Traditional Complex Algorithm:
Characteristics:
- Explicitly programmed logic - every step is defined by humans
- Deterministic - same input always produces same output
- Rule-based - follows pre-written instructions
- No learning - doesn’t change behavior based on experience 💡
Examples:
- Sorting algorithms (QuickSort, MergeSort)
- Encryption algorithms (AES, RSA)
- Path-finding algorithms (Dijkstra’s algorithm)
- Complex financial calculations
AI Model:
Characteristics:
- Learned behavior - patterns discovered from data, not explicitly programmed
- Probabilistic - can give different outputs for same input
- Adaptive - behavior emerges from training, not rules
- Pattern recognition - finds relationships humans didn’t explicitly define
Examples:
- Image recognition neural networks
- Language models like Claude Sonnet 4 or GPT 5
- Recommendation systems
Key Differences:
1. Origin of behavior:
- Algorithm: “If temperature > 80°F, turn on AC” (human-written rule)
- AI Model: Learns from thousands of examples when people were comfortable
2. Flexibility:
- Algorithm: Can only handle scenarios it was explicitly programmed for
- AI Model: Can generalize to new, unseen situations
3. Explainability:
- Algorithm: You can trace every step of the logic
- AI Model: Often a “black box” - hard to explain why it made a decision
The blurry line:
Some complex algorithms incorporate learning (like genetic algorithms), and some AI models use rule-based components. The distinction isn’t always clear-cut!
Bottom line: AI models discover their own “algorithms” through learning, while traditional algorithms are explicitly designed by humans.