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Artificial Intelligence Full Test Bank Chapter 14

Package Title: Tech Guide 4, Testbank

Course Title: Rainer IS 9e

Chapter Number: 14

Question type: Multiple Choice

1) Kroger’s Edge technology and Amazon’s Just Walk Out technology leverage ____-driven technologies.

a) intelligent agent

b) neural network

c) machine learning

d) robotics

Difficulty: Easy

Section Reference 1: Opening Case

Learning Objective 14.5: Provide use case examples of artificial intelligence applications in accounting, finance, marketing, production and operations management, human resource management, and management information systems.

Bloomcode: Knowledge

AACSB: Technology

2) ______________ is a machine learning-powered shopping cart that allows shoppers to skip the checkout line.

a) Amazon’s Dash Cart

b) Kroger’s Edge

c) Sephora’s Virtual Shopper

d) Walmart’s Just Walk Out

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

3) Amazon’s Dash Cart combines _____ technologies to enable shoppers to easily sign in and begin using the cart.

a) machine learning and QR code

b) machine learning and RFID

c) neural network and QR code

d) neural network and RFID

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

4) ______ has installed cameras at each checkout lane to detect customers’ moods using facial recognition technology.

a) Amazon Go

b) Kroger

c) Target

d) Walmart

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

5) Alibaba’s _________ -powered chatbot answers more than 350 million customer inquiries per day, successfully understanding more than _____ percent of them.

a) expert system; 75

b) expert system; 90

c) machine learning; 75

d) machine learning; 90

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

6) The ______________ test is a widely used test to determine whether a computer exhibits intelligent behavior.

a) Expert system

b) Intelligence

c) Moore

d) Turing

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

7) Strong AI is __________ and could be considered to have __________.

a) hypothetical; a soul

b) hypothetical; sentience

c) reality; a soul

d) reality; sentience

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

8) ___________ is a capability performed relatively better by AI than human intelligence.

a) Creativity

b) Reasoning

c) Total cost of knowledge

d) Use of sensory experiences

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

9) _________ is a capability performed relatively better by human intelligence than AI.

a) Documentability of process and knowledge

b) Preservation of knowledge

c) Total cost of knowledge

d) Use of sensory experiences

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

10) Machine learning is still not as good as people in most cases but in some cases better than people in regard to ___________.

a) Documentability of process and knowledge

b) Duplication and dissemination of knowledge

c) Recognizing patterns and relationships

d) Use of sensory experiences

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

11) Current AI systems are considered ________ AI.

a) Big

b) Small

c) Strong

d) Weak

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

12) ______ are AI-enabled crimes of moderate concern to experts.

a) burglar bots

b) data poisoning

c) deepfakes

d) spearphishing

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

13) ______ are AI-enabled crimes of low concern to experts.

a) burglar bots

b) data poisoning

c) deepfakes

d) spearphishing

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

14) ______ are AI-enabled crimes of high concern to experts.

a) autonomous attack drones

b) burglar bots

c) data poisoning

d) deepfakes

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

15) ______ are AI-enabled crimes of high concern to experts.

a) autonomous attack drones

b) burglar bots

c) data poisoning

d) SCADA attacks

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

16) ______ are AI-enabled crimes of moderate concern to experts.

a) autonomous attack drones

b) burglar bots

c) deepfakes

d) spear phishing

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

17) Labeling spam emails as safe is called data _____________ and experts consider it a ____ concern AI-enabled crime.

a) mishandling; moderate

b) mishandling; high

c) poisoning; moderate

d) poisoning; high

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

18) Which of the following technological advancements have NOT led to enhancements of AI?

a) Big Data

b) cloud computing

c) GPU chips

d) QR codes

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology, Reflective Thinking

19) _____________ is an application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

a) An expert system

b) An intelligent agent

c) Machine learning

d) A neural network

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

20) _____________ is a structured combination of data and a computer algorithm that produces answers.

a) Nontraditional programming

b) Traditional programming

c) Supervised machine learning

d) Unsupervised machine learning

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

21) In _____________, developers train the system with labeled input data and the expected output results; after the system is trained, developers feeds it with unlabeled input data and examine the accuracy of the output data.

a) Nontraditional programming

b) Traditional programming

c) Supervised machine learning

d) Unsupervised machine learning

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

22) _____________ are computer systems that attempt to mimic human experts by applying expertise in a specific domain.

a) Expert systems

b) Intelligent agents

c) Learning machines

d) Neural networks

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

23) ___________ require human experts; _____ must be formally structured in the form of rules.

a) Expert systems; expert systems

b) Expert systems; machine learning algorithms

c) Machine learning algorithms; expert systems

d) Machine learning algorithms; machine learning algorithms

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

24) _____ comes from a mismatch between the data used to train and test the systems and the data the system actually encounters in the real world.

a) An algorithmic bias

b) Data shift

c) A false positive

d) Underspecification

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

25) Machine learning systems trained on datasets collected from biased samples can exhibit these biases when they are used; this is called ______.

a) an algorithmic bias

b) data shift

c) a false positive

d) underspecification

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

26) _____ is a result that indicates that a given condition exists when it in fact does not.

a) An algorithmic bias

b) Data shift

c) A false positive

d) Underspecification

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

27) _____________ learning is a type of machine learning in which the system is given labeled input data and the expected output results.

a) Reinforcement

b) Semi-supervised

c) Supervised

d) Unsupervised

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

28) E-mail spam detection is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

29) Churn prediction is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

30) Conversion prediction is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

31) News article categories are an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

32) Plant species classification is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

33) Optical character recognition is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

34) Photo classification is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

35) Fraud detection is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

36) Outlier detection is an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

37) Medical diagnostic tests are an example of _____ classification.

a) binary

b) imbalanced

c) multi-class

d) multi-label

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

38) _____________ learning is a type of machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.

a) Reinforcement

b) Semi-supervised

c) Supervised

d) Unsupervised

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

39) _____________ is an example of semi-supervised learning.

a) Classifying text documents

b) Finding customer segments

c) Labeling spam

d) Optical character recognition

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

40) _____________ learning is a type of machine learning that searches for previously undetected patterns in a data set with no pre-existing labels and with minimal human supervision.

a) Reinforcement

b) Semi-supervised

c) Supervised

d) Unsupervised

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

41) _____________ is one of the primary techniques in supervised learning.

a) Classification

b) Cluster analysis

c) Multiple linear regression

d) Simple linear regression

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

42) _____________ is an example of unsupervised learning.

a) Classifying text documents

b) Finding customer segments

c) Labeling spam

d) Optical character recognition

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

43) _____________ learning is a type of machine learning in which the system learns to achieve a goal in an uncertain, potentially complex environment.

a) Reinforcement

b) Semi-supervised

c) Supervised

d) Unsupervised

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

44) In _____________ learning, the system faces a game-like situations where it employs trial and error to find a solution to a problem.

a) Reinforcement

b) Semi-supervised

c) Supervised

d) Unsupervised

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

45) _____________ is an example of reinforcement learning.

a) Automated ad bidding

b) Finding customer segments

c) Labeling spam

d) Optical character recognition

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

46) Which of the following is NOT an example of reinforcement learning?

a) Classifying text documents

b) Dynamic resource allocation

c) Robotic control

d) Supply chain optimization

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

47) Which of the following is NOT an example of reinforcement learning?

a) Automated ad bidding and buying

b) Finding customer segments

c) Recommendation systems

d) Robotic control

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

48) Which of the following is NOT an example of reinforcement learning?

a) Automated calibration of machines

b) Dynamic resource allocation

c) Labeling spam

d) Self-driving cars

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

49) ___________ is a subset of machine learning in which artificial neural networks learn from large amounts of data.

a) Computer vision

b) Deep learning

c) Long learning

d) Natural language processing

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

50) Deep learning is a subset of ______ in which artificial _____ learn from large amounts of data.

a) machine learning; machine learning algorithms

b) machine learning; neural networks

c) neural networking; machine learning algorithms

d) neural networking; neural networks

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

51) Deep learning systems ____ solve complex problems with a data set that is diverse and _____.

a) can; structured

b) can; unstructured

c) cannot; structured

d) cannot; unstructured

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

52) Deep learning systems ____ solve complex problems and ____ need to be exposed to labeled historical/training data.

a) can; do

b) can; do not

c) cannot; do

d) cannot; do not

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

53) ___________ is NOT an example of deep learning.

a) Customer relationship management

b) Identifying cross-selling opportunities

c) Natural language processing

d) Speech recognition

Difficulty: Easy

Section Reference 1: TG 4.2 Artificial Intelligence Technologies

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

Bloomcode: Knowledge

AACSB: Technology

54) ____________ is an example of a self-driving robot powered by _________.

a) Robot Vera; machine learning

b) Robot Vera; a neural network

c) Snackbot; machine learning

d) Snackbot; a neural network

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

55) ____________ is an example of a self-driving robot powered by _________.

a) Robot Vera; machine learning

b) Robot Vera; a neural network

c) Snackbot; machine learning

d) Snackbot; a neural network

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

56) BlackSwan is an AI company whose product, Element, integrates _____ to address data acquisition, insight discovery, and predictions.

a) Big Data and expert systems

b) Big Data and machine learning

c) the IoT and expert systems

d) the IoT and machine learning

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

57) A(n) _______________ is a set of nodes that work in parallel to simulate the way the human brain works, although in greatly simplified form.

a) Expert system

b) Intelligent agent

c) Learning machine

d) Neural network

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

58) A(n) _______________ neural network is designed to access previous data such as sequential data or time series data during iterations of input.

a) adversarial

b) convolutional

c) generative

d) recurrent

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

59) A(n) _______________ neural network is used in applications such as moving a robotic arm, reading a sentence, predicting time series, and composing music.

a) adversarial

b) convolutional

c) generative

d) recurrent

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

60) Recurrent neural networks are used in applications such as _____.

a) drug discovery

b) facial recognition

c) moving a robotic arm

d) natural language processing

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

61) A(n) _______________ neural network is designed to separate areas of image inputs by extracting features to identify edges, curves, and color density and then recombine these inputs for classification and prediction.

a) adversarial

b) convolutional

c) generative

d) recurrent

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

62) Convolutional neural networks are used in applications such as _____.

a) anomaly detection

b) composing music

c) predicting time series

d) reading a sentence

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

63) Convolutional neural networks are used in applications such as _____.

a) composing music

b) moving a robotic arm

c) predicting time series

d) video analysis

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

64) A generative adversarial network consists of ____ neural networks that compete in a zero-sum game in an effort to segregate real data from synthetic data.

a) 2

b) 3

c) 4

d) 5

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

65) Generative adversarial networks are used in applications such as _____.

a) anomaly detection

b) composing music

c) inpainting

d) reading a sentence

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

66) Generative adversarial networks are used in applications such as _____.

a) composing music

b) improving deep-space photography

c) predicting time series

d) video analysis

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

67) _____________ is the ability of information systems to identify objects, scenes, and activities in images.

a) Computer vision

b) Natural image processing

c) Natural language processing

d) Speech recognition

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

68) Facebook’s facial recognition is an example of _________.

a) Computer vision

b) Natural image processing

c) Natural language processing

d) Speech recognition

Difficulty: Medium

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Application

AACSB: Technology

69) ____________ is the ability of information systems to work with text the way that humans do.

a) Computer vision

b) Computer texting

c) Natural language processing

d) Speech recognition

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

70) eBay’s machine learning platform, Krylov, translates using __________.

a) Computer vision

b) Computer texting

c) Natural language processing

d) Speech recognition

Difficulty: Medium

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Application

AACSB: Technology

71) ____________ is an example of natural language processing.

a) Amazon Alexa

b) Apple Siri

c) eBay Krylov

d) Samsung Bixby

Difficulty: Medium

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Application

AACSB: Technology

72) Google’s translation app uses __________.

a) Computer vision

b) Computer texting

c) Natural language processing

d) Speech recognition

Difficulty: Medium

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Application

AACSB: Technology

73) ___________ focuses on automatically and accurately transcribing human speech.

a) Computer transcription

b) Computer vision

c) Natural language processing

d) Speech recognition

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

74) A(n) _________________ agent is a software program that assists users, or acts on their behalf, in performing computer-related tasks.

a) Intelligent

b) Seller

c) Smart

d) Stakeholder

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

75) A(n) __________ agent searches for information and displays it to users.

a) Information

b) Personal

c) Predictive

d) User

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

75) A(n) _____________ agent constantly observes and reports on some item of interest.

a) Information

b) Personal

c) Predictive

d) User

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

76) H&R Block uses ____ to understand context, interpret client intent, and draw connections between clients’ statements and relevant areas of their returns.

a) Alexa

b) Erica

c) Flexi

d) Watson

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

77) Chatbots are used in finance for ______.

a) algorithmic training

b) insurance and risk management

c) process automation

d) security

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

78) Robo-advisors are used in finance for ______.

a) algorithmic training

b) insurance and risk management

c) process automation

d) security

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

79) Client behavioral modeling is used in finance for ______.

a) algorithmic training

b) insurance and risk management

c) process automation

d) security

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

80) To predict customer churn, companies are using ____ systems to monitor customer behavior.

a) expert

b) intelligent

c) machine learning

d) neural network

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

81) A(n) ___________ pricing strategy allows businesses to offer flexible prices for the product and services they offer.

a) adjustable

b) algorithmic

c) dynamic

d) flexible

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

82) _____________ testing is the process of showing two versions of the same web page to different segments of website visitors at the same time and comparing which version drives more purchases or signups.

a) 1/2

b) A/B

c) Dynamic

d) Flexible

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

83) A(n) ___________ factory is a highly digitized operation that continuously collects and shares data through connected machines, devices, and production systems.

a) intelligent

b) seller

c) smart

d) stakeholder

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

84) Smart factories do NOT use ___________.

a) Big Data analytics

b) expert systems

c) the industrial IoT

d) robotics

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

85) _____ is the process by which new employees acquire the knowledge, skills, and behaviors they need to become effective organizational members; ____ is an AI-driven app that helps with this process.

a) Onboarding; Ellen

b) Onboarding; Textio

c) Onloading; Ellen

d) Onloading; Textio

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

86) _____ is the combination of cultural philosophies, practices, and tools that increase an organization’s ability to quickly develop and deliver applications.

a) Continuous delivery

b) Continuous integration

c) DevOps

d) ModBots

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

87) _____ refers to a situation in which a version of the final software package is always ready to be released but is not sent to production before the decision is made to release it.

a) Continuous delivery

b) Continuous integration

c) DevOps

d) ModBots

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

88) _____ is the practice of combining computer code from multiple contributors on a single software project.

a) Continuous delivery

b) Continuous integration

c) DevOps

d) ModBots

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

89) In March 2019, McDonald’s acquired Dynamic Yield, an ____ AI startup with extensive expertise in personalization, to customize their ____ approach.

a) American; cross-selling

b) American; upselling

c) Israeli; cross-selling

d) Israeli; upselling

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

90) On an _____ basis, McDonald’s tracks customer smartphones using _____.

a) opt-in; geofencing

b) opt-in; RFID

c) opt-out; geofencing

d) opt-out; RFID

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

91) _____ is a startup that develops machine learning-powered, voice-activated platforms with technology that is ____.

a) Apprente; sound-to-meaning

b) Apprente; speech-to-text

c) Dynamic Yield; sound-to-meaning

d) Dynamic Yield; speech-to-text

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

Question type: True/False:

92) Sephora Virtual Artist is the firm’s augmented reality tool that allows customers to try on thousands of makeup products.

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

93) Sephora Virtual Artist is the firm’s virtual reality tool that allows customers to try on thousands of makeup products.

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

94) Many retailers use Google or Amazon machine learning-powered voice recognition technology and natural language processing to provide customers with simple and quick voice search.

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

95) Amazon’s machine learning-powered chatbot answers more than 350 million customer inquiries per day, successfully understanding more than 95 percent of them.

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

96) Alibaba’s machine learning-powered chatbot answers more than 350 million customer inquiries per day, successfully understanding more than 90 percent of them.

Difficulty: Easy

Section Reference 1: Opening Case

Bloomcode: Knowledge

AACSB: Technology

97) AI is a subfield of psychology that studies the thought processes of humans and recreates the effects of those processes through information systems.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

98) AI is a subfield of computer science that studies the thought processes of humans and recreates the effects of those processes through information systems.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

99) AI is defined in terms of how humans think.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

100) AI is defined in terms of the tasks that humans perform rather than how humans think.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

101) The ultimate goal of AI is to build machines that replace human intelligence.

Difficulty: Medium

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

102) The Porter test proposes a scenario in which a man and a computer both pretend to be human, and a human interviewer has to identify which is real.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

103) Moore’s Law proposes a scenario in which a man and a computer both pretend to be human, and a human interviewer has to identify which is real.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

104) The Turing test proposes a scenario in which a man and a computer both pretend to be human, and a human interviewer has to identify which is real.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

105) Based on the Turing test, commercial AI products are far from exhibiting any significant intelligence.

Difficulty: Medium

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

106) Machine learning is better at recognizing patterns and relationships than people.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

107) Machine learning is sometimes better at recognizing patterns and relationships than people.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

108) Artificial intelligence is only good at reasoning in narrow, focused, and unstable domains.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

109) Artificial intelligence is only good at reasoning in narrow, focused, and stable domains.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

110) If a computer can perform a useful and specific function that once required human intelligence to perform and does so at human levels or better, this is considered weak AI.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

111) If a computer can perform a useful and specific function that once required human intelligence to perform and does so at human levels or better, this is considered strong AI.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

112) Weak AI is hypothetical.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

113) Strong AI is hypothetical.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

114) AI with consciousness or sentience is called Broad AI.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

115) AI with consciousness or sentience is called Strong AI.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

116) Current AI systems are considered weak AI.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

117) The advent of AI always diminishes the performance of humans because it gives them the ability to be lazy.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

118) Weak AI is already powerful enough to make a dramatic difference in human life.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

119) Weak AI does not pose a threat to privacy and liberty since the tasks it performs are narrow, focused, and stable.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

120) Weak AI poses a threat to privacy and liberty in the form of things like facial recognition.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

121) Experts consider deepfakes, spear phishing, whaling, and AI-authored fake news high concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

122) Experts consider deepfakes, spear phishing, whaling, and AI-authored fake news moderate concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

123) Experts consider misusing military robots, autonomous attack drones, tricking facing recognition systems, and data poisoning high concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

124) Experts consider misusing military robots, autonomous attack drones, tricking facing recognition systems, and data poisoning moderate concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

125) Experts consider AI-authored fake reviews, AI-assisted stalking, and burglar bots high concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

126) Experts consider AI-authored fake reviews, AI-assisted stalking, and burglar bots low concern AI-enabled crimes.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

127) Experts are moderately concerned with burglar bots.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

128) Experts have a low concern for burglar bots.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

129) Experts are moderately concerned with data poisoning.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

130) If spam e-mails are labeled as safe, this is called data poisoning.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

131) Expert systems are an application of AI that provide systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

132) Machine learning is an application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

133) Traditional programming is an unstructured combination of data and a computer algorithm that produces answers.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

134) Traditional programming is a structured combination of data and a computer algorithm that produces answers.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

135) Supervised machine learning is an unstructured combination of data and a computer algorithm that produces answers.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

136) Supervised machine learning is a structured combination of data and a computer algorithm that produces answers.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

137) In traditional programming, developers train the system with labeled input data and the expected output results.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

138) In supervised machine learning, developers train the system with labeled input data and the expected output results.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

139) Neural networks are computer systems that attempt to mimic human experts by applying expertise in a specific domain.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

140) Expert systems are computer systems that attempt to mimic human experts by applying expertise in a specific domain.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

141) Transferring domain expertise from human experts to the expert system is fairly straightforward since experts have to be able to explain what they know to be experts.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

142) Transferring domain expertise from human experts to the expert system can be difficult because humans cannot always explain how they know what they know.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

143) If human experts can explain their entire reasoning process, an expert programmer can automate that process with computer code.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

144) Even if human experts can explain their entire reasoning process, it may still be impossible to automate that process.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

145) Machine learning systems require human experts.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

146) Expert systems require human experts.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

147) Expert systems always replace human decision makers.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

148) Machine learning must be formally structured in the form of rules.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

149) Expert systems must be formally structured in the form of rules.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

150) Expert system algorithms learn from ingesting vast amounts of data and by adjusting hyperparameters and parameters.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

151) Machine learning algorithms learn from ingesting vast amounts of data and by adjusting hyperparameters and parameters.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

152) Machine learning algorithms are unbiased.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

153) Data movement is a deep learning bias that comes from a mismatch between the data used to train and test the system and the data the system actually encounters in the real world.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

154) Data shift is a machine learning bias that comes from a mismatch between the data used to train and test the system and the data the system actually encounters in the real world.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

155) Machine learning systems used for criminal risk assessment have been found to be biased against people of color.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

156) Machine learning systems trained on datasets collected from biased samples can exhibit these biases when they are used; this is a problem called data shift.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

157) Machine learning systems trained on datasets collected from biased samples can exhibit these biases when they are used; this is a problem called algorithmic bias.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

158) A false negative is a result that indicates that a given condition exists when it in fact does not.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

159) A false positive is a result that indicates that a given condition exists when it in fact does not.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

160) If a machine learning system identifies an email as spam when it is not, this is called a false negative.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

161) If a machine learning system identifies an email as spam when it is not, this is called a false positive.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

162) Classification and SEM analysis are important techniques for supervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

163) Classification and regression analysis are important techniques for supervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

164) Linear regression refers to a predictive modeling problem in which the system generates a class label for a given set of input data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

165) Classification refers to a predictive modeling problem in which the system generates a class label for a given set of input data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

166) There are three types of classification for supervised learning: binary, multi-class, and multi-label.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

167) There are four types of classification for supervised learning: binary, multi-class, multi-label, and imbalanced.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

168) Cluster analysis is an important technique for supervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

169) Classification and regression analysis are important techniques for supervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

170) Classification and regression analysis are important techniques for semi-supervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

171) Supervised learning is an excellent text document classifier because it is easy to find a large amount of labeled text documents.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

172) Semi-supervised learning is an excellent text document classifier because it is difficult to find a large amount of labeled text documents.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

173) The best time to use supervised learning is when an organization does not have data on desired outcomes.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

174) The best time to use unsupervised learning is when an organization does not have data on desired outcomes.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

175) Cluster analysis is an important technique for unsupervised learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

176) Any time a learning system uses neural networks, it is called a deep learning system.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge, Comprehension

AACSB: Technology

177) Machine learning is a subset of deep learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

178) Deep learning systems require structured data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

179) Deep learning systems can handle data sets that are very diverse and unstructured.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

180) Deep learning systems must be exposed to labeled historical/training data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

181) Deep learning systems do not need to be exposed to labeled historical or training data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

182) Frito-Lay “paints” chips and then uses computer vision to determine the chip’s texture.

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

183) Frito-Lay “paints” chips and then uses machine learning algorithms to listen to the sounds coming from the chip to determine its texture.

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

184) PepsiCo used Robot Vera to phone and interview candidates for sales, driver, and factory positions in China.

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

185) PepsiCo used Robot Vera to phone and interview candidates for sales, driver, and factory positions in Russia.

Difficulty: Easy

Section Reference 1: IT’s About Business 14.1

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

186) A hyperparameter in a neural network consists of software that has one or more weighted input connections, a bias function, and activation function, and one or more output connections.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

187) A node in a neural network consists of software that has one or more weighted input connections, a bias function, and activation function, and one or more output connections.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

188) A deep neural network contains multiple visible layers.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

189) A deep neural network contains multiple hidden layers.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

190) The activation rate in a neural network is how much the system is allowed to change the parameters after each training iteration.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

191) The learning rate in a neural network is how much the system is allowed to change the parameters after each training iteration.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

192) The activation functions that reside at each node in a neural network define the output of that node given an input or a set of inputs.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

193) The hyperparameters that reside at each node in a neural network define the output of that node given an input or a set of inputs.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

194) Weights and biases are examples of hyperparameters in a neural network.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

195) Weights and biases are examples of parameters in a neural network.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

196) The difference between the derived data value and the expected value initiates the process of front propagation.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

197) The difference between the derived data value and the expected value initiates the process of back propagation.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

198) A convolutional neural network is designed to access previous data such as sequential data or time series data during iterations of input.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

199) A recurrent neural network is designed to access previous data such as sequential data or time series data during iterations of input.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

200) Recurrent neural networks are highly effective for image and pattern recognition applications.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

201) Convolutional neural networks are highly effective for image and pattern recognition applications.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

202) Neural networks rarely make mistakes given the amount of data used to train them.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

203) Recurrent neural networks perform well in applications where filling in missing or incomplete data may be required.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

204) Generative adversarial networks perform well in applications where filling in missing or incomplete data may be required.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

205) Convolutional neural networks can be used to create deepfakes ranging from image manipulation to news embellishments.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

206) Generative adversarial networks can be used to create deepfakes ranging from image manipulation to news embellishments.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

35) Natural image processing refers to the ability of information systems to identify objects, scenes, and activities in images.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

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

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

208) Natural image processing systems can extract meaning from text and can generate text that is readable, stylistically natural, and grammatically correct.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

209) Drones share jobs with humans on the factory floor.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

210) Cobots share jobs with humans on the factory floor.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

211) Natural language processing focuses on automatically and accurately transcribing human speech.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

212) Speech recognition focuses on automatically and accurately transcribing human speech.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

213) Chatbots communicate with a user; this makes them intelligent agents.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

214) Chatbots are not considered intelligent.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

215) Intelligent agents are less advanced than chatbots.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

216) Intelligent agents are more advanced than chatbots.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

217) Chatbots do not require a knowledge base; they can offer basic problem solving.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

218) Intelligent agents do not simply provide answers from a knowledge base; they can offer basic problem solving.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

219) Information agents and predictive agents perform the same sorts of tasks.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

220) A monitoring and surveillance agent is also called a predictive agent.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

221) Chatbots are useful in call centers.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

222) Customer churn has so many factors associated with it that machine learning has not proven to be particularly effective yet.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

223) Dynamic pricing is common in hospitality, travel, entertainment, and retail.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

224) Dynamic pricing is related to real-time pricing.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

225) An intelligent factory is a highly digitized operation that continuously collects and shares data through connected machines, devices, and production systems.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

226) An smart factory is a highly digitized operation that continuously collects and shares data through connected machines, devices, and production systems.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

227) RFID is a key enabler of smart factories.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

228) The industrial IoT is a key enabler of smart factories.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

229) Virtual reality is a virtual, digital model of a machine or a person created from real-time and historical data.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

230) A virtual twin is a virtual, digital model of a machine or a person created from real-time and historical data.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

231) A digital twin is a virtual, digital model of a machine or a person created from real-time and historical data.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

232) DevOps bots are highly developed expert system-powered bots that assist in all stages of the software development process.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

233) DevOps bots are still under development; they are machine learning-powered bots that assist in all stages of the software development process.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

234) McDonald’s implemented Dynamic Yield’s personalization functionality on electronic ordering boards at its drive-through locations to deliver personalized offerings, to appeal more to customers, and to help the kitchen during periods of peak activity.

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

235) Chick-Fil-A implemented Dynamic Yield’s personalization functionality on electronic ordering boards at its drive-through locations to deliver personalized offerings, to appeal more to customers, and to help the kitchen during periods of peak activity.

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

236) McDonald’s tracks all approaching customers on their mobile apps using geofencing, license plate recognition, and beacons.

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

237) McDonald’s is capable of tracking approaching customers on their mobile apps using geofencing, license plate recognition, and beacons if they opt-in to the service.

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

238) Apprente uses speech recognition because it is the most accurate way to convert speech to text.

Difficulty: Easy

Section Reference 1: Closing Case

Bloomcode: Knowledge

AACSB: Technology

Question type: Text Entry:

239) ________________________________________ is the theory and development of information systems able to perform tasks that normally require human intelligence.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

240) The ________ test proposes a scenario in which a man and a computer both pretend to be human, and a human interviewer has to identify which is real.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

241) ____________ AI is hypothetical AI that matches or exceeds human intelligence.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

242) Strong AI is ____________ AI that matches or exceeds human intelligence.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

243) ____________ AI could be considered to have consciousness or sentience.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

244) ____________ AI performs a useful and specific function that once required human intelligence to perform, and it does so at human levels or better.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

245) ____________ is an attack that tries to manipulate the training dataset of a machine learning system to control the predictive behavior of a trained model such that the model will label malicious examples into a desired class.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

246) Data poisoning is an attack that tries to manipulate the training dataset of a(n) ________________ system to control the predictive behavior of a trained model such that the model will label malicious examples into a desired class.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

247) A(n) ____________________ is a problem-solving method expressed as a finite sequence of steps.

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge

AACSB: Technology

248) ____________ is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

249) ________________________ are computer systems that attempt to mimic human experts by applying expertise in a specific domain.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

250) ________________________ systems require human experts to provide the knowledge for the system; ______________ systems do not require human experts but learn from ingesting vast amounts of data and by adjusting hyperparameters and parameters.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

251) A(n) __________________ comes from a mismatch between the data used to train and test the system and the data the system actually encounters in the real world.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

252) ________________________ is a type of machine learning in which the system is given labeled input data and the expected output results.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

253) Supervised learning is a type of ____________ in which the system is given labeled input data and the expected output results.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

254) ________________________ is a type of machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

255) ________________________ is a type of machine learning that searches for previously undetected patterns in a data set with no pre-existing labels and with minimal human supervision.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

256) Cluster analysis is one of the primary techniques in ____________ learning.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

257) ________________________ groups, or segments, data points to identify common characteristics and then reacts based on whether each new piece of data exhibits these characteristics.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

258) ________________________ is a type of machine learning in which the system learns to achieve a goal in an uncertain, potentially complex environment.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

259) ________________________ is a type of machine learning in which artificial neural networks learn from large amounts of data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

260) Deep learning is a type of ________________ in which artificial neural networks learn from large amounts of data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

261) Deep learning is a type of machine learning in which artificial _____________ learn from large amounts of data.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

262) Deep learning is a type of machine learning in which artificial neural networks learn from _________________.

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge

AACSB: Technology

263) A(n) _______________________ is a set of virtual neurons or nodes that work in parallel to simulate the way the human brain works.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

264) A(n) _______________________ in a neural network consists of software that has one or more weighted input connections, a bias function, an activation function, and one or more output connections.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

265) A(n) _______________________ neural network is designed to access previous data such as sequential data or time series data during iterations of input.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

266) A(n) _______________________ neural network is designed to separate areas of image inputs by extracting features to identify edges, curves, and color density and then recombine these inputs for classification and prediction.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

267) A(n) _______________________ network consists of two neural networks that compete with each other in a zero-sum game in an effort to segregate real data from synthetic data.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge

AACSB: Technology

268) ________________________ refers to the ability of information systems to identify objects, scenes, and activities in images.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

269) __________________________ refers to the ability of information systems to work with text that way that humans do.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

270) ____________________________ focuses on automatically and accurately transcribing human speech.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

271) A(n) _________________________ agent is a software program that assists users, or acts on their behalf, in performing computer-related tasks.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

272) __________________________ agents search for information and display it to users.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

273) __________________________ agents constantly observe and report on some item of interest.

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge

AACSB: Technology

274) A(n) ______________________ is an independent examination of the financial information of organizations to determine whether their financial statements are accurate.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

275) A(n) ______________________ is a small, programmable device that provides access to a physical object.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

276) _________________________ are a special type of chatbot that analyze each customer’s portfolio, risk tolerance, and previous investment decisions to offer advice to financial advisors on portfolio management and investment rebalancing decisions.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

277) ________________________ is the number of customers who ended their relationship with a business.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

278) _______________________ allows businesses to offer flexible prices for the product and services they offer.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

279) __________________________ is the process of showing two versions of, for example, the same web page to different segments of website visitors at the same time and comparing which version drives more purchases or signups.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

280) A(n) ______________________ is a vehicle capable of sensing its environment and moving safely with little or no human input.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

281) ___________________________ is the process by which new employees acquire the knowledge, skills, and behaviors they need to become effective organizational members.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

282) A(n) ______________________ is a virtual, digital model of a machine or a person created from real-time and historical data.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

283) _____________________ is the combination of cultural philosophies, practices, and tools that increase an organization’s ability to quickly develop and deliver applications.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

284) ___________________ refers to a situation in which a version of the final software package is always ready to be released but is not sent to production before the decision is made to release it.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

285) _____________________________________ is a form of continuous deployment and is a strategy to categorize the unique states of computer software as it is developed and released.

Difficulty: Easy

Section Reference 1: 14.5 Artificial Intelligence in the Functional Areas

Bloomcode: Knowledge

AACSB: Technology

Question type: Essay

286) What are the advantages and disadvantages of AI? Why would a business want to use AI?

Disadvantages: privacy, security, loss of jobs, low/uninspired creativity, limited use of sensory experiences, not always great at recognizing patterns and relationships, reasoning only in narrow/focused/stable domains

Business – replace simple human tasks with a cheap computer

Difficulty: Easy

Section Reference 1: 14.1 Introduction to Artificial Intelligence

Learning Objective 14.1: Explain the potential value and the potential limitations of artificial intelligence.

Bloomcode: Knowledge, Comprehension, Application

AACSB: Technology, Reflective Thinking

287) What are some of the barriers to using expert systems? Why may the CEO position be hard to replace with an expert system?

Automating the process may not be possible if the process is too complex or too vague or if the process requires too many rules (the CEO role is very complex particularly for large companies in regard to managing all the different functional areas…there are too many variables to consider for every decision)

Difficulty: Hard

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge, Comprehension, Application, Analysis, Synthesis, Evaluation

AACSB: Technology, Analytic, Reflective Thinking

288) List and describe the various types of biases that exist with machine learning systems.

How developers approach a problem – when there are multiple answers to a problem, the way the developer solves the problem will bias the answers towards that particular solution and ignore the other possible solutions

How data can bias a ML system – data shift = mismatch between the data used to train and test the system and the data the system actually encounters in the real world and algorithmic bias = ML systems trained on datasets collected from biased samples can exhibit these biases when they are used

Difficulty: Medium

Section Reference 1: 14.2 Machine Learning and Deep Learning

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge, Comprehension

AACSB: Technology, Analytic, Reflective Thinking

289) What AI technology helps prevent fraud? How does it detect fraud? How is this useful to you?

Specifically, imbalanced classification helps with fraud detection

Useful – can trust credit card information won’t get stolen (although credit card companies assume all the costs, it is still a pain to get new credit cards); see also Section 14.5 p438 Auditing, p441 Security, p443 Insurance and Risk Management Claim settlement and Compliance issues, and p447 Along Supply Chains

Difficulty: Easy

Section Reference 1: 14.2 Machine Learning and Deep Learning

Section Reference 2: 14.5 Artificial Intelligence in the Functional Areas

Learning Objective 14.2: Differentiate among supervised, semi-supervised, unsupervised, reinforcement, and deep learning.

Bloomcode: Knowledge, Comprehension, Application

AACSB: Technology, Reflective Thinking

290) List and describe the three types of neural networks. Provide an example of each.

Difficulty: Easy

Section Reference 1: 14.3 Neural Networks

Learning Objective 14.3: Describe the structure of a neural network and discuss how that structure contributes to the purpose of neural networks in machine learning.

Bloomcode: Knowledge, Comprehension

AACSB: Technology

291) List and describe the two types of intelligent agents. Provide an example of each.

Predictive or Monitoring and Surveillance agent – constantly observe and report on some item of interest; example = Allstate to monitor agent computers, monitor prices changes for airlines etc., monitor stock prices

Difficulty: Easy

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge, Comprehension

AACSB: Technology

292) List and describe the two types of intelligent agents. What intelligent agents do you use and how?

Information agent – search for information and display it to users; a buyer agent or shopping bot is one type that helps customers find the product and services they need on a web site

Predictive agent – constantly observe and report on some item of interest

Student piece – if they use Amazon.com, they use information agents through the “recommended for you” section; if they own stock, they may use predictive agents; if they fly or use eBay, they may use predictive agents to monitor price changes; if they use Gmail and star any e-mails or have any kind of sorting rules in place, they would use a user agent

Difficulty: Hard

Section Reference 1: 14.4 Artificial Intelligence Applications

Learning Objective 14.4: Provide use case examples of computer vision, natural language processing, robotics, image recognition, and intelligent agents.

Bloomcode: Knowledge, Comprehension, Application, Analysis

AACSB: Technology, Analytic, Reflective Thinking

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Chapter 14 Artificial Intelligence

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