1. Which of these best defines Artificial Intelligence?
Correct Answer: a) Machines that can perform tasks requiring human intelligence
AI refers to systems designed to perform tasks that typically require human intelligence, such as visual perception, decision-making, and language understanding, but doesn't necessarily imply human-level performance in all areas.
2. What is the key difference between Narrow AI and General AI?
Correct Answer: b) General AI can perform any intellectual task a human can
Narrow AI (Weak AI) is designed for specific tasks (like facial recognition), while General AI (Strong AI) would have the adaptable intelligence of humans across diverse domains. General AI doesn't currently exist.
3. What is the fundamental concept behind Machine Learning?
Correct Answer: c) Using algorithms that improve automatically through experience
ML algorithms build mathematical models based on sample data (training data) to make predictions or decisions without being explicitly programmed to perform the task.
4. Which of these is an example of Supervised Learning?
Correct Answer: b) Predicting house prices based on historical sales data
Supervised learning uses labeled datasets to train algorithms to predict outcomes. Here, historical sales provide the "labels" (prices) for the model to learn from.
5. What distinguishes Deep Learning from traditional Machine Learning?
Correct Answer: a) Use of neural networks with multiple hidden layers
Deep Learning utilizes artificial neural networks with many layers (deep architectures) that can automatically learn hierarchical representations of data.
6. Which application is Deep Learning particularly well-suited for?
Correct Answer: b) Image recognition and classification
Deep Learning excels at processing unstructured data like images, videos, and text, where it can automatically learn complex patterns and features.
7. What was the primary characteristic of early Expert Systems?
Correct Answer: b) They were based on rules and knowledge bases
Expert systems relied on explicitly programmed rules and knowledge bases created by human experts, rather than learning from data like modern ML systems.
8. Which component of an Expert System is responsible for applying rules to the known facts?
Correct Answer: b) Inference engine
The inference engine processes the rules from the knowledge base to derive conclusions or make decisions based on the available data.
9. What was a major limitation of traditional Expert Systems?
Correct Answer: a) They couldn't handle uncertainty well
Expert systems struggled with ambiguous or incomplete information because they relied on rigid logical rules rather than probabilistic reasoning.
10. How do modern AI systems differ from classic Expert Systems?
Correct Answer: a) They can learn patterns from data automatically
Modern AI systems, especially those using machine learning, can extract patterns and rules from data without explicit programming, making them more flexible and adaptable than rule-based expert systems.