Emerging meta*Discipline: Collaborative Intelligence
- 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
- Human Computation engages the pattern-recognizing capacities of anonymous human microtask workers to improve on machine capabilities.
- Collaborative intelligence complements these domains, offering principles and frameworks to tap diverse expertise, autonomy and pattern recognition of non-anonymous contributors, from tagged sensors to geo-located devices to identified human experts in next generation social networks for collaborative problem-solving.
Are we are on the verge of a Kuhnian paradigm shift driven by the need to process the explosion of data? To understand complex systems and to advance evolutionary computing, we need better understanding of how living organisms act as distributed information processors in their ecosystems. Humanity’s success as a technological species demands that we become wiser decision-makers, moving beyond competing for advantage toward collaborating to create a thriving civilization that sustains Planet Earth.
Better understanding of how ecosystems co-evolve in nature offers principles to build human / computational problem-solving ecosystems manifesting collaborative intelligence. To design and test alternative modes of communication, visualization, and knowledge exchange in learning networks, requires analysis of the principles of effective learning to improve performance in collaborative intelligence ecosystems.
Gill, Zann. 2013. Wikipedia: Case Study of Innovation Harnessing Collaborative Intelligence. In:The Experimental Nature of New Venture Creation: Capitalizing on Open Innovation 2.0, edited by Martin Curley and Piero Formica. Dordrecht, Springer.
Gill, Zann. 2012. User-Driven Collaborative Intelligence: Social Networks as Crowdsourcing Ecosystems. ACM CHI (Computer Human Interaction). May 5 – 10, 2012. Austin Texas.
Gill, Zann. 2011. Collaborative Intelligence in Living Systems: algorithmic implications of evo-devo debates. GECCO 2011. International Conference on Genetic and Evolutionary Computation (combining the 20th International Conference on Genetic Algorithms ICGA and the 16th Annual Genetic Programming Conference. July 12 – 16. Dublin, Ireland. Also Session Chair at that Conference.
Gill, Zann. 2011. Collaborative Intelligence and Effective Complexity. ICCS 2011 (International Conference on Complex Systems). June 26 – 30. Boston, MA. Also Session Chair at that Conference.