Ынтымақтастық интеллектісі: Жаңа буын әлеуметтік желілер мен мәселе шешу экосистемасы

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. Four related terms are complementary:
Collective intelligence processes input from a large number of anonymous responders to quantitative questions to produce better than average predictions. Crowdsourcing distributes microtasks to a large number of anonymous task performers. Human Computation engages the pattern recognizing capacities of anonymous human microtask workers to improve on machine capabilities and enable machine learning. Collaborative intelligence complements the three methods defined above, but here task performers are not anonymous. Task performers have different skills, motivations and may perform different tasks. These non anonymous devices and human contributors, from tagged sensors to geo located devices to identified unique human contributors, drive collaborative problem solving in next generation social networks.

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" where Collaborative Intelligence, or CQ, is "the ability to build, contribute to and manage power found in networks of people." 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.

Although many sources warn that AI may cause the extinction of the human species, 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. 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.” 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, to an assessment of cloud computing tradeoffs. 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. 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. 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.

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. 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". 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.