Human-like machine learning: limitations and suggestions

This paper attempts to address the issues of machine learning in its current implementation. It is known that machine learning algorithms require a significant amount of data for training purposes, whereas recent developments in deep learning have increased this requirement dramatically. The performance of an algorithm depends on the quality of data and hence, algorithms are as good as the data they are trained on. Supervised learning is developed based on human learning processes by analysing named (i.e. The fact is, that training algorithms the same way we learn ourselves, comes with limitations. This paper discusses the issues around applying a humanlike approach to train algorithms and the implications of this approach when using limited data. Several current studies involving non-data-driven algorithms and natural examples are also discussed and certain alternative approaches are suggested. Keywords: machine leaning, deep learning, computer vision, action recognition, big data, data-driven, synthetic, simulation 1. Introduction The human learning process involves the gradual brain development as we grow from infancy towards adulthood. This capability though, may not always provide accurate results and can often lead to classification errors e.g a clementine may be confused with an orange. The primitive humans' ability to process learning involved a type of a rather incomplete "unsupervised learning", as they observed objects but had not developed a language system to annotate them. The cognitive process where the next generations classified or named objects, is effectively what unsupervised machine learning does by clustering. The unsupervised approach can separate classes, nevertheless, an algorithm has no actual understanding of the action or object that is clustered. On the other hand, in supervised approach, every object or action has an annotation, hence the training algorithm uses an identifier of this particular action or object.