An expert system in AI is a computer program that mimics the decision-making abilities of a human expert in a specific domain. It uses a “knowledge base” of facts and rules to draw inferences and make decisions.
Expert systems are commonly used in fields like medical diagnosis, financial analysis, and troubleshooting.
🧠 Expert Systems vs. Machine Learning vs. Deep Learning
| Feature | Expert Systems | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|---|
| Definition | Rule-based systems using “if-then” logic | Systems that learn from data to make predictions or decisions | Subset of ML using multi-layered neural networks |
| Learning Capability | ❌ Not self-learning | ✅ Learns patterns from data | ✅✅✅ Learns complex patterns from large datasets |
| Transparency | ✅ Fully explainable (rule-based) | ⚠️ Partially explainable (“black box” risk) | ❌ Often a black box, hard to interpret |
| Data Dependency | ❌ No data needed (uses expert knowledge) | ✅ Needs quality training data | ✅✅ Requires large volumes of data and computational power |
| Examples | Medical diagnosis system with fixed rules | Spam detection, credit scoring | Face recognition, speech-to-text, ChatGPT |
| Strengths | Predictable, easy to audit | Adaptable, can improve with more data | Powerful with unstructured data (images, language, audio, etc.) |
| Limitations | Hard to scale, can’t adapt to new situations | May need retraining, harder to interpret | Opaque decision-making, resource-intensive |