Challenges and Limitations of Expert Systems

The video discusses the pros and cons of expert systems compared to human experts. It highlights the emotionless nature of expert systems, which leads to consistency but lacks sensitivity to human issues. The video also covers hardware limitations, employment impacts, and the challenges of programming common sense and creativity into systems. Data integrity is a concern due to the need for manual updates. The video contrasts expert systems with artificial neural networks, emphasizing the latter's ability to learn and adapt, unlike the static nature of expert systems.

Why might the lack of emotion in expert systems be considered a disadvantage?

• It makes systems more expensive to maintain.

• It can result in decisions that negatively impact people.

• It leads to inconsistent decisions.

• It causes systems to require more power.

What is the main focus when discussing hardware limitations of expert systems?

• The cost of hardware.

• The impact on decision-making.

• The need for electricity.

• The size of the system.

What is a challenge in programming common sense into expert systems?

• It requires too much memory.

• It is difficult to translate human intuition into code.

• It makes systems slower.

• It increases the cost of development.

Why might expert systems be considered 'dumb'?

• They require constant human supervision.

• They follow rules without understanding context.

• They make decisions based on outdated data.

• They are unable to process large datasets.

How do expert systems typically handle new problems?

• They update their algorithms automatically.

• They ask for human intervention.

• They apply the same solutions repeatedly.

• They create new solutions.

What is a disadvantage of expert systems' consistency?

• It leads to unpredictable outcomes.

• It prevents the system from adapting to new situations.

• It makes the system more expensive.

• It requires more frequent updates.

What is a significant issue with data integrity in expert systems?

• Data is often inaccurate.

• Data must be manually updated.

• Data is stored inefficiently.

• Data is too complex to process.

What is a key difference between expert systems and artificial neural networks?

• Artificial neural networks require more power.

• Artificial neural networks can learn on their own.

• Expert systems are more creative.

• Expert systems are faster.

How does the lack of learning in expert systems affect their performance?

• They require less maintenance.

• They continue to make the same mistakes.

• They become more efficient over time.

• They adapt to new data automatically.

What is a common issue with the decisions made by expert systems?

• They require too much data.

• They are often inconsistent.

• They are too creative.

• They can be unresponsive to negative outcomes.