This site introduces a range of collaborative systems, from biological ecosystems to knowledge management systems, from multicellular organisms or collaborating cells to the semantic web, from sensor networks to quorum-sensing in bacteria.

What intelligent collaborative systems share is capacity to send and receive signals, capacity to interpret signals in order to decide what to do next. The question is how can their components, agents, sensors etc. become more individually intelligent (the autonomy component) and how can their collaboration be supported to be more effective?

Collaborative systems are needed to support collaborative intelligence.
Collaborative intelligence requires that humans and machines collaborate more effectively as pattern recognizers, allowing humans to perform the tasks where they excel and machines to perform where they excel.

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Practical Applications.

The distant (or not so distant) future vision of a system to support collaborative intelligence will be inspired by life-like systems with autonomy and capacity for pattern recognition (the A-PR Hypothesis), enabling them to navigate. Life-like capacity for pattern recognition and choice may exhibit what Babbage alluded to as a violation, an unpredictability to which the system must be able to adapt, and intelligence to respond to random unpredictable elements, accepting and adapting usable random variations. Babbage, the computer visionary, may have seen beyond the possibilities of today’s algorithmic computing to an era of intelligent computing with the unpredictabilities of life. If so, he anticipated that collaborative intelligence will require the capacity to accept and respond to unpredictability.

Innovation (or Knowledge) Networks link participants, while maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes.

Problem mapping a priori, in contrast to information visualisation after-the-fact, generates visual frameworks, or “empty constructs” to structure the process of knowledge-gathering. Problem maps can evolve into navigable user interfaces. These open frameworks (partial patterns) tap the pattern recognition capabilities of users, serving as vehicles to order incoming information in process, and for use by participants during the problem-solving process. A classic example of a problem map is Dmitri Mendeleev’s Periodic Table of Elements, which prompted chemists to look for elements that appeared logically likely to exist, based upon the pattern of the Table.

References
Tim Berners-Lee, James Hendler and Ora Lassila. 2001. The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American. May 17.

©2011 Zann Gill Please attribute, linking to this site.
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Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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