Collaborative intelligence

Source: Wikipedia, the free encyclopedia.
Not to be confused with collective intelligence.

Collaborative intelligence characterizes

social networks to support collaborative intelligence, crowdsourcing individual expertise, preferences, and unique contributions in a problem solving process.[1]

Four related terms are complementary:

Overview

Collaborative intelligence is a term used in several disciplines. In business it describes heterogeneous networks of people interacting to produce intelligent outcomes. It can also denote non-autonomous

evolutionary adaptation
in nature's ecosystems.

AI and Collaborative Intelligence

Although many sources warn that AI may cause the extinction of the human species,[4] humans may cause our own extinction via climate change, ecosystem disruption, decline of our ocean lifeline, increasing mass murders and police brutality, and an arms race that could trigger World War III, driving humanity extinct before AI gets a chance. The surge of open source applications in generative AI demonstrates the power of collaborative intelligence (AI-human C-IQ) among distributed, autonomous agents, sharing achievements in collaborative partnerships and networks. The successes of small open source experiments in generative AI provide a model for a paradigm shift from centralized, hierarchical control to decentralized bottom-up, evolutionary development.[5] The key role of AI in collaborative intelligence was predicted in 2012 when Zann Gill wrote that collaborative intelligence (C-IQ) requires “multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem-solving network.”[6] Gill’s ACM paper has been cited in applications ranging from an NIH (U. S. National Institute of Health) Center for Biotechnology study of human robot collaboration,[7] to an assessment of cloud computing tradeoffs.[8] A key application domain for collaborative intelligence is risk management, where preemption is an anticipatory action taken to secure first-options in maximising future gain and/or minimising loss.[9] Prediction of gain/ loss scenarios can increasingly harness AI analytics and predictive systems designed to maximize collaborative intelligence. Other collaborative intelligence applications include the study of social media and policing, harnessing computational approaches to enhance collaborative action between residents and law enforcement.[10] In their Harvard Business Review essay, Collaborative Intelligence: Humans and AI Are Joining Forces – Humans and machines can enhance each other’s strengths, authors H. James Wilson and Paul R. Daugherty report on research involving 1,500 firms in a range of industries, showing that the biggest performance improvements occur when humans and smart machines work together, enhancing each other’s strengths.[11]

History

Collaborative intelligence traces its roots to the Pandemonium Architecture proposed by artificial intelligence pioneer Oliver Selfridge as a paradigm for learning.[12] 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 sharing, but not how knowledge is retrieved, typically hiding from the consumer of knowledge who originally produced which knowledge,[13] 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. 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.[14][15] Ants were first characterized by entomologist 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.[16] 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.

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.[17][18]

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.[19] 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

social networks, constraints guide evolution toward increased functional effectiveness, co-evolving with systems to tag, credit, time-stamp, and sort content.[20] Collaborative intelligence requires capacity for effective search, discovery, integration, visualization, and frameworks to support collaborative problem-solving.[21]

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.[22] MURAL formalized the collaborative intelligence category through the acquisition of LUMA Institute,[23] an organization that trains people to be collaborative problem solvers through teaching human-centered design.[24] 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."[25]

Contrast with collective intelligence

The term

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 U.S. stock market that was not present in the standard correlation networks, and published their findings in 2011.[28]

Application

Collaborative intelligence addresses problems where individual expertise, potentially conflicting priorities of stakeholders, and different interpretations of diverse experts are critical for problem-solving. Potential future applications include:

  • competitions, where submissions must be integrated to produce a synergistic outcome;
  • smart search, where social networks of searchers on related topics co-define search results;
  • professional groups, interest collectives, citizen science and other communities, where knowledge-sharing is a prerequisite for effective outcomes;
  • planning, development, and sustainable project management;
  • smart systems to transform independent cities into collaborative, ecological urban networks

Wikipedia, one of the most popular websites on the Internet, is an exemplar of an innovation network manifesting distributed collaborative intelligence that illustrates principles for experimental business laboratories and start-up accelerators.[29]

A new generation of tools to support collaborative intelligence is poised to evolve from crowdsourcing platforms,

forums, web logs, and group Wikis. Managing the Intelligent Enterprise relies on these tools, as well as methods for group member interaction; promotion of creative thinking; group membership feedback; quality control and peer review; and a documented group memory or knowledge base. As groups work together, they develop a shared memory, which is accessible through the collaborative artifacts created by the group, including meeting minutes, transcripts from threaded discussions, and drawings. The shared memory (group memory) is also accessible through the memories of group members; current interest focuses on how technology can support and augment the effectiveness of shared past memory and capacity for future problem-solving. Metaknowledge characterizes how knowledge content interacts with its knowledge context in cross-disciplinary, multi-institutional, or global distributed collaboration.[30]

See also

References

  1. S2CID 15027953
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  4. ^ Mitchell, Melanie (2023-04-23). "Do half of AI researchers believe that there's a 10% chance AI will kill us all?". AI: A Guide for Thinking Humans.
  5. ^ Patel, Dylan; Ahmad, Afzal (2023-05-04). "Google "We Have No Moat, And Neither Does OpenAI"". www.semianalysis.com. Retrieved 2023-08-07.
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  10. ^ Sachdeva, Niharika; Kumaraguru, Ponnurangam (Advisor) (April 2017). Social media and policing : Computational approaches to enhancing collaborative action between residents and law enforcement (PhD thesis). IIIT-Delhi. {{cite thesis}}: |first2= has generic name (help)
  11. ^ Wilson, James H.; Daugherty, Paul R. (July 2018). "Collaborative Intelligence: Humans and AI Are Joining Forces". Harvard Business Review (published July–August 2018): 114–123.
  12. ^ Selfridge, O. (1959) Pandemonium: A paradigm for learning. Symposium on the mechanization of thought processes. London: H.M. Stationery Office
  13. S2CID 207507849
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  14. PMID 9891813
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  15. ^ Ben-Jacob, E; Cohen, I. (1997). "Cooperative formation of bacterial patterns". In Shapiro, J. A.; Dworkin, M (eds.). Bacteria as Multicellular Organisms. New York: Oxford University Press. pp. 394–416.
  16. S2CID 85810040
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  17. ^ "Swarm Intelligence: AI Algorithm Predicts the Future". Newsweek. 2016-01-25. Retrieved 2017-08-11.
  18. ^ "Artificial intelligence turns $20 into $11,000 in Kentucky Derby bet". Newsweek. 2016-05-10. Retrieved 2017-08-11.
  19. ISBN 9780674896666
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  20. S2CID 1099837
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  21. ^ a b Collaborative Intelligence Resources
  22. ^ IBM MURAL: Brainstorm and refine designs online
  23. ^ "MURAL Acquires LUMA Institute, Establishes Leadership in Collaborative Intelligence". MarTech Series. 2022-03-29. Retrieved 2022-03-11.
  24. ^ Our Approach | LUMA Institute
  25. ^ MURAL (2022) Disconnected teams need Collaborative Intelligence, The Principles of Collaborative Intelligence, 5
  26. ^ Lévy P. (1994) L'Intelligence collective: Pour une anthropologie du cyberspace. Paris: La Découverte.
  27. ^ Lévy, P. (1997) Collective Intelligence: Mankind's Emerging World in Cyberspace. New York: Plenum Press
  28. ^ Kenett et al. (2010) PLoS ONE 5(12): e15032
  29. ^ Gill, Zann (2013). Wikipedia: Case Study of Innovation Harnessing Collaborative Intelligence. In: Martin Curley and Piero Formica (Editors). The Experimental Nature of Venture Creation: Capitalizing on Open Innovation 2.0. NY: Springer.
  30. ^ Evans, J.A. and Foster, J.G. (2011) Metaknowledge. Science. vol. 331. 11 February. pp. 721–725.
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