Editing Machine learning (section)

== History ==
{{See also|Timeline of machine learning}}
The 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>

Although the earliest machine learning model was introduced in the 1950s when [[Arthur Samuel (computer scientist)|Arthur Samuel]] invented a [[Computer program|program]] that calculated the winning chance in checkers for each side, 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" />

By the early 1960s, an experimental "learning machine" with [[punched tape]] memory, called Cybertron, had been developed by [[Raytheon Company]] to analyse [[sonar]] signals, [[Electrocardiography|electrocardiograms]], and speech patterns using rudimentary [[reinforcement learning]]. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "[[goof]]" button to cause it to reevaluate incorrect decisions.<ref>"Science: The Goof Button", [[Time (magazine)]], 18 August 1961.
</ref> A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.<ref>Nilsson N. Learning Machines, McGraw Hill, 1965.</ref> Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.<ref>Duda, R., Hart P. Pattern Recognition and Scene Analysis, Wiley Interscience, 1973</ref> In 1981 a report was given on using teaching strategies so that an [[artificial neural network]] learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.<ref>S. Bozinovski "Teaching space: A representation concept for adaptive pattern classification" COINS Technical Report No. 81-28, Computer and Information Science Department, University of Massachusetts at Amherst, MA, 1981. https://web.cs.umass.edu/publication/docs/1981/UM-CS-1981-028.pdf {{Webarchive|url=https://web.archive.org/web/20210225070218/https://web.cs.umass.edu/publication/docs/1981/UM-CS-1981-028.pdf |date=25 February 2021 }}</ref>

[[Tom M. Mitchell]] provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience ''E'' with respect to some class of tasks ''T'' and performance measure ''P'' if its performance at tasks in ''T'', as measured by ''P'',  improves with experience ''E''."<ref name="Mitchell-1997">{{cite book
|author=Mitchell, T. 
|title=Machine Learning
|publisher=McGraw Hill
|isbn= 978-0-07-042807-2
|pages=2
|year=1997}}</ref> This definition of the tasks in which machine learning is concerned offers a fundamentally [[operational definition]] rather than defining the field in cognitive terms. This follows [[Alan Turing]]'s proposal in his paper "[[Computing Machinery and Intelligence]]", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".<ref>{{Citation |chapter-url=http://eprints.ecs.soton.ac.uk/12954/ |first=Stevan |last=Harnad |author-link=Stevan Harnad |year=2008 |chapter=The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence |editor1-last=Epstein |editor1-first=Robert |editor2-last=Peters |editor2-first=Grace |title=The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer |pages=23–66 |publisher=Kluwer |isbn=9781402067082 |access-date=11 December 2012 |archive-date=9 March 2012 |archive-url=https://web.archive.org/web/20120309113922/http://eprints.ecs.soton.ac.uk/12954/ }}</ref>

Modern-day machine learning has two objectives.  One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.<ref>{{Cite web|date=8 December 2020|title=Introduction to AI Part 1|url=https://edzion.com/2020/12/09/introduction-to-ai-part-1/|access-date=9 December 2020|website=Edzion|language=en|archive-date=18 February 2021|archive-url=https://web.archive.org/web/20210218005157/https://edzion.com/2020/12/09/introduction-to-ai-part-1/|url-status=live}}</ref>