Unsupervised Machine Learning Could Help Us Solve the Unsolvable

In contrast, unsupervised learning systems freely analyze'patterns' in unlabeled data, with no corresponding error or reward linked to a conclusion. It works with'unlabeled data' and is similar to'associative' or'discovery' learning in humans, something that we do very well (and often take for granted). For example, when an unsupervised system is asked to sort or arrange fruits based on raw observations, the system might'choose' to arrange the fruit based on recognition of color, placing strawberries and cherries in the'red' category; or, the system might sort based on observed sizes, grouping pears, apples, and oranges in a'medium-sized' fruit category. This latter method is commonly known as'clustering' and the accepted approach used by these systems to categorize information. Unsupervised learning is a stepping stone, a means to another end such as categorization or finding potential correlations or solutions unable to be spotted by humans or supervised learning systems alone.