Engineering smarter systems : new approaches to systems engineering and design with artificial intelligence, machine learning and system modelling /: new approaches to systems engineering and design with artificial intelligence, machine learning and system modelling. (2021)

• Record Type: Book
• Further Information: Note: Barclay R. Brown.
• Authors: Brown, Barclay R
• Contents: 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 theWatchers 9 1.6 Cybersecurity in aWorld 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 WeLiveinaWorldofSystems 49 2.1 What Is a System? 49 2.2 Natural Systems 51 2.3 Engineered Systems 53 Trim Size: 152mm x 229mm Single Column Brown665595 ftoc.tex V1 - 06/20/2022 11:02pm Page vi _ _ _ _ vi Contents 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 Trim Size: 152mm x 229mm Single Column Brown665595 ftoc.tex V1 - 06/20/2022 11:02pm Page vii _ _ _ _ Contents vii 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 BetterWay 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 5.12 Roles and Personas 161 6 Use Cases: The Superpower of Systems Engineering 165 6.1 The Main Purpose of Systems Engineering 165 6.2 Getting the Requirements Right: A Parable 166 6.2.1 A Parable of Systems Engineering 168 6.3 Building a Home: A Journey of Requirements and Design 170 6.4 Where Requirements Come From and a Koan 173 6.4.1 A Requirements Koan 177 6.5 The Magic of Use Cases 177 6.6 The Essence of a Use Case 181 6.7 Use Case vs. Functions: A Parable 184 6.8 Identifying Actors 186 6.8.1 Actors Are Outside the System 187 6.8.2 Actors Interact with the System 187 6.8.3 Actors Represent Roles 188 6.8.4 Finding the Real Actors 188 6.8.5 Identifying Nonhuman Actors 191 Trim Size: 152mm x 229mm Single Column Brown665595 ftoc.tex V1 - 06/20/2022 11:02pm Page viii _ _ _ _ viii Contents 6.8.6 DoWe Have ALL the Actors? 193 6.9 Identifying Use Cases 193 6.10 Use Case Flows of Events 196 6.10.1 BalancingWork Up-Front with Speed 199 6.10.2 Use Case Flows and Scenarios 201 6.10.3 Writing Alternate Flows 202 6.10.4 Include and Extend with Use Cases 203 6.11 Examples of Use Cases 205 6.11.1 Example Use Case 1: Request Customer Service from Acme Library Support 205 6.11.2 Example Use Case 2: Ensure Network Stability 206 6.11.3 Example Use Case 3: Search for Boat in Inventory 206 6.12 Use Cases with Human Activity Systems 207 6.13 Use Cases as a Superpower 208 References 208 7 Picturing Systems with Model Based Systems Engineering 209 7.1 How Humans Build Things 209 7.2 C: Context 212 7.2.1 Actors for the VX 213 7.2.2 Actors for the Home System 216 7.3 U: Usage 217 7.4 S: States and Mode
• Edition: 1st
• Publisher Details: Hoboken : John Wiley & Sons, Inc
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 620.001171; Systems engineering; Systems engineering -- Design; Artificial intelligence; Systems engineering -- Simulation methods
• Languages: English
• ISBNs: 9781119665632; 9781119665618
• Related ISBNs: 9781119665595
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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