What is Machine Learning? A detailed Definition

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What is Machine Learning? A detailed Definition
« on: January 11, 2020, 12:27:38 PM »
What is Machine Learning?



Machine learning is a technology that allows computers to learn directly from examples and experience in the form of data. Traditional approaches to programming rely on hard coded rules, which set out how to solve a problem, step-by-step. In contrast, machine learning systems are set a task, and given a large amount of data to use as examples of how this task can be achieved or from which to detect patterns. The system then learns how best to achieve the desired output. It can be thought of as narrow AI: machine learning supports intelligent systems, which are able to learn a particular function, given a specific set of data to learn from.

In some specific areas or tasks, machine learning is already able to achieve a higher level of performance than people. For other tasks, human performance remains much better than that of machine learning systems. For example, recent advances in image recognition have made these systems more accurate than ever before. In one image labeling challenge, the accuracy of machine learning has increased from 72% in 2010, to 96% in 2015, surpassing human accuracy at this task [1]. However, human-level performance at visual recognition in more general terms remains considerably higher than these systems can achieve.
" In one image labeling challenge, the accuracy of machine learning has increased from 72% in 2010, to 96% in 2015, surpassing human accuracy at this task."




While not approaching the human-level intelligence which is usually associated with the term AI, the ability to learn from data increases the number and complexity of functions that machine learning systems can undertake, in comparison to traditional programming methods. Machine learning can carry out tasks of such complexity that the desired outputs could not be specified in programs based on step-by-step processes created by humans. The learning element also creates systems which can be adaptive, and continue to improve the accuracy of their results after they have been deployed [2].

Machine learning lives at the intersection of computer science, statistics, and data science. It uses elements of each of these fields to process data in a way that can detect and learn from patterns, predict future activity, or make decisions.


References:

[1] The Economist. 2016 From not working to neural networking. See http://www.economist.com/news/specialreport/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not (accessed 22 March 2017).


[2] Shalev-Shwartz S, Ben-David S. 2014 Understanding machine learning: from theory to algorithms. Cambridge, UK: Cambridge University Press.
« Last Edit: January 11, 2020, 12:29:54 PM by mechanic »