  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. annotated) objects, scenes
and actions. Whether training on large quantities of data (i.e. big data) is
the right or the wrong approach, is debatable. The fact is, that training
algorithms the same way we learn ourselves, comes with limitations. This paper
discusses the issues around applying a human-like 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.
