ML is considered to be the origin of Artificial Intelligence (AI) and one of the basic ways in which it can be understood. Currently it has been established that ML is an autonomous discipline that seeks to achieve some concrete and specific goals that Artificial Intelligence is no longer trying to get because it is pursuing more general and wide objectives. ML can be defined as “a field of computer science that studies algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programing methods” (Rebala et atl, 2019).
The advantage brought by ML are related to the bigger perspectives and to the possibility to work with a much wider amount of information in comparison with human beings: “ML algorithms tend to be more accurate than human-created rules since ML algorithms will consider all data points in a dataset without any human bias due to prior knowledge” (Rebala et atl, 2019).
Some of the main problems of ML, still being solved, are: the ability to classify contents into different classes or categories; clustering, this is, creating new categories in order to include there some objects from the information analyses; prediction based on the classified and clustered information (Rebala et atl, 2019).