Collaborative Intelligence - Precursors in The Life Sciences

Precursors in The Life Sciences

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 an intriguing collective faculty, which could be viewed as a precursor of collaborative intelligence, the ability to switch between different morphotypes to better adapt with the environment. Primarily studied are the transitions between the branching (or tip-splitting) morphotype and the chiral morphotype, which is marked by curly branches with well defined handedness. Morphotype transitions can be viewed as identity switching In order to make these switches colonies of bacteria must cooperatively make drastic alterations of their internal genomic state, effectively transforming themselves into cells that look and behave differently in order to generate colonies with an entirely different organization. Scientists have only recently begun to decode, how, using sophisticated chemical communication, bacteria can rapidly adapt to changes in the environment, distribute tasks, learn from experience, prepare for the future and make decisions. Bacteria in a colony, numbering many times the population on Earth, exchange “chemical tweets” to synchronize their behavior.

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. 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. Deborah M. Gordon shows that ant colonies operate without central control using algorithms based on a dynamical network of brief interactions. Colonies allocate workers to different tasks, and workers switch from one task to another in response to changing conditions.

The mechanisms of evolution through which species adapt toward increased functional effectiveness in their ecosystems are the foundation for principles of collaborative intelligence. Topics such as stigmergy and evolutionary genetic algorithms are inspired by the life sciences.