As such, the entire universe of AI can be split into these two groups. A computer system that achieves AI through a rule-based technique is called rule-based system. A computer system that achieves AI through a machine learning technique is called a learning system.Machine learning systems can learn from past data and adapt to new situations by themselves, whereas rule-based systems require human intervention for any changes.A Rule-based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. In other words, rule-based learning follows the relationship or patterns in data defined by the developer.
Certainly! Here's a differentiation between Rule-based AI and Learning-based AI:
**Rule-Based AI (Knowledge-Based AI):**
1. **Knowledge Representation:** Rule-based AI systems rely on explicit rules and knowledge representations created by human experts. These rules are often in the form of "if-then" statements.
2. **Programming:** Human experts manually program the rules and knowledge into the system.
3. **Limited Generalization:** Rule-based AI systems have limited ability to generalize beyond the predefined rules. They make decisions based on explicit rules and may not adapt well to novel situations.
4. **Transparency:** They are transparent and explainable since the reasoning is based on explicitly defined rules.
5. **Examples:** Expert systems, which provide specialized knowledge and decision-making in specific domains, are often rule-based.
**Learning-Based AI (Machine Learning):**
1. **Data-Driven:** Learning-based AI systems learn patterns and make decisions based on data. They don't rely on explicit rules defined by humans.
2. **Training:** These systems are trained on large datasets, and their performance improves with more data and training.
3. **Generalization:** Learning-based AI systems excel at generalization. They can adapt to new, unseen data and perform well in a wide range of tasks.
4. **Complex Models:** They often use complex models, such as neural networks, to capture intricate patterns in data.
5. **Black-Box:** While they provide excellent performance, learning-based AI models can be challenging to interpret. They are often seen as "black-box" systems where it's challenging to explain why a particular decision was made.
In summary, rule-based AI relies on predefined rules and human knowledge, making it transparent but limited in flexibility. Learning-based AI, on the other hand, learns from data, excels in generalization, but can be less transparent due to its complex models. The choice between these approaches depends on the specific problem and requirements of an AI application.
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Explanation:
As such, the entire universe of AI can be split into these two groups. A computer system that achieves AI through a rule-based technique is called rule-based system. A computer system that achieves AI through a machine learning technique is called a learning system.Machine learning systems can learn from past data and adapt to new situations by themselves, whereas rule-based systems require human intervention for any changes.A Rule-based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. In other words, rule-based learning follows the relationship or patterns in data defined by the developer.
Verified answer
Explanation:
Certainly! Here's a differentiation between Rule-based AI and Learning-based AI:
**Rule-Based AI (Knowledge-Based AI):**
1. **Knowledge Representation:** Rule-based AI systems rely on explicit rules and knowledge representations created by human experts. These rules are often in the form of "if-then" statements.
2. **Programming:** Human experts manually program the rules and knowledge into the system.
3. **Limited Generalization:** Rule-based AI systems have limited ability to generalize beyond the predefined rules. They make decisions based on explicit rules and may not adapt well to novel situations.
4. **Transparency:** They are transparent and explainable since the reasoning is based on explicitly defined rules.
5. **Examples:** Expert systems, which provide specialized knowledge and decision-making in specific domains, are often rule-based.
**Learning-Based AI (Machine Learning):**
1. **Data-Driven:** Learning-based AI systems learn patterns and make decisions based on data. They don't rely on explicit rules defined by humans.
2. **Training:** These systems are trained on large datasets, and their performance improves with more data and training.
3. **Generalization:** Learning-based AI systems excel at generalization. They can adapt to new, unseen data and perform well in a wide range of tasks.
4. **Complex Models:** They often use complex models, such as neural networks, to capture intricate patterns in data.
5. **Black-Box:** While they provide excellent performance, learning-based AI models can be challenging to interpret. They are often seen as "black-box" systems where it's challenging to explain why a particular decision was made.
In summary, rule-based AI relies on predefined rules and human knowledge, making it transparent but limited in flexibility. Learning-based AI, on the other hand, learns from data, excels in generalization, but can be less transparent due to its complex models. The choice between these approaches depends on the specific problem and requirements of an AI application.