Quarter 2-G12-AI Concepts Quiz

Which type of AI is used for creating systems that can perform specific tasks but cannot think beyond them?

• Strong AI

• Narrow AI

• Artificial General Intelligence

• Superintelligent AI

What is the goal of Superintelligent AI?

• To perform tasks at human intelligence levels.

• To perform specific tasks better than humans.

• To outperform human intelligence in every aspect.

• To understand and process human emotions.

Which type of AI involves systems designed to solve problems in specific fields, such as medical diagnosis or fraud detection?

• Narrow AI

• Artificial General Intelligence

• Superintelligent AI

• Reactive Machines

What is the main characteristic of Weak or Narrow AI?

• It can perform a wide range of tasks with human-like intelligence.

• It is capable of understanding and adapting to any new task.

• It is designed to perform specific tasks with limited functionality.

• It can solve problems across various domains like a human.

What is Domain-Specific AI designed for?

• To solve all human problems across multiple domains

• To be general and adaptable for any task

• To specialize in specific industries or tasks using tailored knowledge

• To think and reason like humans

What is Strong AI capable of?

• Performing specific tasks with human-like abilities

• Solving problems and learning across various domains like a human

• Outperforming humans in every task

• Having specific knowledge in narrow fields

In which application is clustering used for image segmentation?

• Fraud detection

• Document classification

• Medical image analysis

• Sentiment analysis

Which of the following algorithms is an example of supervised learning?

• Decision Tree

• K-means

• t-Distributed Stochastic Neighbor Embedding (t-SNE)

• DBSCAN

What is a common application of supervised learning?

• Fraud detection

• Customer segmentation

• Topic modeling

• Market basket analysis

Which of the following is a popular clustering algorithm used in unsupervised learning?

• Random Forest

• Logistic Regression

• Hierarchical Clustering

• Linear Discriminant Analysis