Collaborative intelligence

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Collaborative intelligence characterizes multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem solving network. Collaborative autonomy of organisms in their ecosystems makes evolution possible. Natural ecosystems, where each organism's unique signature is derived from its genetics, circumstances, behavior and position in its ecosystem, offer principles for design of next generation social networks to support collaborative intelligence, crowdsourcing individual expertise, preferences, and unique contributions in a problem solving process. [1]

Contents

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 multi-agent problem-solving systems. The term was used in 1999 to describe the behavior of an intelligent business "ecosystem" [2] where Collaborative Intelligence, or CQ, is "the ability to build, contribute to and manage power found in networks of people." [3] When the computer science community adopted the term collective intelligence and gave that term a specific technical denotation, a complementary term was needed to distinguish between anonymous homogeneity in collective prediction systems and non-anonymous heterogeneity in collaborative problem-solving systems. Anonymous collective intelligence was then complemented by collaborative intelligence, which acknowledged identity, viewing social networks as the foundation for next generation problem-solving ecosystems, modeled on 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]

A type of collaborative AI was the focus of a DARPA Artificial Intelligence Exploration (AIE) [19] Program from 2021 to 2023. Named Shared Experience Lifelong Learning [20] , 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 [21] , 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 [22] .

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. [23] 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:

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. [24] Collaborative intelligence requires capacity for effective search, discovery, integration, visualization, and frameworks to support collaborative problem-solving. [25]

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. [26] MURAL formalized the collaborative intelligence category through the acquisition of LUMA Institute, [27] an organization that trains people to be collaborative problem solvers through teaching human-centered design. [28] 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." [29]

Contrast with collective intelligence

The term collective intelligence originally encompassed both collective and collaborative intelligence, and many systems manifest attributes of both. Pierre Lévy coined the term "collective intelligence" in his book of that title, first published in French in 1994. [30] 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". [31] 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 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 U.S. stock market that was not present in the standard correlation networks, and published their findings in 2011. [32]

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:

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. [33]

A new generation of tools to support collaborative intelligence is poised to evolve from crowdsourcing platforms, recommender systems, and evolutionary computation. [25] Existing tools to facilitate group problem-solving include collaborative groupware, synchronous conferencing technologies such as instant messaging, online chat, and shared white boards, which are complemented by asynchronous messaging like electronic mail, threaded, moderated discussion 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. [34]

See also

Related Research Articles

Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.

Distributed artificial intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.

In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.

<span class="mw-page-title-main">Multi-agent system</span> Built of multiple interacting agents

A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.

Social computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. Thus, blogs, email, instant messaging, social network services, wikis, social bookmarking and other instances of what is often called social software illustrate ideas from social computing.

The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

<i>The Wisdom of Crowds</i> 2004 book by James Surowiecki

The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology.

<span class="mw-page-title-main">Intelligent agent</span> Software agent which acts autonomously

In intelligence and artificial intelligence, an intelligent agent (IA) is an agent that perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.

<span class="mw-page-title-main">History of artificial intelligence</span>

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or even decades later.

Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking 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 systems. In this case, human-based computation has been referred to as human-aided artificial intelligence.

The following outline is provided as an overview of and topical guide to artificial intelligence:

Social information processing is "an activity through which collective human actions organize knowledge." It is the creation and processing of information by a group of people. As an academic field Social Information Processing studies the information processing power of networked social systems.

<span class="mw-page-title-main">Collective intelligence</span> Group intelligence that emerges from collective efforts

Collective intelligence (CI) is shared or group intelligence (GI) that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. Collective IQ is a measure of collective intelligence, although it is often used interchangeably with the term collective intelligence. Collective intelligence has also been attributed to bacteria and animals.

Lateral computing is a lateral thinking approach to solving computing problems. Lateral thinking has been made popular by Edward de Bono. This thinking technique is applied to generate creative ideas and solve problems. Similarly, by applying lateral-computing techniques to a problem, it can become much easier to arrive at a computationally inexpensive, easy to implement, efficient, innovative or unconventional solution.

<span class="mw-page-title-main">Social collaboration</span>

Social collaboration refers to processes that help multiple people or groups interact and share information to achieve common goals. Such processes find their 'natural' environment on the Internet, where collaboration and social dissemination of information are made easier by current innovations and the proliferation of the web.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

Cognitive city is a term which expands the concept of the smart city with the aspect of cognition or refers to a virtual environment where goal-driven communities gather to share knowledge. A physical cognitive city differs from conventional cities and smart cities in the fact that it is steadily learning through constant interaction with its citizens through advanced information and communications technologies (ICT) based ICT standards and that, based on this exchange of information, it becomes continuously more efficient, more sustainable and more resilient. A virtual cognitive city differs from social media platforms and project management platforms in that shared data is critical for the group's performance, and the community consists of members spanning diverse expertise, backgrounds, motivations, and geographies but with a common desire to solve large problems. The virtual cognitive city is steadily learning through constant metadata generated by activity in the user community.

Computational psychometrics is an interdisciplinary field fusing theory-based psychometrics, learning and cognitive sciences, and data-driven AI-based computational models as applied to large-scale/high-dimensional learning, assessment, biometric, or psychological data. Computational psychometrics is frequently concerned with providing actionable and meaningful feedback to individuals based on measurement and analysis of individual differences as they pertain to specific areas of enquiry.

References

  1. ↑ Gill, Zann (2012). "User-driven collaborative intelligence". CHI '12 Extended Abstracts on Human Factors in Computing Systems. pp. 161–170. doi:10.1145/2212776.2212794. ISBN   9781450310161. S2CID   15027953.
  2. ↑ Isaacs, William (1999). Dialogue: The Art Of Thinking Together. Crown Business. ISBN   978-0-385-47999-8.
  3. ↑ Joyce, Stephen (2007). Teaching an Anthill to Fetch: Developing Collaborative Intelligence @ Work . Crown Business. ISBN   978-0-9780312-0-6.
  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.
  6. ↑ Gill, Zann (2012). "User-driven collaborative intelligence". CHI '12 Extended Abstracts on Human Factors in Computing Systems. pp. 161–170. doi:10.1145/2212776.2212794. ISBN   9781450310161. S2CID   15027953.
  7. ↑ Vinanzi, S.; Cangelosi, A.; Goerick, C. (2021). "The collaborative mind: Intention reading and trust in human-robot interaction". iScience. 24 (2): 102130. Bibcode:2021iSci...24j2130V. doi:10.1016/j.isci.2021.102130. PMC   7890414 . PMID   33659886.
  8. ↑ Magalhães, W. F.; Farias, M. C. De; Gomes, H. M.; Marinho, L. B.; Aguiar, G. S.; Silveira, P. (June 2020). "Evaluating Edge-Cloud Computing Trade-Offs for Mobile Object Detection and Classification with Deep Learning". Journal of Information and Data Management. 11 (1): 3–19. doi: 10.5753/jidm.2020.2026 . ISSN   2178-7107.
  9. ↑ Ng, Provides (2022). "Preemptive Futures: Entropic and Negentropic Information in Speculative Design". Proceedings of the 10th Conference on Computation, Communication, Aesthetics & X: 85. doi: 10.24840/xCoAx_2022_16 .
  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. ↑ Brooks, Rodney A. (1991). "Intelligence without representation" (PDF). Artificial Intelligence. 47 (1–3): 139–159. doi:10.1016/0004-3702(91)90053-M. S2CID   207507849.
  14. ↑ Ben-Jacob, E.; Cohen, I.; Gutnick, D. L. (1998). "Cooperative organization of bacterial colonies: from genotype to morphotype". Annual Review of Microbiology. 52: 779–806. doi:10.1146/annurev.micro.52.1.779. PMID   9891813.
  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. ↑ Wheeler, William Morton (1911). "The ant-colony as an organism". Journal of Morphology. 22 (2): 307–325. doi:10.1002/JMOR.1050220206. S2CID   85810040.
  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. ↑ "DARPA launches artificial intelligence exploration program".
  20. ↑ "DARPA Wants AI That Can Learn From Others' Experiences".
  21. ↑ Soltoggio, Andrea; Ben-Iwhiwhu, Eseoghene; Braverman, Vladimir; Eaton, Eric; Epstein, Benjamin; Ge, Yunhao; Halperin, Lucy; How, Jonathan; Itti, Laurent; Jacobs, Michael; Kantharaju, Pavan; Le, Long; Lee, Steven; Liu, Xinran; Monteiro, Sildomar; Musliner, David; Nath, Saptarshi; Panda, Priyadarshini; Peridis, Christos; Pirsiavash, Hamed; Parekh, Vishwa; Roy, Kaushik; Shperberg, Shahaf; Siegelmann, Hava; Stone, Peter; Vedder, Kyle; Wu, Jingfeng; Yang, Lin; Zheng, Guangyao; Kolouri, Soheil. "A collective AI via lifelong learning and sharing at the edge". Nature Machine Intelligence. doi:10.1038/s42256-024-00800-2.
  22. ↑ "Scientists think 'collective AI' will resemble Star Trek's Borg — only nicer (hopefully)".
  23. ↑ Mayr, Ernst (1988). Toward a New Philosophy of Biology: Observations of an Evolutionist. Harvard University Press. pp. 224–225. ISBN   9780674896666.
  24. ↑ Gill, Zann (2011). "Collaborative intelligence in living systems". Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. pp. 803–804. doi:10.1145/2001858.2002097. ISBN   9781450306904. S2CID   1099837.
  25. 1 2 Collaborative Intelligence Resources
  26. ↑ IBM MURAL: Brainstorm and refine designs online
  27. ↑ "MURAL Acquires LUMA Institute, Establishes Leadership in Collaborative Intelligence". MarTech Series. 2022-03-29. Retrieved 2022-03-11.
  28. ↑ Our Approach | LUMA Institute
  29. ↑ MURAL (2022) Disconnected teams need Collaborative Intelligence, The Principles of Collaborative Intelligence, 5
  30. ↑ Lévy P. (1994) L'Intelligence collective: Pour une anthropologie du cyberspace. Paris: La Découverte.
  31. ↑ Lévy, P. (1997) Collective Intelligence: Mankind's Emerging World in Cyberspace . New York: Plenum Press
  32. ↑ Kenett et al. (2010) PLoS ONE 5(12): e15032
  33. ↑ 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.
  34. ↑ Evans, J.A. and Foster, J.G. (2011) Metaknowledge. Science. vol. 331. 11 February. pp. 721–725.
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