Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
Instructor Manual
Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1:
Introduction to Artificial Intelligence
TABLE OF CONTENTS
Purpose and Perspective of the Chapter......................................................................2
List of Student Downloads.........................................................................................................................2
Chapter Objectives........................................................................................................2
Complete List of Chapter Activities and Assessments.................................................2
Key Terms.....................................................................................................................3
Chapter Outline.............................................................................................................6
Additional Activities and Assignments.......................................................................13
Additional Resources..................................................................................................14
Internet Resources....................................................................................................................................14
Appendix.....................................................................................................................15
Generic Rubrics....................................................................................................................................... 15
Standard Discussion Rubric.....................................................................................................................15
Standard Collaboration Rubric................................................................................................................ 15
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 1
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
PURPOSE AND PERSPECTIVE OF THE CHAPTER
The purpose of this chapter is to define artificial intelligence and explain how it has evolved over time.
Artificial intelligence (AI) aims to create computer systems that can mimic human intellect and behavior.
Early AI systems relied on explicit, human-defined rules to function. Modern AI systems learn from vast
datasets and adapt their behavior without explicit programming. Both of these types of systems, as well as
their roles in business and industries, are described. A brief introduction to the hardware that powers AI
computations is provided.
CHAPTER OBJECTIVES
The following objectives are addressed in this chapter:
1.1 Define artificial intelligence.
1.2 Explain the evolution of artificial intelligence.
1.3 Analyze the impact of AI hardware accelerators and their role in driving innovation across industries.
1.4 Evaluate generative AI’s role in automation, creativity, industry, and ethical challenges.
1.5 Determine how artificial intelligence is built without code.
COMPLETE LIST OF CHAPTER ACTIVITIES AND
ASSESSMENTS
The following table organizes activities and assessments by objective, so that you can see how all this
content relates to objectives and make decisions about which content you would like to emphasize in your
class based on your objectives. For additional guidance, refer to the Teaching Online Guide.
Chapter Activity/Assessment Source (i.e., PPT slide, Duration
Objective Workbook)
1.1, 1.3 Activity 1.1: Knowledge Check PPT Slides 41-42 10 minutes
1.5 Activity 1.2: Breakout Groups PPT Slide 48 20 minutes
1.4 Activity 1.3: Discussion PPT Slide 57 10 minutes
[return to top]
KEY TERMS
AI accelerators: Specialized chips designed specifically to improve the performance of AI tasks.
AI algorithms: Algorithms that analyze complex datasets and identify subtle patterns that humans may
miss, leading to more accurate predictions and informed decisions.
AI model: Software that uses data to recognize patterns and make decisions or predictions.
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 2
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
algorithm: Step-by-step set of instructions or rules designed to solve a problem or accomplish a
specific task.
algorithmic probability: Using algorithms to predict or infer the likelihood of something happening
given certain data.
artificial general intelligence (AGI): A type of artificial intelligence that can think and learn like
a human.
artificial intelligence (AI): Computer systems that can mimic human intelligence and behavior.
artificial narrow intelligence (ANI): A type of artificial intelligence that specializes in one task,
such as a voice assistant.
artificial super intelligence (ASI): A type of artificial intelligence that surpasses human
intelligence.
chatbot: AI-driven software that simulates human conversation to interact with users and assist in tasks
or provide information.
data augmentation: A technique used in machine learning to artificially increase the size and
diversity of a training dataset by applying transformations to the data.
decision tree: A machine learning algorithm that helps the computer decide what to do next by asking
“yes or no” questions.
deep learning (DL): A subset of artificial intelligence that uses neural networks with many layers to
model complex patterns in large datasets.
early AI: Systems that rely heavily on human-defined rules, which require following a strict recipe.
edge AI: Deploying AI on local devices, enabling real-time processing without relying solely on cloud
computing.
evolution of AI: The progression from rule-based systems to more advanced, autonomous systems.
explainable AI (XAI): A branch of artificial intelligence focused on revealing how AI makes its
decisions.
generative AI (GenAI or GAI): An area of deep learning that focuses on creating new content such
as art, music, or even stories using trained patterns of data.
human intelligence: The innate ability of humans to learn, reason, adapt, and apply knowledge to
solve problems.
inductive inference: The process of generalizing from specific examples.
integrated chip: The tiny powerful brain in modern computers. Also known as an integrated circuit,
microchip, or chip.
integrated circuit (IC): A set of electronic circuits that power the electronic components of a
computer. Also known as an integrated chip, microchip, or chip.
intelligent automation: Systems that can learn and adapt while performing automated tasks.
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 3
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
large language model (LLM): AI technology that can interpret and generate human language.
logic gate number: A number that refers to the total count of individual logic gates in an integrated
circuit.
machine learning (ML): Training algorithms to learn from data and improve over time without
being explicitly programmed.
machine learning algorithm: A rule or process an AI system uses to conduct tasks.
microchip: A specific type of integrated chip that powers modern computers.
modern AI: Machine learning that enables systems to learn from vast datasets.
multimodal AI: Artificial intelligence systems that can process and understand multiple types of data
or inputs such as text, images, audio, and video simultaneously.
natural language processing (NLP): A technology for interacting with humans using natural
language, such as text or speech, in a meaningful way.
neural network: A program that processes information and makes decisions in ways similar to the
human brain. It uses interconnected nodes (artificial neurons) in a layered structure to mimic how neurons
in the brain communicate.
no-code AI platforms: AI solutions built without extensive programming knowledge.
no-code development: An approach to programming that allows coders (programmers) and even
nontechnical users to build applications through visual modeling tools.
reinforcement learning (RL): A machine learning technique that trains software to make decisions
by interacting with its environment and receiving rewards or penalties for its actions.
stages of artificial intelligence: Subsets of artificial intelligence that have evolved over time.
symbolic AI: An approach to artificial intelligence that represents knowledge and reasoning using
symbols, rules, and logic, and explores how computers can solve problems and reason logically.
synthetic data: Data generated through algorithms or simulations to mirror the statistical properties of
real-world data.
systolic array: A technology that AI accelerators use to reduce data movement and enhance speed.
transistor count: The total number of transistors a chip contains.
Turing test: A test that assesses a machine’s ability to demonstrate intelligent behavior
indistinguishable from that of a human. Also called the imitation game.
[return to top]
CHAPTER OUTLINE
1.1Cut Through the Hype to Clarify What AI Truly Means (Objective 1.1, PPT
Slides 4-15)
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 4
Intelligence
Instructor Manual
Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1:
Introduction to Artificial Intelligence
TABLE OF CONTENTS
Purpose and Perspective of the Chapter......................................................................2
List of Student Downloads.........................................................................................................................2
Chapter Objectives........................................................................................................2
Complete List of Chapter Activities and Assessments.................................................2
Key Terms.....................................................................................................................3
Chapter Outline.............................................................................................................6
Additional Activities and Assignments.......................................................................13
Additional Resources..................................................................................................14
Internet Resources....................................................................................................................................14
Appendix.....................................................................................................................15
Generic Rubrics....................................................................................................................................... 15
Standard Discussion Rubric.....................................................................................................................15
Standard Collaboration Rubric................................................................................................................ 15
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 1
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
PURPOSE AND PERSPECTIVE OF THE CHAPTER
The purpose of this chapter is to define artificial intelligence and explain how it has evolved over time.
Artificial intelligence (AI) aims to create computer systems that can mimic human intellect and behavior.
Early AI systems relied on explicit, human-defined rules to function. Modern AI systems learn from vast
datasets and adapt their behavior without explicit programming. Both of these types of systems, as well as
their roles in business and industries, are described. A brief introduction to the hardware that powers AI
computations is provided.
CHAPTER OBJECTIVES
The following objectives are addressed in this chapter:
1.1 Define artificial intelligence.
1.2 Explain the evolution of artificial intelligence.
1.3 Analyze the impact of AI hardware accelerators and their role in driving innovation across industries.
1.4 Evaluate generative AI’s role in automation, creativity, industry, and ethical challenges.
1.5 Determine how artificial intelligence is built without code.
COMPLETE LIST OF CHAPTER ACTIVITIES AND
ASSESSMENTS
The following table organizes activities and assessments by objective, so that you can see how all this
content relates to objectives and make decisions about which content you would like to emphasize in your
class based on your objectives. For additional guidance, refer to the Teaching Online Guide.
Chapter Activity/Assessment Source (i.e., PPT slide, Duration
Objective Workbook)
1.1, 1.3 Activity 1.1: Knowledge Check PPT Slides 41-42 10 minutes
1.5 Activity 1.2: Breakout Groups PPT Slide 48 20 minutes
1.4 Activity 1.3: Discussion PPT Slide 57 10 minutes
[return to top]
KEY TERMS
AI accelerators: Specialized chips designed specifically to improve the performance of AI tasks.
AI algorithms: Algorithms that analyze complex datasets and identify subtle patterns that humans may
miss, leading to more accurate predictions and informed decisions.
AI model: Software that uses data to recognize patterns and make decisions or predictions.
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 2
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
algorithm: Step-by-step set of instructions or rules designed to solve a problem or accomplish a
specific task.
algorithmic probability: Using algorithms to predict or infer the likelihood of something happening
given certain data.
artificial general intelligence (AGI): A type of artificial intelligence that can think and learn like
a human.
artificial intelligence (AI): Computer systems that can mimic human intelligence and behavior.
artificial narrow intelligence (ANI): A type of artificial intelligence that specializes in one task,
such as a voice assistant.
artificial super intelligence (ASI): A type of artificial intelligence that surpasses human
intelligence.
chatbot: AI-driven software that simulates human conversation to interact with users and assist in tasks
or provide information.
data augmentation: A technique used in machine learning to artificially increase the size and
diversity of a training dataset by applying transformations to the data.
decision tree: A machine learning algorithm that helps the computer decide what to do next by asking
“yes or no” questions.
deep learning (DL): A subset of artificial intelligence that uses neural networks with many layers to
model complex patterns in large datasets.
early AI: Systems that rely heavily on human-defined rules, which require following a strict recipe.
edge AI: Deploying AI on local devices, enabling real-time processing without relying solely on cloud
computing.
evolution of AI: The progression from rule-based systems to more advanced, autonomous systems.
explainable AI (XAI): A branch of artificial intelligence focused on revealing how AI makes its
decisions.
generative AI (GenAI or GAI): An area of deep learning that focuses on creating new content such
as art, music, or even stories using trained patterns of data.
human intelligence: The innate ability of humans to learn, reason, adapt, and apply knowledge to
solve problems.
inductive inference: The process of generalizing from specific examples.
integrated chip: The tiny powerful brain in modern computers. Also known as an integrated circuit,
microchip, or chip.
integrated circuit (IC): A set of electronic circuits that power the electronic components of a
computer. Also known as an integrated chip, microchip, or chip.
intelligent automation: Systems that can learn and adapt while performing automated tasks.
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 3
, Instructor Manual: Riggins/Osei, Artificial Intelligence and Machine Learning, 1e ©28, 9798214002163; Chapter 1: Introduction to Artificial
Intelligence
large language model (LLM): AI technology that can interpret and generate human language.
logic gate number: A number that refers to the total count of individual logic gates in an integrated
circuit.
machine learning (ML): Training algorithms to learn from data and improve over time without
being explicitly programmed.
machine learning algorithm: A rule or process an AI system uses to conduct tasks.
microchip: A specific type of integrated chip that powers modern computers.
modern AI: Machine learning that enables systems to learn from vast datasets.
multimodal AI: Artificial intelligence systems that can process and understand multiple types of data
or inputs such as text, images, audio, and video simultaneously.
natural language processing (NLP): A technology for interacting with humans using natural
language, such as text or speech, in a meaningful way.
neural network: A program that processes information and makes decisions in ways similar to the
human brain. It uses interconnected nodes (artificial neurons) in a layered structure to mimic how neurons
in the brain communicate.
no-code AI platforms: AI solutions built without extensive programming knowledge.
no-code development: An approach to programming that allows coders (programmers) and even
nontechnical users to build applications through visual modeling tools.
reinforcement learning (RL): A machine learning technique that trains software to make decisions
by interacting with its environment and receiving rewards or penalties for its actions.
stages of artificial intelligence: Subsets of artificial intelligence that have evolved over time.
symbolic AI: An approach to artificial intelligence that represents knowledge and reasoning using
symbols, rules, and logic, and explores how computers can solve problems and reason logically.
synthetic data: Data generated through algorithms or simulations to mirror the statistical properties of
real-world data.
systolic array: A technology that AI accelerators use to reduce data movement and enhance speed.
transistor count: The total number of transistors a chip contains.
Turing test: A test that assesses a machine’s ability to demonstrate intelligent behavior
indistinguishable from that of a human. Also called the imitation game.
[return to top]
CHAPTER OUTLINE
1.1Cut Through the Hype to Clarify What AI Truly Means (Objective 1.1, PPT
Slides 4-15)
© 2028 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part. 4
