What are the different AI methods and how do they compare?

What are the different AI methods and how do they compare?

There are several AI methods, including machine learning, deep learning, and rule-based systems. Machine learning focuses on algorithms that learn from data, while deep learning uses neural networks to analyze complex patterns. Rule-based systems rely on predefined rules to make decisions.

Can you explain the advantages of using deep learning over traditional machine learning?

Deep learning can automatically extract features from raw data, making it more effective for complex tasks like image and speech recognition. It often requires less manual feature engineering compared to traditional machine learning.

What are some real-world applications of these AI methods?

Machine learning is used in recommendation systems, while deep learning powers self-driving cars and virtual assistants. Rule-based systems are common in expert systems for medical diagnosis.

Are there any limitations to these AI methods?

Yes, machine learning can struggle with small datasets, deep learning requires significant computational resources, and rule-based systems can be inflexible and hard to scale.