Rainer 5th Canadian Edition Test Questions & Answers

ePackage Title: Practice Questions

Course Title: Rainer, Introduction to Information Systems, Fifth Canadian Edition

Chapter Number: Tech Guide 4

Shuffle: No

Question Type: True/False

1) From an organizational point of view, artificial intelligence is perishable, whereas natural intelligence is permanent.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

2) It is difficult to document the knowledge of artificial intelligence, but it is easy to document the knowledge of natural intelligence.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

3) The goal of artificial intelligence is to completely replace human intelligence.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

4) Expertise refers to the extensive, task-specific knowledge acquired from training, reading, and experience.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

5) Expert systems attempt to mimic human experts by applying expertise in a specific domain.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

6) An expert system has a knowledge base and an inference engine.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

7) One problem with expert systems is that they decrease quality.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

8) The explanation subsystem is used to justify recommendations.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

9) A neural network has two layers of interconnected nodes.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

10) Neural networks simulate the underlying concepts of the biological brain.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

11) Neural networks require complete inputs to be effective.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

12) Fuzzy logic defines subjective concepts.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

13) Fuzzy logic can only address problems that are black and white.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

14) Google uses fuzzy logic.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

15) Machine learning develops algorithms that can learn from and make predictions about data.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

16) A genetic algorithm is an optimizing method that finds the combination of outputs that produces the best inputs.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

17) Intelligent agents use expert systems and fuzzy logic to create their behaviour.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

18) A shopping bot is also called a personal agent.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

19) A user agent is also called a personal agent.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

Question Type: Multiple Choice

20) Which of the following is NOT a characteristic of natural intelligence?

a) It is perishable from an organizational point of view.

b) It is easy, fast, and inexpensive.

c) It is erratic and inconsistent.

d) It is highly creative.

e) It makes use of a wide context of experiences.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

21) Which of the following is NOT a characteristic of artificial intelligence?

a) It is permanent.

b) It is easy, fast, and inexpensive.

c) It is highly creative.

d) It tends to be effective only in narrow domains.

e) It is consistent and thorough.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

22) Which of the following is a characteristic of artificial intelligence?

a) It is difficult to document.

b) It makes use of a wide context of experiences.

c) It can be erratic, inconsistent, and incomplete at times.

d) It is a permanent preservation of knowledge.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

23) What is the primary purpose of the Turing test?

a) determining whether computers can exhibit intelligent behaviour

b) supporting or replacing decision-makers

c) explaining recommendations provided by the computer

d) recognizing patterns within complex data

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

24) Which of the following is NOT an intelligent system?

a) ujam

b) IBM’s Watson

c) Blackboard

d) Norfolk Southern’s PLASMA

Difficulty: Hard

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

25) Expert systems

a) solve problems that are too difficult for human experts.

b) are based on procedural computer programming languages.

c) work in specific domains.

d) can apply to any business problem.

e) share characteristics with mainframe computing.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

26) Which of the following statements is FALSE?

a) Expert systems cannot replace decision-makers.

b) Expert systems apply expertise in a specific domain.

c) Expert systems capture the expertise from a domain expert (a person).

d) Expert systems can be embedded in larger systems.

e) Expert systems follow a logical path towards a recommendation.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

27) Which of the following is NOT an expert system activity?

a) knowledge acquisition

b) knowledge domain

c) knowledge inferencing

d) knowledge representation

e) knowledge transfer

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

28) An inference engine is

a) a data mining strategy used by intelligent agents.

b) the programming environment of an expert system.

c) a method of organizing expert system knowledge into chunks.

d) a methodology used to search through the rule base of an expert system.

e) the user interface of an expert system.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

29) Which of the following statements concerning expert systems is FALSE?

a) The knowledgebase contains facts.

b) The knowledgebase contains rules.

c) An expert system can explain its recommendation.

d) The blackboard displays the recommendation.

e) Expert systems cannot learn from their own mistakes.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

30) Which of the following is NOT a benefit of expert systems?

a) increased output and productivity

b) capture and dissemination of scarce expertise

c) increased decision-making time

d) reliability

e) works with incomplete, uncertain information

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

31) Which of the following is NOT a limitation of expert systems?

a) Expert systems cannot work with incomplete or uncertain data.

b) A process might contain too many rules to work as an expert system.

c) A process might be too vague to work as an expert system.

d) Decisions made by expert systems might be a potential liability.

e) Expert systems need to learn from their own mistakes.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

32) Hyo runs an ice cream shop with her family. They need to train someone to close the store at the end of the day. This process is an example of which type of intelligent system?

a) expert systems

b) neural network

c) fuzzy logic

d) genetic algorithms

e) intelligent agent

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

33) ______________ attempt to mimic human experts by applying expertise in a specific domain.

a) Neural networks

b) Expert systems

c) Genetic algorithms

d) Intelligent agents

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

34) Which of the following is the correct order of the activities to transfer expertise from the expert to a computer?

a) Acquisition, Representation, Inferencing, Transfer

b) Transfer, Acquisition, Representation, Inferencing

c) Acquisition, Inferencing, Representation, Transfer

d) Transfer, Acquisition, Inferencing, Representation

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

35) In this stage of transferring expertise from the expert to a computer, the acquired knowledge is organized as rules (or frames) and is stored electronically in a knowledge base.

a) Acquisition

b) Inferencing

c) Representation

d) Transfer

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

36) Neural networks are NOT used for _____________.

a) combatting fraud

b) preventing money-laundering

c) airline security

d) medical expertise

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

37) Neural networks are _________________.

a) a system of programs and data structures

b) used to approximate the operation of the human brain

c) particularly good at recognizing subtle, hidden, and newly emerging patterns in complex data

d) All of the above describe neural networks.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

38) _______ refer(s) to computer reasoning that deals with uncertainties by simulating the process of human reasoning.

a) Expert systems

b) Artificial neural networks

c) Speech understanding systems

d) Fuzzy logic

e) Computer vision systems

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

39) Fuzzy logic could be used to define which of the following terms?

a) gender

b) moderate-income

c) age

d) address

e) income

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

40) _______________ is a mathematical method of handling imprecise or subjective information.

a) An expert system

b) Fuzzy logic

c) A neural network

d) An intelligent agent

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

41) Fuzzy logic handles _______________ concepts.

a) black and white

b) objective

c) subjective

d) well-defined

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

42) Which of the following chips are used by AI systems?

a) RAM

b) CPU

c) GPU

d) Logic

Difficulty: Medium

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

43) Which of the following statements is FALSE?

a) Computer vision refers to the ability of information systems to identify objects, scenes, and activities in images.

b) Computer vision applications are designed to operate in unconstrained environments.

c) Computer vision has diverse applications.

d) Computer vision, combined with deep learning, can improve object detection, classification, and labelling.

e) None of the above.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

44) ______________ refers to the ability of information systems to work with text the way humans do.

a) Intelligent Agents

b) Robotics

c) Speech Recognition

d) Natural Language Processing

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

45) Which of the following is NOT a capability of User-Agents?

a) They can detect electronic attacks early so they can be prevented.

b) They can check your email and sort it according to priority rules.

c) They can alert you when high-value emails appear in your inbox.

d) They can automatically fill out forms on the Web for you.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

46) Self-driving cars rely heavily on _________________.

a) natural language processing

b) speech recognition

c) computer vision

d) intelligent agents

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

47) Buyer agents are also called _________________.

a) shopping bots

b) predictive agents

c) intelligent agents

d) surveillance agents

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

48) Which of the following statements is FALSE?

a) An intelligent agent is a software program.

b) Intelligent agents are also called bots.

c) Intelligent agents are always helpful.

d) Intelligent agents use export systems and fuzzy logic.

e) Intelligent agents perform repetitive computer-related tasks.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

49) Which of the following statements is FALSE?

a) Information agents search for information and store it for the user.

b) Information agents are used by Google to surf the Web sites in Google’s index.

c) Monitoring-and-surveillance agents are also called predictive agents.

d) Personal agents take action on behalf of the user.

e) User agents automatically fill out forms on the Web from stored information.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

50) Hyo runs an ice cream shop with her family. They have configured their computer to put any email that contains the word “order” into a folder called Possible Orders. This process is an example of which type of intelligent system?

a) expert systems

b) neural network

c) fuzzy logic

d) genetic algorithms

e) intelligent agent

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

51) Hyo runs an ice cream shop with her family. They just started letting customers fax in their ice cream orders. Sometimes the writing is hard to read, and Hyo’s family has to guess what the customers have ordered based on what flavours the shop has. This process is an example of which type of intelligent system?

a) expert systems

b) neural network

c) fuzzy logic.

d) genetic algorithms

e) intelligent agent.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

52) Behind the scenes, intelligent agents often use ________________.

a) expert systems and fuzzy logic

b) expert systems and neural networks

c) fuzzy logic and neural networks

d) fuzzy logic and genetic algorithms

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

53) ____________ agents help customers find the products and services they need on a web site.

a) Information

b) Monitoring-and-surveillance

c) Buyer

d) User

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

54) A(n) _____________ agent is software that will search several retailer websites and provide a comparison of each retailer’s offerings, including price and availability.

a) buyer

b) personal

c) user

d) information

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

55) ____________ agents constantly observe and report on some item of interest.

a) Information

b) Monitoring-and-surveillance

c) Buyer

d) User

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

Question Type: Essay

56) What are the advantages and disadvantages of artificial intelligence systems?

Difficulty: Hard

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

57) Describe how your university could use an expert system in its admissions process.

Difficulty: Hard

Bloomcode: Application

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

58) Compare and contrast expert systems and intelligent agents.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Learning Objective 2: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Technologies

Section Reference 2: Artificial Intelligence Applications

59) How are neural networks useful to businesses?

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Learning Objective 2: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Technologies

Section Reference 2: Artificial Intelligence Applications

60) How are intelligent agents useful to businesses?

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Learning Objective 2: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Technologies

Section Reference 2: Artificial Intelligence Applications

61) Compare and contrast fuzzy logic and neural networks.

Difficulty: Medium

Bloomcode: Comprehension

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Learning Objective 2: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Technologies

Section Reference 2: Artificial Intelligence Applications

62) Contrast the three types of intelligent agents and give examples of how they might be used in business.

Difficulty: Hard

Bloomcode: Application

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

Question Type: Fill-in-the-Blank

63) Artificial intelligence is a subfield of computer science concerned with studying the thought processes of ____________.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Explain the potential value and the potential limitations of artificial intelligence.

Section Reference 1: Introduction to Artificial Intelligence

64) The _________________ is a computer program that provides a methodology for reasoning and formulating conclusions.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

65) A _________________ is a system of programs and data structures that approximates the operation of the human brain.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of expert systems, machine learning systems, deep learning systems, and neural networks.

Section Reference 1: Artificial Intelligence Technologies

66) Fuzzy logic is a mathematical method of handling _______________ information.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

67) A ____________ mimics the evolutionary “survival-of-the-fittest” process to generate increasingly better solutions to a problem.

Difficulty: Easy

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications

68) An intelligent agent is a software program that assists you, or acts on your behalf, in performing ______________________ computer-related tasks.

Difficulty: Medium

Bloomcode: Knowledge

AACSB Code: Technology

Learning Objective 1: Provide examples of computer vision, natural language processing, robotics, speech recognition, and intelligent agents.

Section Reference 1: Artificial Intelligence Applications