Editing Collaborative intelligence (section)

==History==
Collaborative intelligence traces its roots to the Pandemonium Architecture proposed by artificial intelligence pioneer [[Oliver Selfridge]] as a paradigm for [[learning]].<ref>Selfridge, O. (1959) Pandemonium: A paradigm for learning. ''Symposium on the mechanization of thought processes''. London: H.M. Stationery Office</ref> His concept was a precursor for the blackboard system where an opportunistic solution space, or blackboard, draws from a range of partitioned knowledge sources, as multiple players assemble a jigsaw puzzle, each contributing a piece. [[Rodney Brooks]] notes that the blackboard model specifies how knowledge is posted to a blackboard for general [[knowledge sharing|sharing]], but not how knowledge is retrieved, typically hiding from the consumer of knowledge who originally produced which knowledge,<ref>{{Cite journal |url=http://www.denizyuret.com/ref/brooks/brooks.pdf |doi=10.1016/0004-3702(91)90053-M |title=Intelligence without representation |date=1991 |last1=Brooks |first1=Rodney A. |journal=Artificial Intelligence |volume=47 |issue=1–3 |pages=139–159 |s2cid=207507849 }}</ref> so it would not qualify as a collaborative intelligence system.

In the late 1980s, [[Eshel Ben-Jacob]] began to study bacterial [[self-organization]], believing that bacteria hold the key to understanding larger biological systems. He developed new pattern-forming bacteria species, [[Paenibacillus vortex]] and [[Paenibacillus dendritiformis]], and became a pioneer in the study of social behaviors of bacteria. ''[[Paenibacillus dendritiformis|P. dendritiformis]]'' manifests a collective faculty, which could be viewed as a precursor of collaborative intelligence, the ability to switch between different morphotypes to adapt with the environment.<ref>{{cite journal | url=https://pubmed.ncbi.nlm.nih.gov/9891813/ | pmid=9891813 | date=1998 | last1=Ben-Jacob | first1=E. | last2=Cohen | first2=I. | last3=Gutnick | first3=D. L. | title=Cooperative organization of bacterial colonies: from genotype to morphotype | journal=Annual Review of Microbiology | volume=52 | pages=779–806 | doi=10.1146/annurev.micro.52.1.779 }}</ref><ref>{{Cite book |last1=Ben-Jacob |first1=E |title=Bacteria as Multicellular Organisms |last2=Cohen |first2=I. |publisher=Oxford University Press |year=1997 |editor-last=Shapiro |editor-first=J. A. |location=New York |pages=394–416 |chapter=Cooperative formation of bacterial patterns |editor-last2=Dworkin |editor-first2=M}}</ref> Ants were first characterized by entomologist [[William Morton Wheeler|W. M. Wheeler]] as cells of a single "superorganism" where seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism.<ref>{{Cite journal |doi=10.1002/JMOR.1050220206 |title=The ant-colony as an organism |date=1911 |last1=Wheeler |first1=William Morton |journal=Journal of Morphology |volume=22 |issue=2 |pages=307–325 |s2cid=85810040 |url=https://zenodo.org/record/1427665 }}</ref> Later research characterized some insect colonies as instances of [[collective intelligence]]. The concept of [[ant colony optimization algorithms]], introduced by [[Marco Dorigo]], became a dominant theory of [[evolutionary computation]]. The mechanisms of [[evolution]] through which species adapt toward increased functional effectiveness in their ecosystems are the foundation for principles of collaborative intelligence.

[[Swarm Intelligence|Artificial Swarm Intelligence]] (ASI) is a real-time technology that enables networked human groups to efficiently combine their knowledge, wisdom, insights, and intuitions into an emergent intelligence. Sometimes referred to as a "hive mind," the first real-time human swarms were deployed by [[Unanimous A.I.]] using a cloud-based server called "'''UNU"''' in 2014. It enables online groups to answer questions, reach decisions, and make predictions by thinking together as a unified intelligence. This process has been shown to produce significantly improved decisions, predictions, estimations, and forecasts, as demonstrated when predicting major events such as the Kentucky Derby, the Oscars, the Stanley Cup, Presidential Elections, and the World Series.<ref>{{Cite news|url=http://www.newsweek.com/swarm-intelligence-ai-algorithm-predicts-future-418707|title=Swarm Intelligence: AI Algorithm Predicts the Future|date=2016-01-25|work=Newsweek|access-date=2017-08-11|language=en}}</ref><ref>{{Cite news|url=http://www.newsweek.com/artificial-intelligence-turns-20-11000-kentucky-derby-bet-457783|title=Artificial intelligence turns $20 into $11,000 in Kentucky Derby bet|date=2016-05-10|work=Newsweek|access-date=2017-08-11|language=en}}</ref>

A type of collaborative AI was the focus of a [[DARPA]] Artificial Intelligence Exploration (AIE)<ref>{{Cite news|url=https://intelligencecommunitynews.com/darpa-launches-artificial-intelligence-exploration-program/ | title=DARPA launches artificial intelligence exploration program}}</ref> Program from 2021 to 2023. Named Shared Experience Lifelong Learning,<ref>{{Cite news| url=https://www.nextgov.com/artificial-intelligence/2021/06/darpa-wants-ai-can-learn-others-experiences/175043/ | title=DARPA Wants AI That Can Learn From Others' Experiences}}</ref> the program aimed to develop a population of agents capable of sharing a growing number of machine-learned tasks without forgetting. The vision behind this initiative was later elaborated in a Perspective in [[Nature Machine Intelligence]],<ref>{{cite journal | journal=Nature Machine Intelligence | doi=10.1038/s42256-024-00800-2 | title=A collective AI via lifelong learning and sharing at the edge | last1=Soltoggio | first1=Andrea | last2=Ben-Iwhiwhu | first2=Eseoghene | last3=Braverman | first3 = Vladimir | last4 = Eaton | first4=Eric | last5= Epstein | first5=Benjamin | last6 = Ge | first6= Yunhao | last7 = Halperin | first7 = Lucy | last8 = How | first8 = Jonathan | last9 = Itti | first9 = Laurent | last10=Jacobs |  first10=Michael | last11=Kantharaju | first11 = Pavan | last12=Le | first12=Long | last13=Lee | first13=Steven | last14=Liu | first14=Xinran | last15=Monteiro | first15=Sildomar | last16=Musliner | first16=David | last17=Nath | first17=Saptarshi | last18=Panda | first18=Priyadarshini | last19=Peridis | first19=Christos | last20 = Pirsiavash | first20 = Hamed | last21=Parekh | first21=Vishwa | last22=Roy | first22= Kaushik | last23 = Shperberg | first23=Shahaf | last24 = Siegelmann | first24 = Hava | last25=Stone | first25 = Peter | last26 = Vedder | first26 = Kyle | last27 = Wu | first27 = Jingfeng | last28 = Yang | first28 = Lin | last29 = Zheng | first29 = Guangyao | last30 = Kolouri | first30 = Soheil| date=2024 | volume=6 | issue=3 | pages=251–264 | url=https://figshare.com/articles/journal_contribution/25470358 }}</ref> which proposed a synergy between lifelong learning and the sharing of machine-learned knowledge in populations of agents. The envisioned network of AI agents promises to bring about emergent properties such as faster and more efficient learning, a higher degree of open-ended learning, and a potentially more democratic society of AI agents, in contrast to monolithic, large-scale AI systems. These research developments were deemed to implement concepts inspired by sci-fi concepts such as the [[Borg]] from [[Star Trek]], however, featuring more appealing characteristics such as individuality and autonomy.<ref>{{Cite news | url=https://www.nextgov.com/artificial-intelligence/2021/06/darpa-wants-ai-can-learn-others-experiences/175043/ | title=Scientists think 'collective AI' will resemble Star Trek's Borg — only nicer (hopefully)}}</ref> 

[[Crowdsourcing]] evolved from anonymous collective intelligence and is evolving toward credited, open source, collaborative intelligence applications that harness social networks. Evolutionary biologist Ernst Mayr noted that competition among individuals would not contribute to species evolution if individuals were typologically identical. Individual differences are a prerequisite for evolution.<ref>{{Cite book |url=https://books.google.com/books?id=5f5kg_w2dJ4C |pages=224–225 |title=Toward a New Philosophy of Biology: Observations of an Evolutionist |isbn=9780674896666 |last1=Mayr |first1=Ernst |date=1988 |publisher=Harvard University Press }}</ref> This evolutionary principle corresponds to the principle of collaborative autonomy in collaborative intelligence, which is a prerequisite for next generation platforms for crowd-sourcing. Following are examples of crowdsourced experiments with attributes of collaborative intelligence:
* SwarmSketch is a crowd-sourced art experiment.
* Galaxy Zoo is a citizen science project led by Chris Lintott at Oxford University to tap human pattern recognition capacities to catalog galaxies.
* [[DARPA Network Challenge]] explores how the Internet and social networking can support timely communication, wide-area team-building, and urgent mobilization to solve broad-scope, time-critical problems.
* Climate CoLab, spun out of MIT and its Center for Collective Intelligence.
* reCAPTCHA is a project to digitize books, one word at a time

As [[crowdsourcing]] evolves from basic pattern recognition tasks to toward collaborative intelligence, tapping the unique expertise of individual contributors in [[social networks]], constraints guide [[evolution]] toward increased functional effectiveness, co-evolving with systems to tag, credit, time-stamp, and sort content.<ref>{{cite book | chapter-url=https://dl.acm.org/doi/10.1145/2001858.2002097 | doi=10.1145/2001858.2002097 | chapter=Collaborative intelligence in living systems | title=Proceedings of the 13th annual conference companion on Genetic and evolutionary computation | date=2011 | last1=Gill | first1=Zann | pages=803–804 | isbn=9781450306904 | s2cid=1099837 }}</ref> Collaborative intelligence requires capacity for effective search, discovery, integration, visualization, and frameworks to support collaborative problem-solving.<ref name=res>[http://collaborative-intelligence.org/ Collaborative Intelligence Resources]</ref>

The collaborative intelligence technology category was established in 2022 by MURAL, a software provider of [[interactive whiteboard]] collaboration spaces for group ideation and problem-solving.<ref>IBM [https://www.ibm.com/garage/method/practices/culture/tool_mural/ MURAL: Brainstorm and refine designs online]</ref> MURAL formalized the collaborative intelligence category through the acquisition of LUMA Institute,<ref>{{Cite news|url=https://martechseries.com/content/digi-asset-mgmt/mural-acquires-luma-institute-establishes-leadership-in-collaborative-intelligence/|title=MURAL Acquires LUMA Institute, Establishes Leadership in Collaborative Intelligence|date=2022-03-29|work=MarTech Series|access-date=2022-03-11|language=en}}''</ref> an organization that trains people to be collaborative problem solvers through teaching [[human-centered design]].<ref>[https://www.luma-institute.com/about-luma/our-approach/ Our Approach | LUMA Institute]</ref> The collaborative intelligence technology category is described by MURAL as combining "collaboration design with collaboration spaces and emerging Collaboration Insights™️ ... to enable and amplify the potential of the team."<ref>MURAL (2022) [https://engage.mural.co/rs/480-LFM-113/images/principles-of-collaborative-intelligence.pdf Disconnected teams need Collaborative Intelligence], ''The Principles of Collaborative Intelligence'', 5</ref>

===Contrast with collective intelligence===
The term [[collective intelligence]] originally encompassed both collective and collaborative intelligence, and many systems manifest attributes of both. [[Pierre Lévy (philosopher)|Pierre Lévy]] coined the term "collective intelligence" in his book of that title, first published in French in 1994.<ref>Lévy P. (1994) ''L'Intelligence collective: Pour une anthropologie du cyberspace''. Paris: La Découverte.</ref> Lévy defined "collective intelligence" to encompass both collective and collaborative intelligence: "a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and in the effective mobilization of skills".<ref>Lévy, P. (1997) ''[https://dl.acm.org/citation.cfm?id=550283 Collective Intelligence: Mankind's Emerging World in Cyberspace]''. New York: Plenum Press</ref> Following publication of Lévy's book, computer scientists adopted the term collective intelligence to denote an application within the more general area to which this term now applies in computer science. Specifically, an application that processes input from a large number of discrete responders to specific, generally quantitative, questions (e.g. what will the price of [[Dynamic random-access memory|DRAM]] be next year?) [[Algorithms]] homogenize input, maintaining the traditional anonymity of survey responders to generate better-than-average predictions.

Recent dependency network studies suggest links between collective and collaborative intelligence. Partial correlation-based Dependency Networks, a new class of correlation-based networks, have been shown to uncover hidden relationships between the nodes of the network. Research by Dror Y. Kenett and his Ph.D. supervisor [[Eshel Ben-Jacob]] uncovered hidden information about the underlying structure of the [[New York Stock Exchange|U.S. stock market]] that was not present in the standard [[Stock correlation network|correlation networks]], and published their findings in 2011.<ref>Kenett et al. (2010) ''PLoS ONE'' 5(12): e15032</ref>