Tag: rule-based

Legacy AI (Rule-based)

Posted on by lechuck park

The image shows a diagram explaining “Legacy AI” or rule-based AI systems. The diagram is structured in three main sections:

  1. At the top: A workflow showing three steps:
    • “Analysis” (illustrated with a document and magnifying glass with charts)
    • “Prioritize” (shown as a numbered list with 1-2-3 and an upward arrow)
    • “Choose the best” (depicted with a network diagram and pointing hand)
  2. In the middle: Programming conditional statement structure:
    • “IF [ ]” section contains analysis and prioritization icons, representing the condition evaluation
    • “THEN [ ]” section includes “optimal choice” icons, representing the action to execute when the condition is true
    • “It’s Rule” label on the right indicates this is a traditional program code processing approach
  3. At the bottom: A pipeline process labeled “It’s Algorithm (Rule-based AI)” showing:
    • A series of interconnected components with arrows
    • Each component contains small icons representing analysis and rules
    • The process ends with “Serialize without duplications”

This diagram effectively illustrates the structure and workflow of traditional rule-based AI systems, demonstrating how they operate like conventional programming with IF-THEN statements. The system first analyzes data, then prioritizes information based on predefined criteria, and finally makes decisions by selecting the optimal choice according to the programmed rules. This represents the foundation of early AI approaches before the advent of modern machine learning techniques, where explicit rules rather than learned patterns guided the decision-making process.

With Claude

Rule-Based vs LLM AI

Posted on by lechuck park

Rule-Based AI vs. Machine Learning: Finding the Fastest Hiking Route

Rule-Based AI

  • A single expert hiker analyzes a map, considering terrain and conditions to select the optimal route.
  • This method is efficient and requires minimal energy (a small number of lunchboxes).

Machine Learning

  • A large number of hikers explore all possible paths without prior knowledge.
  • The fastest hiker’s route is chosen as the optimal path.
  • This approach requires many attempts, consuming significantly more energy (a vast number of lunchboxes).

👉 Comparison Summary

  • Rule-Based AI: Finds the best route through analysis → Efficient, low energy consumption
  • Machine Learning: Finds the best route through trial and error → Inefficient but discovers optimal paths, high energy consumption

with ChatGPT