Hybrid Intelligence Framework for Augmented Analytics

Hybrid Intelligence Framework for Augmented Analytics

Alexander P. Ryjov (Lomonosov Moscow State University, Russia)
Copyright: © 2021 |Pages: 24
DOI: 10.4018/978-1-7998-4963-6.ch002
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Analytics is a key success factor for any business in the competitive and fast-changing world we live in. Using analytics, people, business, social, and government organizations become capable of understanding the past, including lessons from faults and achievements; realize current strengths, weaknesses, opportunities, and threats; and predict the future. Intelligent analytics allow doing these more effectively and efficiently. Modern analytics uses many advanced techniques like big data, artificial intelligence, and many others. This chapter aims to introduce the hybrid intelligence approach by focusing on its unique analytical capabilities. The state-of-the-art in hybrid intelligence—symbiosis and cooperative interaction between human intelligence and artificial intelligence in solving a wide range of practical tasks—and one of the hybrid intelligence frameworks—a human-centered evaluation approach and monitoring of complex processes—have been considered in this chapter. The chapter could be interesting for analysts and researchers who desire to do analytics with more intelligence.
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Organism and organization are words with the same root. Both of them survive in a complex environment and try to achieve their goals. Both of them have particular organs and structures for surviving and being effective and efficient. In this analogy, analysts are a brain for any organization. The analytical structure is people and tools. Analytical tools had evolved from simple models and standard software like Excel to specialized tools based on advanced complex models and methods. The more intelligent analytical structure organizations have, the more effective organization is. An intelligent analytical structure means intelligent analysts and intelligence tools. Analytical talents are a limited resource; therefore, improvement of analytical tools' intelligence level is a topical and requested task.

Figure 1.

Technology stack


Which analytical tool is suitable for a particular organization? There is no universal recommendation; all organizations are unique. But it is possible to present a general picture (Figure 1). Any organization accumulates data and expertise (or knowledge) during its lifetime. If the organization has a large amount of data, suitable tools could be Big Data-based analytics like Data Mining, Artificial Neural Networks, etc. The examples could be retail, telecom, government (smart city). If the principal asset is knowledge (for example, consulting companies), suitable tools could be Artificial Intelligence (AI)-based analytics. Both these situations (left-top and right-bottom corners in Figure 1) are well-studied. The left- bottom corner is when only intuition could help analysts; the right-top corner is engineering tasks (like calculating the reliability of a bridge or building).

This chapter focuses on the situation when an organization has some mix of data and knowledge. This situation is typical for many organizations and is not so well-studied, like analytics based on Big Data or AI.

Key Terms in this Chapter

Artificial Intelligence (AI): AI is the simulation of human intelligence processes by computer systems. Particular applications of AI include text and speech recognition, machine vision, learning by examples, etc.

Evaluation and Monitoring: It is a process that helps improve performance and achieve results for particular processes. Its goal is to improve current and future management of outputs, outcomes, and impact. It establishes links between the past, present, and future actions.

Hierarchical Systems: It is a special type of systems where elements (objects, names, values, categories, etc.) are represented as being “above”, “below”, or “at the same level as” one another. A hierarchy can link elements either directly or indirectly, and either vertically or diagonally.

Fuzzy Logic: Fuzzy Logic is a form of mathematical logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

Measurement: Measurement is the assignment of a value (number, symbol, etc.) to a characteristic of an object or event, which can be compared with other objects or events.

Augmented Intelligence: Human-computer systems allow us to combine human intelligence strengths (for example, intuition) and computer’s computational power. Augmented intelligence enhances and scales human expertise; AI systems attempt to replicate human intelligence.

Fuzzy Sets: It is a set of elements that have no strict boundaries. Examples of such sets are sets of “young people”, “expensive cars”, “successful companies”, etc.

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