Artificial Intelligence Applied: Six Actual Projects in Big Organizations

Artificial Intelligence Applied: Six Actual Projects in Big Organizations

Gaetano Bruno Ronsivalle, Arianna Boldi
DOI: 10.4018/978-1-5225-6261-0.ch006
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The purpose of the chapter is to present some real applications of the most advanced information technologies in complex adaptive systems like for-profit companies and organizations. In particular, the authors present the application of machine learning and artificial intelligence to support some of the activities that are strategic for an effective management of human resources. The tools have been applied to analyze the professional profiles (competencies, skills, knowledge, and activities), to evaluate the candidates for hiring and selection, to assess the competences in order to obtain a certification, or to prove the results of a training course. For each project, the authors provide a description of 1) the context, 2) the problem, 3) the solution implemented, 4) an analysis of the advantages and the limits of the solution. All these cases offer quantitative and qualitative data to sustain the thesis: artificial intelligence is a tool that can help humans managing the complexity levels of the so-called Anthropocene era we live in.
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The greatest difficulty for those who research on Artificial Intelligence (AI) and apply it to solve problems, could be the fact that a precise and single definition of the term, universally accepted by practitioners, is still lacking. The borders of the discipline are still blurry, hampering the ability to distinguish between what constitutes AI and what does not (NSTC, 2016).

From a scientific point of view, AI can be defined as a multidisciplinary area of research and industrial application. Some of the disciplines that belong to this area are: Computer Science, Logic, Mathematics, Cybernetics, Robotics, Bionics, Genetics, Computational Linguistics, Neuroscience, Psychology, Philosophy of Science, Data Science, Electronics Engineering, Physics, Biology. Artificial intelligence, then, is the point of convergence of a multiplicity of interpretations and views that depend on: the core competencies of various experts, their relative scientific-disciplinary sectors, the membership of their work Team and the scope/application of the research.

Anyway, this diversity is also a point of strength. Indeed, thanks to the interplay between the different disciplines and the various contributions of the experts, AI represents, today, a more organic and systemic response to global issues. AI seems to be both the most important challenge and opportunity that humans must take on, in order to solve the problems affecting their lives and the entire biosphere. In the world, the sight is set on AI, as everyone is waiting for the next threshold of the history of the human kind. In this context, AI is literally spread across the world, not only in the form of debates and discussions: it is embedded in the objects of everyday life.

Not only a close category of scientists and experts is engaged in the debates, as a common sense of AI has arisen among people who do not work in the field and could easily misunderstand it. Depending on a variety of elements (their culture, literacy, origin, job…) the perceptions, emotions and opinions associated with the term “AI” may be very contrasting and very little consistent. The natural tendency to confer humanlike properties to the non-human entities (anthropomorphism) and the cognitive bias happening during reasoning lead people to build fictional images of AI, which are often transmitted and strengthen by non-scientific media.

This phenomenon produces undesirable outcomes for those who try to use the technologies related to AI to analyze and resolve problems concerning complex dynamic system (organizations, companies and groups) as in the case of the authors. When trying to present the methodology and the tool or to interpret the results we obtained in front of the Customer, it is common to encounter resistance and to detect false beliefs about the functioning of an AI technology and the way to interpret the results. “Artificial Intelligence refers to a vastly greater space of possibilities than does the term Homo Sapiens” writes Yudkowsky (2008, p. 5): and, in this space, people may get lost.

The misunderstanding is rooted in the lack of culture about the topic, starting from the basic glossary of the terms related. People find hard to give a common description of the term “intelligence”, namely to identify and describe the component parts and their relations, and to distinguish a “natural”, human intelligence from an artificial one. AI tools, as neural networks, are intelligent, but not in the human sense: they require a change in our way to formulate the problems and to interpret the results of the solution these tools propose.

In fact, as consultants and researchers, the main goal, for those who use any AI tool for a better understanding of a phenomenon, is to provide the professionals working in the organization with the information they need to take decisions: selection, recruiting and career choices, for instance, which have a big impact both on the workers and on the whole organizations. An ethic issue that everyone will address in the future and that should take in mind.

In this chapter, therefore, the authors refer to a specific area of AI, the field which aims to understand and to build intelligent entities, rational thinkers and learning systems. Some of the methods and the tools derived from this particular approach are neural networks, which are extremely useful to face issues regarding knowledge and learning in a complex adaptive system (CAS).

Key Terms in this Chapter

Bayesian Network: A particular type of statistical model that represents a set of variables and their conditional dependencies. It is usually used to make previsions in a great variety of events.

Supervised Learning: A particular form of learning process that takes place under supervision and that affects the training of an artificial neural networks.

Self-Organizing Map: Neural network which simulated some cerebral functions in elaborating visual information. It is usually used to classify a large amount of data.

Unsupervised Learning: A particular form of learning process that takes place without supervision and that affects the training of an artificial neural networks.

Artificial Neural Network: Information elaboration system, software, or hardware that is based on the biological nervous systems, and it is composed of code units called “nodes” or “artificial neurons.”

Perceptron: The simplest form of artificial neural network, a basic operational unit which employs supervised learning. It is used to classify data into two classes.

Moodle: An open source learning platform designed to create customized virtual learning environments.

Artificial Intelligence: A variety of disciplines, tools, studies, and techniques that have the purpose to describe every aspect of learning and intelligence in a way that a machine can be simulate it.

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