Collaborative Intelligence – Key for next generation AI systems

  1. Why Collaborate

Collaboration is a pervasive need of human civilization evolving towards higher and higher intelligence. It accounts for the ability to build, contribute to, and manage power found in networks of people. It enables powerful augmentation of systems to sustain and evolve. The entire history of natural evolution – for humans and non-humans, underlines the strong foundation of augmentation. This augmentation includes any natural or artificial system, from genomes, multicellular organisms, social insects, and human societies to the networks of sensors, computers, robots, and factories. Collaboration is one of the basic principles for sustainability and evolution of any organization of natural or artificial entities. Practically, collaboration is enabled through sharing information, resources, and responsibilities by distributed agents to jointly plan, implement, and analyse the activities required to achieve individual and common goals. Tangible benefits of collaboration is enhanced intelligence resulting in achieving something which is otherwise not possible for individuals, and/or with lesser efforts, and/or with lesser time, and/or with lesser cost, etc.

  1. What is Collaboration

Fundamentally, collaboration is “the action of working with someone to produce or create something” – Oxford dictionary. To illustrate, humans work with machines (e.g., driver-car; pilot-aircraft), e-Business parties work with each other (e.g., clients-servers; buyers-sellers), all to create benefits that are/may not be achievable individually. These are the examples where activities necessitate collaboration among the entities (known as mandatory collaboration). There are some other activities which do not necessarily rely on but can be improved by collaboration (known as optional collaboration). Thus, mutual benefits are the driving potential for collaboration. Examples listed are the illustrations of artificial collaboration driven by the desire for mutual benefits. Whereas, most of the natural examples of collaboration are driven by ‘Natural Instincts’ resulting in varied forms of benefits.

(a) As exhibited by Nature

(b) Artificial examples

Intelligence augmentation through Collaboration

More interestingly, collaboration does not always take place among peers or entities of the same party. An alternative definition for collaboration is “to give help to an enemy who has invaded your country during a war” – Meriam-Webster dictionary. This implies that it may even be beneficial for competitors to collaborate (e.g. supply/logistics networks).

  1. Design paradigm for Collaborative Intelligence

While realizing the collaborative systems in practice for better intelligence, it is important to understand the paradigm involved. Though collaboration may somehow overlap with the definitions of other related terminologies such as coordination and cooperation, the system design pyramid finely distinguishes between these terms.

  • Coordination
    • Involves the use of communication and information exchange to reach mutual benefits among entities through working harmoniously.
  • Cooperation
    • Involves, besides all aspects of coordination, a resource-sharing dimension to support goal achievement.
  • Collaboration
    • Involves the functionalities of both coordination and cooperation, and refers to the sharing of information, resources, and responsibilities among entities to jointly plan, execute, and analyse the activities required to achieve individual and common goals.
  1. Ratiocination

The original vision for artificial intelligence was the simulation of (implicitly human) intelligence. Gradually research has shifted to distributed, self-driven, autonomous systems that compete with people. “A group, in the right circumstances, can be smarter than its smartest member” – Following such simple fundamental principles, current trends are towards generating innovation through collective consciousness and intelligence. Such collective, collaborative intelligent systems essentially exhibit the three prime features –

  1. Effectively adaptive in uncertain and unknown environments
  2. Organise themselves, and
  3. Exhibit so-called ‘emergent’ behavior.

These are the applications which ‘work by themselves’ and hence becoming more popular for industrial automation requirements. Keep on watching this space for illustrations of these concepts with real-life use cases.

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Umesh Hivarkar


Multidisciplinary skills with a unique combination of scientific R&D, academics, and industry profile. Doctoral research in fiber optics & sensors, Instrumentation, Integrated optics, and Modeling & Simulation. Spanning over 24+ years of application-oriented R&D and multi-sector industrial experience. Expertise in Engineering Analytics, automation, and control, and Advanced Intelligent Systems. Known for innovative solutioning and industrialization of cutting edge technologies.

More on Umesh Hivarkar.

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  1. Hanumant Gade June 13, 2019 Reply

    Nice and informative one