Kordon Consulting LLC

Practical Issues in Human and Artificial Intelligence Interaction

The book chapter focuses on some practical issues in human and AI interaction based on applying AI in several large corporations. The following topics are discussed: weaknesses of human intelligence, disadvantages of AI, benefits of human intelligence from AI, adverse effects of AI on human intelligence, the resistance of human intelligence toward AI, and how to improve the interaction between human and artificial intelligence. The discussed issues are illustrated with examples from real-world applications.The book chapter focuses on some practical issues in human and AI interaction based on applying AI in several large corporations. The following topics are discussed: weaknesses of human intelligence, disadvantages of AI, benefits of human intelligence from AI, adverse effects of AI on human intelligence, the resistance of human intelligence toward AI, and how to improve the interaction between human and artificial intelligence. The discussed issues are illustrated with examples from real-world applications.

How Competitive Is Genetic Programming in Business Data Science Applications?

The paper evaluates Genetic Programming’s competitiveness in business Data Science-driven applications and suggests the necessary steps to increase its reach, impact and competitiveness. First, the key business needs for Data Science are identified and discussed, followed by an analysis of the competitive landscape and popularity of Data Science methods. The competitive advantages and weaknesses of GP as well its impressive application record are reviewed. Two business applications with high value creation – inferential sensors and nonlinear business forecasting – are identified and described. The recommended action items to increase competitive presence of GP in Data Science business applications include: develop a successful marketing strategy toward statistical, machine/deep learning, and business communities; broaden application areas; improve professional development tools; and increase GP visibility and teaching in Data Science classes.The paper evaluates Genetic Programming’s competitiveness in business Data Science-driven applications and suggests the necessary steps to increase its reach, impact and competitiveness. First, the key business needs for Data Science are identified and discussed, followed by an analysis of the competitive landscape and popularity of Data Science methods. The competitive advantages and weaknesses of GP as well its impressive application record are reviewed. Two business applications with high value creation – inferential sensors and nonlinear business forecasting – are identified and described. The recommended action items to increase competitive presence of GP in Data Science business applications include: develop a successful marketing strategy toward statistical, machine/deep learning, and business communities; broaden application areas; improve professional development tools; and increase GP visibility and teaching in Data Science classes.

Intelligent Systems in Industry: A Realistic Overview

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The objective of this paper is to give a realistic overview of the current state of the art of intelligent systems in industry based on the experience from applying these systems in a large global corporation. It includes a short analysis of the differences between academic and industrial research, examples of the key implementation areas of intelligent systems in manufacturing and business, a discussion about the main factors for success and failure of industrial intelligent systems, and an estimate of the projected industrial needs that may drive future applications of intelligent systems.The objective of this paper is to give a realistic overview of the current state of the art of intelligent systems in industry based on the experience from applying these systems in a large global corporation. It includes a short analysis of the differences between academic and industrial research, examples of the key implementation areas of intelligent systems in manufacturing and business, a discussion about the main factors for success and failure of industrial intelligent systems, and an estimate of the projected industrial needs that may drive future applications of intelligent systems.