Squared Outline: Intelligence in 3*4

Three Motifs

Assuming that intelligence is the capacity to learn, three kind of motifs should be considered:

• Data, for observations from the environment
• Information, for structures and semantics applied to data
• Knowledge, for information put to use 

That taxonomy neatly coincides with Spinoza’s philosophical one: data for senses and beliefs, information for reason, and knowledge for judgment..

Four Threads

Brains’ processing capacities can thus be defined 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.

Brains’ Capabilities

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.

FURTHER READING

• 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

Books

•  Marco Iansiti, Karim R. Lakhani, “Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World”