An organization’s ability to learn and translate that learning into action is the ultimate competitive advantage.
Jack Welch, Former CEO, General Electric
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.
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.
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 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
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.
- Signs & Symbols
- Generative & General Artificial Intelligence
- Thesauruses, Taxonomies, Ontologies
- EA Engineering interfaces
- Ontologies Use cases
- Cognitive Capabilities
- LLMs & the matter of transparency
- LLMs & the matter of regulations
OTHER INTERNAL REFERENCES
- Chatbots in the Galaxies of Meanings
- Things Speaking in Tongues
- What Did You Learn Last Year ?
- Brands, Bots, & Storytelling
- Transcription & Deep Learning
- Out of Mind Content Discovery
- Caminao Framework Overview
- A Knowledge Engineering Framework
- Knowledge interoperability
- Edges of Knowledge
- The Pagoda Playbook
- ABC of EA: Agile, Brainy, Competitive
- Knowledge-driven Decision-making (1)
- Knowledge-driven Decision-making (2)
- Ontological Text Analysis: Example