Individual & Collective Learning

An organization’s ability to learn and translate that learning into action is the ultimate competitive advantage.

Jack Welch, Former CEO, General Electric

Scaling collective learning (Wang Qingsong)

Artificial brains seem to be coming of age: first learning about environments by touching (data mining), then babbling (chatbots), and now trying to learn together with people.

Cognitive Capabilities

An ecumenical approach to learning can be best defined by the improvement of individual and collective handling of data (facts), information (categories) and knowledge (concepts); hence the three pillars:

  • Naming relevant objects and phenomena in environments
  • Developing mental representations of environments, values and objectives
  • Developing shared symbolic representations

and the corresponding cross function:

  • Communication (facts/concepts): Exchange of meaningful signs or symbols between living organisms (plants included) and machines.
  • Classification (facts/categories): Grouping of identified objects and phenomena according to shared features
  • Reasoning (concepts/categories): Truth-preserving processing of mental and/or symbolic representations.

Learning Configurations

Assuming that learning is pushing the edges of knowledge, six configurations can be defined:

Intrinsic improvements are obtained through a more effective use of resources:

  • Practical knowledge: from actions and observations
  • Shared knowledge: from a better management of shared symbolic representations
  • Personal knowledge: from individual studying

Cross improvements are obtained through synergies:

  • Theory: alignment of concepts and categories
  • Practice: applying categories to facts, and honing both
  • Collaboration: direct exchanges of ideas and modus operandi

Learning capabilities can also be leveraged across individual and collective levels.

Learning Primitives

Learning operates through four basic mechanisms:

  • Observation of facts carried out by people and/or machines
  • Experience, a mix of observation and action carried out by people and/or machines
  • Reasoning about facts, categories, and concepts, carried out by people and/or machines
  • Judgment about facts, categories, and concepts, carried out only by people

The weaving of individual and collective learning would be achieved with the former operating clockwise and the latter counter-clockwise.

Implicit & Explicit Knowledge

For organizations, learning raises a two-pronged challenge as it must bridge the gaps between individual and collective knowledge on the one hand, between people and systems agency on the other hand. That conundrum can be sorted out by expressing learning in terms of symbolic and non-symbolic knowledge:

  • Symbolic knowledge is explicit, represented by models and/or knowledge graphs
  • Non-symbolic knowledge is implicit, embodied in individual know-how, collaboration routines, and neural networks

Assuming that symbolic knowledge can be implemented through systems, the objective could be to use languages to process implicit contents into explicit knowledge, typically:

  • Natural languages, for learning through conversations
  • Empiric languages, which apply statistics and machine learning to actual observations in order to build descriptive and/or predictive models of environments
  • Generative languages, which do the same to textual realms in order to build semantic networks
Learning pathways through systems (right) and organization (left)

While the theoretical and pragmatic bases of that approach are well established for the system path through empiric and formal languages, that’s not the case for the organizational path due to the generative languages’ lack of symbolic dimension. Hence the flurry of initiatives towards foundation languages meant to harness generative and knowledge graph technologies.




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