Artificial Intelligence Yesterday, Today and Tomorrow

Artificial Intelligence (AI) is one of the current emerging technologies. In the history of computing AI has been in the similar role earlier - almost every decade since the 1950s, when the programming language Lisp was invented and used to implement self-modifying applications. The second time that AI was described as one of the frontier technologies was in the 1970s, when Expert Systems (ES) were developed. A decade later AI was again at the forefront when the Japanese government initiated its research and development effort to develop an AI-based computer architecture called the Fifth Generation Computer System (FGCS). Currently in the 2010s, AI is again on the frontier in the form of (self-)learning systems manifesting in robot applications, smart hubs, intelligent data analytics, etc. What is the reason for the cyclic reincarnation of AI? This paper gives a brief description of the history of AI and also answers the question above. The current AI “cycle” has the capability to change the world in many ways. In the context of the CE conference, it is important to understand the changes it will cause in education, the skills expected in different professions, and in society at large.

I. Introduction

Technology analysis is an important field of applied research. It gives an understanding of technological changes and their consequences in daily life and society. Every educator at all levels should be familiar with this topic, because they are educating people for the future and for future professions. Some technologies affect work life dramatically – some professions are disappearing, some are being born and almost all are changing, many of them becoming enriched. New technologies – innovations - are triggers that, when adopted by the users, cause changes in society. The changes may be incremental (improvements in a current trend) or radical (step up to a new level and continuing the existing trend there). In some cases innovations may cause changes in technological systems (combining several innovations provides new opportunities to adopt the innovation) or in paradigms, creating the foundation for changes in society [1]. The authors have discussed the principles of technology analysis in some of their earlier papers [2]; [3]. We will not repeat the topic in this paper, but encourage the reader to study them to get a good understanding of the principles of technological forecasting and analysis.