Prompt Detail:
| Aspect | Traditional AI | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|---|
| Definition | AI programmed to follow rules and logic | AI using training data to learn from | ML with more complex models for advanced tasks |
| to make decisions and perform tasks. | experience and create a model for | ||
| categorizing new data. | |||
| Learning Method | Pre-defined rules and knowledge bases | Training data-driven learning | Large-scale training data-driven learning |
| Model Complexity | Rule-based systems, logical reasoning | Varied model complexity based on problem | Complex models using deep neural networks |
| Neural Networks | Not typically used | Shallow neural networks or other | Deep neural networks with many layers |
| techniques, depending on the problem | |||
| Adaptability | Limited, based on pre-programmed rules | Adaptable, learns from new data | Highly adaptable, learns from vast amounts of data |
| Applications | Expert systems, planning, gaming | Image recognition, sentiment analysis, | Image and speech recognition, self-driving cars, |
| recommendation systems, etc. | natural language processing, etc. | ||
| Example Use Cases | Chess AI, rule-based expert systems | Spam filtering, product recommendations | Google DeepMind's AlphaGo, self-driving cars |
| Strengths | Efficient, reliable in well-defined | Learns and adapts from data, less reliance | Handles complex tasks, can work with unstructured |
| scenarios with clear rules | on rule-based systems | data, and learns hierarchical features | |
| Limitations | Limited to well-defined problems, not | Requires large amounts of training data, | Requires even larger amounts of training data, |
| adaptable, struggles with ambiguity | may struggle with ambiguous data | may overfit or require significant compute power |
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