language Model - LLMs, we help you advance beyond the ongoing global AI-hype - on both dystopian and utopian extremes - to focus on the latest AI Event-Driven Architectures technical developments such as in AIOps, MLOPs, DevSecOps, Infrastructure As Code, Configuration As Code, Platform As Code, Pipeline As Code for building Cloud Computing AI-Agility and Cyber-Resilience Sustainability, the focus of our last year's 2022 New York State Cybersecurity Conference presentation as AWS Partner. With increasing Digitization of Networks as Code, we help world's global 'Hardware', 'Software' and other 'Computing' providers and practitioners advance beyond legacy models of computing to the latest Cloud-based Networked Computing Utility models. Our 30-year R&D focus on Network-Centric Computing from both Socio-technical and Systems Engineering perspectives spans the schisms presented by Claude Shannon's Information Theory in how AI-ML enabled Information Processing and Sense Making can occur across both Socio-Technical and Computing-Telecom Networks*.Having already addressed the core issues being debated on mainstream Business, Finance, Technology and other media and resolved them based on 30-years of our AI-Cyber-Network Science-Engineering R&D leading worldwide Digital practices, we also help you advance ahead by 25-years on the global Risk Management standards given that the latest ISO 31000 Risk Management Standard is playing catch up having lagged 25-years behind our R&D leading global Risk Management practices. Based on most thorough R&D over 30-years and most in-depth analysis of current academic, scholarly, policy and practice resources, we provide definitive answers on many issues that have left too many confused with what our analysis shows as extreme dystopian and utopian views. This confusion results from ambiguity between the specific AI technologies and their use, abuse, misuse by the respective human users in diverse contexts, our core R&D focus on Human-Machine Systems Learning, Intelligence and Performance for 30-years ranked for its worldwide impact among AI-Quant Finance Nobel Laureates such as Herbert Simon (ASIS&T and University of Minnesota research impact and citation impact reports, for example) and Black-Scholes, Markowitz, and Sharpe (AACSB Impact of Research Report, for example) with our related research models and methods applied in empirical practice by organizations as diverse as the National Aeronautics and Space Administration (NASA) and Big Banks, for instance.• Should we be afraid of latest AI such as Generative AI and ban such R&D?- No! Such R&D must continue by dispelling the myths being spun about AI.• Does AI have the capability or capacity of posing existential risk to humans?- No! But Humans with vested interest in exploiting other Humans do!• Does AI have intelligence to take over control of humans & destroy them?- No! But Humans with vested interest in exploiting other Humans do!• What can be done for dispelling the AI myths spun up to make real progress?- We have already been doing so having already advanced beyond such myths with focus on saving all 90% time, cost, & resources by our focus on Science & Scientific R&D driven Zero-Hype practices. - We share our learning and freely accessible R&D leading global practices with all in order to help you advance beyond the AI-Hype in making real progress for the human civilization and humanity at large.* There has been an over-concentration on Shannon's definition of information in terms of uncertainty (a very good definition for the original purposes) with little attempt to understand how MEANING directs a message in a network. This, combined with a concentration on end-points (equilibria) rather than properties of the trajectory (move sequence) in games has led to a very unsatisfactory treatment of the dynamics of organizations. – AI-Genetic Algorithms pioneer Dr. John H. Holland, then at the Santa Fe Institute (personal communication, June 21, 1995) :Source: Malhotra, Y., Expert Systems for Knowledge Management: Crossing the Chasm between Information Processing and Sense Making, Expert Systems with Applications: An International Journal, 20(1), 7-16, 2001. (Ranked by the journal as top-ranked article in its usage statistics)
