I. Introduction
Artificial intelligence (AI) was recognized as an academic discipline as early as in 1950s, but it was not widely explored by scientific community due to its limited practical feasibility for a long time [1]. In last couple of decades, because of increasing availability of processing power and emergence of big data, AI became a focal point of research and business discussions. AI went through various phases in its development, often regarded as “Seasons of AI”. Until recently the best-known season of AI, was the period from the 1970s to the 2000s and the majority of this period is referred to as the “AI Winter” [2]. One of the main reasons why AI could not develop at a fast pace during this period is the fact that computers did not have sufficient processing power to handle the work required [3]. The comeback of AI started when IBM developed their chess playing program, Deep Blue, which was able to beat world champion Gary Kasparov in 1997 [1]. In the following years AI started to develop rapidly, which resulted in development of new fields such as Machine Learning (ML) and Deep Learning (DL). In contrast to ML, DL uses a set number of traits and requires human input, and can be trained to classify data on its own [2].