Difference Between Machine Learning and AI
Difference Between Machine Learning and Artificial Intelligence (AI)
| Feature | Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|---|
| Definition | AI is the broader concept of machines performing tasks that typically require human intelligence. | ML is a subset of AI that focuses on algorithms learning patterns from data to make predictions. |
| Goal | Simulate human intelligence, including reasoning, decision-making, and perception. | Enable machines to learn from data and improve performance without explicit programming. |
| Scope | Encompasses ML, deep learning, expert systems, and robotics. | A specific AI technique using statistical models and algorithms. |
| Data Dependency | Can function with or without large datasets (e.g., rule-based AI). | Requires large datasets for training and improving accuracy. |
| Techniques | Includes ML, deep learning, neural networks, expert systems, and rule-based systems. | Includes supervised learning, unsupervised learning, and reinforcement learning. |
| Example Applications | Self-driving cars, chatbots, robotic process automation, intelligent assistants. | Spam detection, recommendation systems (Netflix, YouTube), image recognition. |
| Decision-Making | AI can make decisions based on logic, rules, and learning. | ML models predict outcomes based on data patterns but do not make human-like decisions. |
Key Takeaways
- AI is the broader field, while ML is a subset of AI focused on learning from data.
- AI includes rule-based systems, robotics, and ML, whereas ML specifically involves learning from data.
- All ML is AI, but not all AI is ML (e.g., rule-based AI does not involve learning from data).