Motifs & Motives
Assuming that intelligence is the capacity to learn, three kind of motifs should be considered:
- Data, for facts from physical and symbolic environments
- Information, for categories giving structure and meaning to data
- Knowledge, for concepts adding purposes to information
These motifs serve three kinds of motives: communication between agents; representation of contexts, concerns, and conversations; imagination of alternatives realities.
That taxonomy neatly coincides with Spinoza’s philosophical one: data for senses and beliefs, information for reason, and knowledge for judgment..
Intelligence can thus be characterized by four cognitive operations:
- Deduction: using observations (data) and models (information) to establish new facts
- Induction: making hypothesises (knowledge) on the matching between models (information) and observations (data)
- Abduction: assessing inductions
- Intuition: making hypothesises (knowledge) from direct observations
Induction, deduction, and abduction may arguably be performed by artificial brains providing traceability; that’s not the case for intuition because it involves accountability.
The 3*4 taxonomy of intelligence’s motifs and threads can then be used to characterize brains’ capabilities in terms of problem solving.
To begin with the representation of issues:
- Symbolic representation can rely on functions (data), models (information), and semantic or conceptual graphs (explicit knowledge)
- Alternatively, non symbolic representation can start with digits (data), documents (information), and neural networks (implicit knowledge)
Problem solving itself will make use of:
- Symbolic representation: deduction (data), induction (information), and abduction (knowledge)
- Non symbolic representation: data analytics (data), pattern matching (information), and Machine learning (implicit knowledge)
Capabilities can then be used to draw a line between natural and artificial brains.
Artificial vs Natural Brains
The enterprises immersion in digital environments combined with the ubiquity of Artificial intelligence and Machine learning technologies have put traceability and accountability on top of design issues; hence the need to set the limits of artificial brains:
- Artificial brains can build and process symbolic and non symbolic representations
- Artificial brains can solve problems applying logic and abstraction or
data analytics, respectively to symbolic and non symbolic representations
- Like natural ones, artificial brains combine sensory-motor capabilities with purely cognitive ones
- Like human ones, and apart from sensory-motor capabilities, artificial brains support a degree of cognitive plasticity and versatility between symbolic and non symbolic representation and processing.
While artificial brains’ capabilities appear to match human ones, that’s not the case fall when shortfalls in traceability (e.g., with abduction or intuitions) must be supplemented by judgements’ accountability.
Artificial General Intelligence
While still an open-ended field of research, the debate about Artificial ‘General’ Intelligence (AGI) can be summarized in terms of scope or agents.
Regarding scope the debate turns around the ability of Machine learning technologies to:
- Reproduce human implicit knowledge and intuition
- Deal consciously with actual and virtual realities
As both issues remain shrouded in the mystery of individual consciousness, it might be worth to consider the collective dimension of general intelligence. Applying the principle to enterprises:
- Motifs: intelligence rooted in operations (facts), organization (concepts) and systems (categories)
- Motives: intelligence driven by business processes (communication), information management (representation), and planning and decision-making (imagination)
Implicit knowledge and intuition would be rooted in data and process mining, and weaved with the fabric of consciousness built from a continuous adjustment of managed (actual) and planned (virtual) realities.
- AI & Embedded Insanity
- Detour from Turing Game
- Self-driving Cars & Turing’s Imitation Game
- AlphaGo: From Intuitive Learning to Holistic Knowledge
- AlphaGo & Non-Zero-Sum Contests
- New Year: 2016 is the One to Learn
- 2018: Clones vs Octopuses
- Marco Iansiti, Karim R. Lakhani, “Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World”