Barclay R. Brown

Engineering Intelligent Systems (eBook, PDF)

Systems Engineering and Design with Artificial Intelligence, Visual Modeling, and Systems Thinking

• Format: PDF

Produktbeschreibung

Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: * An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter * An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required * An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering * An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence * Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle * A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers.

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Produktdetails

• Verlag: For Dummies
• Seitenzahl: 384
• Erscheinungstermin: 9. September 2022
• Englisch
• ISBN-13: 9781119665618
• Artikelnr.: 66270920

Autorenporträt

Barclay R. Brown, PhD, is the Associate Director for Artificial Intelligence for Collins Aerospace, a division of Raytheon Technologies. Prior to that he was an Engineering Fellow in the missile systems division of Raytheon, and before that served as a Global Solution Architect for IBM, working with systems engineering and AI products. Dr. Brown has been a practitioner, consultant and speaker on artificial intelligence, systems engineering, and software development for over 25 years and holds degrees in electrical engineering, psychology, business, and systems engineering. He is a certified Expert Systems Engineering Professional and CIO of INCOSE, the International Council on Systems Engineering.

Inhaltsangabe

Acknowledgments xi

Introduction xiii

Part I Systems and Artificial Intelligence 1

1 Artificial Intelligence, Science Fiction, and Fear 3

1.1 The Danger of AI 3

1.2 The Human Analogy 5

1.3 The Systems Analogy 6

1.4 Killer Robots 7

1.5 Watching the Watchers 9

1.6 Cybersecurity in a World of Fallible Humans 12

1.7 Imagining Failure 17

1.8 The New Role of Data: The Green School Bus Problem 23

1.9 Data Requirements 25

1.9.1 Diversity 26

1.9.2 Augmentation 28

1.9.3 Distribution 29

1.9.4 Synthesis 30

1.10 The Data Lifecycle 31

1.11 AI Systems and People Systems 41

1.12 Making an AI as Safe as a Human 45

References 48

2 We Live in a World of Systems 49

2.1 What Is a System? 49

2.2 Natural Systems 51

2.3 Engineered Systems 53

2.4 Human Activity Systems 54

2.5 Systems as a Profession 54

2.5.1 Systems Engineering 54

2.5.2 Systems Science 55

2.5.3 Systems Thinking 55

2.6 A Biological Analogy 56

2.7 Emergent Behavior: What Makes a System, a System 56

2.8 Hierarchy in Systems 60

2.9 Systems Engineering 64

3 The Intelligence in the System: How Artificial Intelligence

Really Works 71

3.1 What Is Artificial Intelligence? 71

3.1.1 Myth 1: AI SystemsWork Just Like the Brain Does 72

3.1.2 Myth 2: As Neural Networks Grow in Size and Speed, They Get Smarter 72

3.1.3 Myth 3: Solving a Hard or Complex Problem Shows That an AI Is Nearing Human Intelligence 73

3.2 Training the Deep Neural Network 75

3.3 Testing the Neural Network 76

3.4 Annie Learns to Identify Dogs 76

3.5 How Does a Neural NetworkWork? 80

3.6 Features: Latent and Otherwise 81

3.7 Recommending Movies 82

3.8 The One-Page Deep Neural Network 84

4 Intelligent Systems and the People they Love 97

4.1 Can Machines Think? 97

4.2 Human Intelligence vs. Computer Intelligence 98

4.3 The Chinese Room: Understanding, Intentionality, and Consciousness 99

4.4 Objections to the Chinese Room Argument 104

4.4.1 The Systems Reply to the CRA 104

4.4.2 The Robot Reply 104

4.4.3 The Brain Simulator Reply 105

4.5 Agreement on the CRA 107

4.5.1 Analyzing the Systems Reply: Can the Room Understand when Searle Does Not? 109

4.6 Implementation of the Chinese Room System 114

4.7 Is There a Chinese-Understanding Mind in the Room? 115

4.7.1 Searle and Block on Whether the Chinese Room Can Understand 116

4.8 Chinese Room: Simulator or an Artificial Mind? 118

4.8.1 Searle on Strong AI Motivations 120

4.8.2 Understanding and Simulation 121

4.9 The Mind of the Programmer 127

4.10 Conclusion 133

References 135

Part II Systems Engineering for Intelligent Systems 137

5 Designing Systems by Drawing Pictures and Telling Stories 139

5.1 Requirements and Stories 139

5.2 Stories and Pictures: A Better Way 141

5.3 How Systems Come to Be 141

5.4 The Paradox of Cost Avoidance 145

5.5 Communication and Creativity in Engineering 147

5.6 Seeing the Real Needs 148

5.7 Telling Stories 150

5.8 Bringing a Movie to Life 153

5.9 Telling System Stories and the Combination Pitch 157

5.10 The Combination Pitch 159

5.11 Stories in Time 160
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