Modeling the Management Metaphor with Massive Deep Learning using Artificial Intelligence
DOI:
Abstract
In recent years, unprecedented global changes have occurred in the digital and traditional worlds, associated with a significant acceleration of the impact of digital transformations and artificial intelligence tools as one of the main factors of the evolutionary and revolutionary engines of modern civilization, which provides unprecedented opportunities, problems and challenges for everyone. The problem, in our opinion, is that the huge potential of using advanced digital transformations and artificial intelligence systems for mass deep lifelong learning, increasing the literacy and competencies of persons through the implementation of their long-term projects has still not found widespread application in practice. One of the methods and means of promoting the sustainable development of persons under the influence of increasingly rapid changes is our evolutionary approach to modeling a general mathematized metaphor of a dynamic science-based object, a long-term project, means “Massive Deep Learning Throughout the Life of Civil Servants of Ukraine”, namely: “Escalator of Sustainable Development of Unique Person-Centered Projects”. The aim of this study was to define in the mathematical constructs of our arrow theory, this Escalator, which represents the evolution of personal project trajectories relative to established planned results and aims to facilitate the search for the best first search for an exemplary solution based on best practices, allowing persons to timely understand their status quo and adapt to the future of learning, work and social life. Our arrow approach is based on the proposed mathematized method of horizontal and vertical reduction, system methodology, abstraction, analysis and synthesis, analogies, virtualization, methods from basic disciplines such as mathematics, psychology, digital pedagogy, lifelong learning, linguistics, computer science, project management. Project volume indicators for the next 3-5 years are determined.
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