Machine learning: Difference between revisions

Revision as of 05:26, 6 July 2025

Revision as of 08:04, 6 July 2025

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== History ==

{{See also|Timeline of machine learning}}

4TheThe term ''machine learning'' was coined in 1959 by [[Arthur Samuel (computer scientist)|Arthur Samuel]], an [[IBM]] employee and pioneer in the field of [[computer gaming]] and [[artificial intelligence]].<ref name="Samuel">{{Cite journal|last=Samuel|first=Arthur|date=1959|title=Some Studies in Machine Learning Using the Game of Checkers|journal=IBM Journal of Research and Development|volume=3|issue=3|pages=210–229|doi=10.1147/rd.33.0210|citeseerx=10.1.1.368.2254|s2cid=2126705 }}</ref><ref name="Kohavi">R. Kohavi and F. Provost, "Glossary of terms", Machine Learning, vol. 30, no. 2–3, pp. 271–274, 1998.</ref> The synonym ''self-teaching computers'' was also used in this time period.<ref name=cyberthreat>{{cite news |last1=Gerovitch |first1=Slava |title=How the Computer Got Its Revenge on the Soviet Union |url=https://nautil.us/issue/23/dominoes/how-the-computer-got-its-revenge-on-the-soviet-union |access-date=19 September 2021 |work=Nautilus |date=9 April 2015 |archive-date=22 September 2021 |archive-url=https://web.archive.org/web/20210922175839/https://nautil.us/issue/23/Dominoes/how-the-computer-got-its-revenge-on-the-soviet-union |url-status=dead }}</ref><ref>{{cite journal |last1=Lindsay |first1=Richard P. |title=The Impact of Automation On Public Administration |journal=Western Political Quarterly |date=1 September 1964 |volume=17 |issue=3 |pages=78–81 |doi=10.1177/106591296401700364 |s2cid=154021253 |url=https://journals.sagepub.com/doi/10.1177/106591296401700364 |access-date=6 October 2021 |language=en |issn=0043-4078 |archive-date=6 October 2021 |archive-url=https://web.archive.org/web/20211006190841/https://journals.sagepub.com/doi/10.1177/106591296401700364 |url-status=live |url-access=subscription }}</ref>

The earliest machine learning program was introduced in the 1950s when [[Arthur Samuel (computer scientist)|Arthur Samuel]] invented a [[computer program]] that calculated the winning chance in checkers for each side, but the history of machine learning roots back to decades of human desire and effort to study human cognitive processes.<ref name="WhatIs">{{Cite web |title=History and Evolution of Machine Learning: A Timeline |url=https://www.techtarget.com/whatis/A-Timeline-of-Machine-Learning-History |access-date=8 December 2023 |website=WhatIs |language=en |archive-date=8 December 2023 |archive-url=https://web.archive.org/web/20231208220935/https://www.techtarget.com/whatis/A-Timeline-of-Machine-Learning-History |url-status=live }}</ref> In 1949, [[Canadians|Canadian]] psychologist [[Donald O. Hebb|Donald Hebb]] published the book ''[[Organization of Behavior|The Organization of Behavior]]'', in which he introduced a [[Hebbian theory|theoretical neural structure]] formed by certain interactions among [[nerve cells]].<ref>{{Cite journal |last=Milner |first=Peter M. |date=1993 |title=The Mind and Donald O. Hebb |url=https://www.jstor.org/stable/24941344 |journal=Scientific American |volume=268 |issue=1 |pages=124–129 |doi=10.1038/scientificamerican0193-124 |jstor=24941344 |pmid=8418480 |bibcode=1993SciAm.268a.124M |issn=0036-8733 |access-date=9 December 2023 |archive-date=20 December 2023 |archive-url=https://web.archive.org/web/20231220163326/https://www.jstor.org/stable/24941344 |url-status=live |url-access=subscription }}</ref> Hebb's model of [[neuron]]s interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or [[artificial neuron]]s used by computers to communicate data.<ref name="WhatIs" /> Other researchers who have studied human [[cognitive systems engineering|cognitive systems]] contributed to the modern machine learning technologies as well, including logician [[Walter Pitts]] and [[Warren Sturgis McCulloch|Warren McCulloch]], who proposed the early mathematical models of neural networks to come up with [[algorithm]]s that mirror human thought processes.<ref name="WhatIs" />