Human-based computation: Difference between revisions

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'''Human-based computation''' ('''HBC'''), '''human-assisted computation''',<ref>{{cite web |url=https://commonsensereasoning.org/2007/papers/shahaf-and-amir.pdf |title=Towards a Theory of AI Completeness |author=Shahaf, Dafna; and Eyal Amir |date=28 March 2007 |access-date=12 May 2022}}</ref> '''ubiquitous human computing''' or '''distributed thinking''' (by analogy to [[distributed computing]]) is a [[computer science]] technique in which a machine performs its function by outsourcing certain steps to humans, usually as [[microwork]]. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training [[Deep Learning]]-based [[Artificial intelligence|Artificial Intelligence]] systems. In this case, human-based computation has been referred to as '''human-aided artificial intelligence'''.<ref>{{Cite journal|last=Mühlhoff|first=Rainer|date=2019-11-06|title=Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning |journal=New Media & Society |volume=22|issue=10|language=en|pages=1868–1884|doi=10.1177/1461444819885334|s2cid=209363848|issn=1461-4448|url=https://depositonce.tu-berlin.de/handle/11303/12510}}</ref>

In traditional computation, a human employs a computer<ref>the term "computer" is used the modern usage of computer, not the one of [[human computer]]</ref> to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret.<ref>{{cite web|url=https://www.csee.umbc.edu/courses/471/papers/turing.pdf|author=Turing, Alan M.|title=Computer Machinery and Intelligence|date=1950|access-date=12 May 2022}}</ref> Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem,<ref>{{cite webbook|chapter-url=https://ieeexplore.ieee.org/document/1225961?section=abstract|titlechapter=Automatic concept evolution|author=Fogarty, Terence C.|title=The Second IEEE International Conference on Cognitive Informatics, 2003. Proceedings. |date=20 August 2003|page=89 |doi=10.1109/COGINF.2003.1225961 |isbn=0-7695-1986-5 |s2cid=30299981 |access-date=21 June 2021}}</ref> then collects, interprets, and integrates their solutions. This turns hybrid networks of humans and computers into "large scale distributed computing networks".<ref>{{Citation|last=von Ahn|first=Luis|title=Human Computation |url=https://www.youtube.com/watch?v=tx082gDwGcM |archive-url=https://ghostarchive.org/varchive/youtube/20211219/tx082gDwGcM |archive-date=2021-12-19 |url-status=live |volume=Google Tech Talk July 26, 2006 |access-date=2019-11-22}}{{cbignore}}. Cited after Mühlhoff, Rainer (2019). [https://journals.sagepub.com/doi/abs/10.1177/1461444819885334 "Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning".] New Media & Society: 146144481988533. doi:10.1177/1461444819885334. ISSN 1461-4448.</ref><ref>{{cite web |url=http://fc05.ifca.ai/p26.pdf|title=Secure Distributed ''Human'' Computation |author=Gentry, Craig; Zulfikar Ramzan, and Stuart Stubblebine |access-date=12 May 2022}}</ref><ref>{{cite book |chapter-url=https://link.springer.com/chapter/10.1007/11507840_28 |title=Secure Distributed ''Human'' Computation |chapter=Secure Distributed Human Computation |series=Lecture Notes in Computer Science |year=2005 |doi=10.1007/11507840_28 |access-date=12 May 2022|last1=Gentry |first1=Craig |last2=Ramzan |first2=Zulfikar |last3=Stubblebine |first3=Stuart |volume=3570 |pages=328–332 |isbn=978-3-540-26656-3 }}</ref> where code is partially executed in human brains and on silicon based processors.

==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 journalbook |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 is due 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}} {{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.}} {{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 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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* (CH) [[Interactive genetic algorithm|Interactive EC]] (Dawkins, 1986; Caldwell and Johnston, 1991; Sims, 1991) IEC enables the user to create an abstract drawing only by selecting his/her favorite images, so human only performs fitness computation and software performs innovative role. [Unemi 1998] Simulated breeding style introduces no explicit fitness, just selection, which is easier for humans.<ref>{{cite web |url=https://www.koreascience.or.kr/article/CFKO199811920543230.page |author=Unemi, Tastsuo |title=A Design of Multi-Field User Interface for Simulated Breeding, pp.&nbsp;489-494 |access-date=12 May 2022}}</ref>

* (HH₂) [[Wiki]] (Cunningham, 1995) enabled editing the web content by multiple users, i.e. supported two types of human-based innovation (contributing new page and its incremental edits). However, the selection mechanism was absent until 2002, when wiki has been augmented with a revision history allowing for reversing of unhelpful changes. This provided means for selection among several versions of the same page and turned wiki into a [[The Wiki Way|tool]] supporting collaborative content evolution (would be classified as human-based evolution strategy in EC terms).

* (HH₃) [[Human-based genetic algorithm]] (Kosorukoff, 1998) uses both human-based selection and three types of human-based innovation (contributing new content, mutation, and recombination). Thus, all operators of a typical [[genetic algorithm]] are outsourced to humans (hence the origin of '''human-based'''). This idea is extended to integrating crowds with genetic algorithm to study creativity in 2011.<ref>{{cite journalbook |url=https://dl.acm.org/doi/10.1145/1978942.1979147 |title=Cooks or cobblers?: Crowd creativity through combination |author=Yu, Lixiu; and Jeffrey V. Nickerson |date=May 7, 2011 |pages=1393–1402 |doi=10.1145/1978942.1979147 |isbn=9781450302289 |s2cid=11287874 |access-date=12 May 2022}}</ref>

* (HH₁) [[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 |url=https://www.cs.cmu.edu/~biglou/reCAPTCHA_Science.pdf |title=reCAPTCHA: Human-Based Character Recognition via Web Security Measures |author=von Ahn, Luis; Benjamin Maurer, Colin McMillen, David Abraham, and Manuel Blum |access-date=12 May 2022}}</ref>