Quiz: Foundations of Intelligent Systems and Ethical AI — 5 questions

1. How do the knowledge base and inference engine in expert systems architecture fundamentally differ from each other?

A. The inference engine stores the knowledge base and retrieves information, whereas the knowledge base applies reasoning rules.

B. The knowledge base stores rules and facts, while the inference engine applies logical reasoning to these facts.

C. The inference engine stores expertise and facts, while the knowledge base applies logical rules to these facts.

D. The knowledge base processes data to generate new rules, whereas the inference engine stores the expert's knowledge.

Answer: (B)

Explanation

The knowledge base stores expertise, facts, and rules, while the inference engine applies logical reasoning to this stored knowledge to simulate decision-making, as described in the source.

2. What is the primary purpose of analyzing language at different NLP levels such as phonological, morphological, lexical, syntactic, semantic, and pragmatic?

A. To build a hierarchical understanding of language structure and meaning

B. To identify grammatical errors in sentences

C. To improve speech synthesis quality

D. To translate text from one language to another

Answer: (A)

Explanation

The hierarchy of NLP analysis levels—from phonology to pragmatics—serves to build a comprehensive understanding of language by examining sounds, word structure, meaning, and context. This layered approach allows NLP systems to interpret language accurately in various applications.

3. What is computer vision primarily concerned with?

A. Creating 3D models from images

B. Training robots to perform tasks

C. Processing images to interpret visual data

D. Generating synthetic images for simulations

Answer: (C)

Explanation

Computer vision is primarily concerned with processing images to extract meaningful information and enable machines to interpret visual data. The source states that it involves techniques such as filtering and feature extraction to analyze images effectively.

4. What specific AI technique is mentioned as improving fault diagnosis in VLSI testing according to the source?

A. Supervised learning for fault detection

B. Natural language processing for circuit analysis

C. Machine learning for fault prediction

D. Reinforcement learning for circuit optimization

Answer: (C)

Explanation

The source explicitly states that 'machine learning and deep learning techniques significantly improve fault diagnosis accuracy and speed,' indicating that machine learning is the AI technique used for fault prediction in VLSI testing.

5. What is a key property that responsible AI frameworks aim to promote?

A. Fairness, transparency, and accountability

B. Speed of decision-making

C. Cost-efficiency in deployment

D. Maximization of automation

Answer: (A)

Explanation

Responsible AI frameworks are explicitly designed to promote fairness, transparency, and accountability in AI systems, ensuring ethical development and deployment. These qualities are fundamental to responsible AI, as stated in the source content.