Lecture on AI Programming vs. Traditional Programming

Introduction

• Traditional Programming: Programs follow specific instructions provided by the programmer.
• AI Programming: Acts like a black box; outputs are generated from inputs through a less-deterministic process.

AI Programming Components

1. Data
   • Training Data: Helps models learn.
   • Validation Data: Tunes the model.
   • Test Data: Assesses model performance.
   • Diverse Datasets: Essential for generalizing learning to new data.
2. Algorithms
   • Machine Learning (ML): Can make predictions without explicit programming.
   • Reinforcement Learning (RL): Learns through rewards and punishments.
3. Computing Power
   • GPUs are crucial for processing large data and running complex algorithms.

Traditional Programming

• Explicit Instructions: Programs follow step-by-step instructions.
• Deterministic Approach: Effective for clearly defined problems with limited outcomes.
• Manual Programming: Requires detailed coding for every scenario.

Comparison: AI vs. Traditional Programming

1. Stability and Scalability

• Traditional: Stable and predictable but requires manual scaling.
• AI: Scalable and adaptive but less stable and predictable.

2. Control and Transparency

• Traditional: Offers complete control and traceability.
• AI: Functions as black boxes with unclear internal processes.
  • Explainable AI: Emerging field to address transparency issues.

3. Learning and Data Handling

• Traditional: Rigid and requires manual updates for new data.
• AI: Flexible with capacity for continuous learning from unstructured data.

Conclusion

• Traditional Programming: Not disappearing; remains essential for specific tasks.
• AI Programming: Offers opportunities to address complex, dynamic challenges with advanced technologies.
• Future of AI: Growing field with potential for developing more transparent and controllable systems.