Three major paradigms of artificial intelligence
Posted: Tue Jul 07, 2026 1:10 am
AI writes to me:
The provided code demonstrates the conceptual differences between three major paradigms of artificial intelligence:
* **Rule-Based AI (`TRuleBasedAI`)**: This represents the "Classical AI" or Expert Systems. It relies on fixed logic provided by the programmer. It is efficient but lacks the ability to handle scenarios not explicitly coded.
* **Learning AI (`TLearningAI`)**: This simulates a basic connectionist model (like a single-neuron perceptron). It uses a feedback loop (training) to adjust internal parameters (`FWeight` and `FBias`). This allows the system to "learn" a relationship between input and output without being told the specific rule.
* **Imagination/Generative AI (`TImaginationAI`)**: This demonstrates how AI can "design" or "imagine." Instead of just mapping inputs to outputs, it uses stochastic (randomized) processes to mutate existing patterns or explore a design space, which is the foundation of modern Generative AI and Evolutionary Algorithms.
Key Concepts in the Code:
* **Encapsulation**: Each AI type is encapsulated in its own class, inheriting from a base `TIntelligenceEngine`.
* **Gradient Descent Simulation**: The `Train` procedure in `TLearningAI` uses a simplified version of the delta rule to minimize error.
* **Stochastic Design**: The `ImagineNewDesign` function shows how "imagination" in code is often the result of combining deterministic patterns with controlled randomness (entropy).
The provided code demonstrates the conceptual differences between three major paradigms of artificial intelligence:
* **Rule-Based AI (`TRuleBasedAI`)**: This represents the "Classical AI" or Expert Systems. It relies on fixed logic provided by the programmer. It is efficient but lacks the ability to handle scenarios not explicitly coded.
* **Learning AI (`TLearningAI`)**: This simulates a basic connectionist model (like a single-neuron perceptron). It uses a feedback loop (training) to adjust internal parameters (`FWeight` and `FBias`). This allows the system to "learn" a relationship between input and output without being told the specific rule.
* **Imagination/Generative AI (`TImaginationAI`)**: This demonstrates how AI can "design" or "imagine." Instead of just mapping inputs to outputs, it uses stochastic (randomized) processes to mutate existing patterns or explore a design space, which is the foundation of modern Generative AI and Evolutionary Algorithms.
Key Concepts in the Code:
* **Encapsulation**: Each AI type is encapsulated in its own class, inheriting from a base `TIntelligenceEngine`.
* **Gradient Descent Simulation**: The `Train` procedure in `TLearningAI` uses a simplified version of the delta rule to minimize error.
* **Stochastic Design**: The `ImagineNewDesign` function shows how "imagination" in code is often the result of combining deterministic patterns with controlled randomness (entropy).