Intelligence itself is a measure of human decision-making ability. Exploring intelligence in humans and machines should be codified in a model. My reference model for human intelligence involves some very specific learn-and-do process sequences.
- Learn in the context of what one already knows
- Take in new facts, whether through the senses or through text (data)
- Analyze the data to categorize and file it (information)
- Take in the awareness of events
- Analyze events to determine their implications (information)
- File away information from data and events (“be-do” knowledge)
- Make decisions based upon the implications of events (action)
- Take in feedback on the quality or outcome of decisions
- Analyze decision feedback to define the best choices for next time (wisdom)
These are part of the human’s life cycle of data.
The same model applies to certain forms of intelligence:
- Human Intelligence
- Linguistics-Based Intelligence
- Artificial Intelligence
Some non-intelligent systems are often confused with intelligence itself, though they may be powerful for their specific application. They only incorporate fragments of the above and are of several classes:
- Database access
- Machine Learning (Single-Factor Correlators)
- Genetic algorithms
- Scripted decision-making (Coded Programs)
When exploring intelligence concepts, consider: Intelligence and DIKW