The crux of Machine Learning (ML) is based on the recognition that human intelligence hinges more on ‘probability’ than ‘reason’ or ‘logic’. Our brains unconsciously perform more estimation – calculating the fastest driving route based on prior experience, guessing an opponent’s move while playing a board game, plotting a strategy – than using the skill of reasoning and thinking. The human brain relies first and foremost on its ability to assess how likely something is; we are not consciously aware of this.
The machine is flexible in its learning and uses an iterative approach to independently adapt to new data. The models learn from previous computations to produce repeatable and reliable results and decisions. Machine Learning algorithms have been around since the 1950s. However, the ability to apply complex mathematical calculations to big data is a recent development. The growing volumes and varieties of available information, cheap and powerful computational processing and affordable data storage makes the comprehensive machine learning capabilities available.
All of these things mean it's easier than ever for organizations to benefit from this technology by identifying profitable opportunities or avoiding unknown risks, building precise models and analyzing them to deliver effective solutions.
One of the critical questions about machine learning is, how much of human intelligence can be approximated with statistics?