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==Early work==
Human-based computation (apart from the [[Computer (job description)|historical meaning of "computer]]") research has its origins in the early work on [[interactive evolutionary computation]] (EC).<ref>{{cite book |url=https://link.springer.com/chapter/10.1007/3-540-61723-X_966 |author=Herdy, Michael |title=Evolution strategies with subjective selection. Basic Concepts of Evolutionary Computation. Volumen 1141, pp. 22-31 |date=1996|pages=22–31 |doi=10.1007/3-540-61723-X_966 |isbn=9783540706687 |access-date=12 May 2022}}</ref> The idea behind interactive evolutionary algorithms has been attributed to [[Richard Dawkins]]; in the Biomorphs software accompanying his book ''[[The Blind Watchmaker]]'' (Dawkins, 1986)<ref>{{cite web|url=https://archive.org/stream/BlindWatchmakerTheRichardDawkins/Blind_Watchmaker_The_-_Richard_Dawkins_djvu.txt|title=''The Blind Watchmaker'' |access-date=12 May 2022|author=Dawkins, Richard}}</ref> the preference of a human experimenter is used to guide the evolution of two-dimensional sets of line segments. In essence, this program asks a human to be the fitness function of an evolutionary algorithm, so that the algorithm can use human visual perception and aesthetic judgment to do something that a normal evolutionary algorithm cannot do. However, it is difficult to get enough evaluations from a single human if we want to evolve more complex shapes. [[Victor Johnston]]<ref>{{cite web |url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=5375195.PN.&OS=PN/5375195&RS=PN/5375195 |title=''Method and apparatus for generating composites of human faces'' |access-date=12 May 2022 |author=Johnston, Victor }}{{Dead link|date=August 2024 |bot=InternetArchiveBot |fix-attempted=yes }} {{US patent|5375195}}</ref> and [[Karl Sims]]<ref>{{cite web |url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=6088510.PN.&OS=PN/6088510&RS=PN/6088510 |title=''Computer system and method for generating and mutating objects by iterative evolution'' |access-date=12 May 2022 |author=Sims, Karl P. }}{{Dead link|date=August 2024 |bot=InternetArchiveBot |fix-attempted=yes }} {{US patent|6088510}}</ref> extended this concept by harnessing the power of many people for fitness evaluation (Caldwell and Johnston, 1991; Sims, 1991). As a result, their programs could evolve beautiful faces and pieces of art appealing to the public. These programs effectively reversed the common interaction between computers and humans. In these programs, the computer is no longer an agent of its user, but instead, a coordinator aggregating efforts of many human evaluators. These and other similar research efforts became the topic of research in aesthetic selection or [[interactive evolutionary computation]] (Takagi, 2001), however the scope of this research was limited to outsourcing evaluation and, as a result, it was not fully exploring the full potential of the outsourcing.
A concept of the automatic [[Turing test]] pioneered by [[Moni Naor]] (1996)<ref>{{cite web |url=http://www.wisdom.weizmann.ac.il/~naor/PAPERS/human_abs.html |author=Naor, Moni |title=Verification of a human in the loop or Identification via the Turing Test |access-date=12 May 2021}}</ref> is another precursor of human-based computation. In Naor's test, the machine can control the access of humans and computers to a service by challenging them with a [[natural language processing]] (NLP) or [[computer vision]] (CV) problem to identify humans among them. The set of problems is chosen in a way that they have no algorithmic solution that is both effective and efficient at the moment. If it existed, such an algorithm could be easily performed by a computer, thus defeating the test. In fact, Moni Naor was modest by calling this an automated Turing test. The [[imitation game]] described by [[Alan Turing]] (1950) didn't propose using CV problems. It was only proposing a specific NLP task, while the Naor test identifies and explores a large [[AI-complete|class]] of problems, not necessarily from the ___domain of NLP, that could be used for the same purpose in both automated and non-automated versions of the test.
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* (HH<sub>1</sub>) [[Social search]] applications accept contributions from users and attempt to use human evaluation to select the fittest contributions that get to the top of the list. These use one type of human-based innovation. Early work was done in the context of HBGA. [[Digg]] and [[Reddit]] are recently popular examples. See also [[Collaborative filtering]].
* (HC) Computerized tests. A computer generates a problem and presents it to evaluate a user. For example, [[CAPTCHA]] tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer. While CAPTCHAs are effective security measures for preventing automated abuse of online services, the human effort spent solving them is otherwise wasted. The [[reCAPTCHA]] system makes use of these human cycles to help digitize books by presenting words from scanned old books that optical character recognition cannot decipher.<ref>{{cite web |author=von Ahn |first=Luis |last2=Maurer |first2=Benjamin |last3=McMillen |first3=Colin |last4=Abraham |first4=David |last5=Blum |first5=Manuel |date=12 September 2008 |title=reCAPTCHA: Human-Based Character Recognition via Web Security Measures |url=https://www.cs.cmu.edu/~biglou/reCAPTCHA_Science.pdf |access-date=12 May 2022}}</ref>
* (HC) Interactive online games: These are programs that extract knowledge from people in an entertaining way.<ref>{{cite web |url=http://www.20q.net/index.html |author=Burgener, Robin |title=20Q . net. Twenty Questions. The neural-net on the Internet. Play Twenty Questions |access-date=12 May 2022 |archive-url=https://web.archive.org/web/20000229091147/http://www.20q.net/index.html |archive-date=29 February 2000 |url-status=dead}}</ref><ref>{{cite web |author=von Ahn |first=Luis |last2=Dabbish |first2=Laura |title=Labeling Images with a Computer Game |url=https://www.cs.cmu.edu/~biglou/ESP.pdf |access-date=12 May 2022}}</ref><ref>{{cite web |author=von Ahn |first=Luis |last2=Kedia |first2=Mihir |last3=Blum |first3=Manuel |title=Verbosity: A Game for Collecting Common-Sense Facts |url=https://www.cs.cmu.edu/~biglou/Verbosity.pdf |access-date=12 May 2022}}</ref><ref>{{cite web |author=von Ahn |first=Luis |last2=Ginosar |first2=Shiri |last3=Kedia |first3=Mihir |last4=Liu |first4=Ruoran |last5=Blum |first5=Manuel |title=Improving Accessibility of the Web with a Computer Game |url=https://www.cs.cmu.edu/~biglou/Phetch.pdf |access-date=12 May 2022}}</ref><ref>{{cite web |url=https://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=7980953.PN.&OS=PN/7980953&RS=PN/7980953 |title=Method for labeling images through a computer game |author=von Ahn, Luis |date=19 July 2011 |access-date=12 May 2022 }}{{Dead link|date=August 2024 |bot=InternetArchiveBot |fix-attempted=yes }}{{US patent|7980953}}</ref>
* (HC) "Human Swarming" or "Social Swarming". The UNU platform for human swarming establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified [[collective intelligence]].<ref>{{cite web |author=Rosenberg, Louis B. |title=Human Swarms: a real-time paradigm for Collective intelligence |url=http://sites.lsa.umich.edu/collectiveintelligence/wp-content/uploads/sites/176/2015/05/Rosenberg-CI-2015-Abstract.pdf |access-date=12 May 2021 |website=[[University of Michigan College of Literature, Science, and the Arts | University of Michigan College of LSA]]}}</ref><ref>{{cite web |url=https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |title=Swarms: a real-time paradigm for Collective intelligence |access-date=12 May 2022 |archive-url=https://web.archive.org/web/20151027132802/https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |archive-date=27 October 2015 |url-status=dead}}</ref><ref>{{cite web |url=https://papers.ssrn.com/sol3/papers.cfm?abstract_id=924249 |title=Infotopia: How Many Minds Produce Knowledge |author=Sunstein, Cass R. |date=August 16, 2006 |ssrn=924249 |access-date=12 May 2022}}</ref><ref>{{cite journal |author=Malone |first=Thomas W. |last2=Laubacher |first2=Robert |last3=Dellarocas |first3=Chrysanthos |date=February 3, 2009 |title=Harnessing Crowds: Mapping the Genome of Collective Intelligence |url=https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1381502 |journal= |doi=10.2139/ssrn.1381502 |s2cid=110848079 |ssrn=1381502 |access-date=12 May 2022 |hdl-access=free |hdl=1721.1/66259}}</ref><ref>{{cite web |url=https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |title=Human Swarms, a real-time method for collective intelligence |access-date=October 12, 2015 |archive-url=https://web.archive.org/web/20151027132802/https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |archive-date=October 27, 2015 |url-status=dead |df=mdy-all }}</ref><ref>{{Cite web |url=http://news.discovery.com/human/life/swarms-of-humans-power-a-i-platform-150603.htm |title=Swarms of Humans Power A.I. Platform : Discovery News |access-date=June 21, 2015 |archive-date=June 21, 2015 |archive-url=https://web.archive.org/web/20150621165834/http://news.discovery.com/human/life/swarms-of-humans-power-a-i-platform-150603.htm |url-status=dead }}</ref>
* (NHC) Natural Human Computation involves leveraging existing human behavior to extract computationally significant work without disturbing that behavior.<ref>[https://arxiv.org/abs/1306.6376 Estrada, Daniel, and Jonathan Lawhead, "Gaming the Attention Economy" in ''The Springer Handbook of Human Computation'', Pietro Michelucci (ed.), (Springer, 2014)]</ref> NHC is distinguished from other forms of human-based computation in that rather than involving outsourcing computational work to human activity by asking humans to perform novel computational tasks, it involves taking advantage of previously unnoticed computational significance in existing behavior.
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