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

Description

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. Cover Title Page Copyright Contents Acknowledgments Introduction Part I Systems and Artificial Intelligence Chapter 1 Artificial Intelligence, Science Fiction, and Fear 1.1 The Danger of AI 1.2 The Human Analogy 1.3 The Systems Analogy 1.4 Killer Robots 1.5 Watching the Watchers 1.6 Cybersecurity in a World of Fallible Humans 1.7 Imagining Failure 1.8 The New Role of Data: The Green School Bus Problem 1.9 Data Requirements 1.9.1 Diversity 1.9.2 Augmentation 1.9.3 Distribution 1.9.4 Synthesis 1.10 The Data Lifecycle 1.11 AI Systems and People Systems 1.12 Making an AI as Safe as a Human References Chapter 2 We Live in a World of Systems 2.1 What Is a System? 2.2 Natural Systems 2.3 Engineered Systems 2.4 Human Activity Systems 2.5 Systems as a Profession 2.5.1 Systems Engineering 2.5.2 Systems Science 2.5.3 Systems Thinking 2.6 A Biological Analogy 2.7 Emergent Behavior: What Makes a System, a System 2.8 Hierarchy in Systems 2.9 Systems Engineering Chapter 3 The Intelligence in the System: How Artificial Intelligence Really Works 3.1 What Is Artificial Intelligence? 3.1.1 Myth 1: AI Systems Work Just Like the Brain Does 3.1.2 Myth 2: As Neural Networks Grow in Size and Speed, They Get Smarter 3.1.3 Myth 3: Solving a Hard or Complex Problem Shows That an AI Is Nearing Human Intelligence 3.2 Training the Deep Neural Network 3.3 Testing the Neural Network 3.4 Annie Learns to Identify Dogs 3.5 How Does a Neural Network Work? 3.6 Features: Latent and Otherwise 3.7 Recommending Movies 3.8 The One‐Page Deep Neural Network Chapter 4 Intelligent Systems and the People they Love 4.1 Can Machines Think? 4.2 Human Intelligence vs. Computer Intelligence 4.3 The Chinese Room: Understanding, Intentionality, and Consciousness 4.4 Objections to the Chinese Room Argument 4.4.1 The Systems Reply to the CRA 4.4.2 The Robot Reply 4.4.3 The Brain Simulator Reply 4.4.4 The Combination Reply 4.4.5 The Other Minds Reply 4.4.6 The Many Mansions Reply 4.5 Agreement on the CRA 4.5.1 Analyzing the Systems Reply: Can the Room Understand when Searle Does Not? 4.6 Implementation of the Chinese Room System 4.7 Is There a Chinese‐Understanding Mind in the Room? 4.7.1 Searle and Block on Whether the Chinese Room Can Understand 4.8 Chinese Room: Simulator or an Artificial Mind? 4.8.1 Searle on Strong AI Motivations 4.8.2 Understanding and Simulation 4.9 The Mind of the Programmer 4.10 Conclusion References Part II Systems Engineering for Intelligent Systems Chapter 5 Designing Systems by Drawing Pictures and Telling Stories 5.1 Requirements and Stories 5.2 Stories and Pictures: A Better Way 5.3 How Systems Come to Be 5.4 The Paradox of Cost Avoidance 5.5 Communication and Creativity in Engineering 5.6 Seeing the Real Needs 5.7 Telling Stories 5.8 Bringing a Movie to Life 5.9 Telling System Stories 5.10 The Combination Pitch 5.11 Stories in Time 5.12 Roles and Personas Chapter 6 Use Cases: The Superpower of Systems Engineering 6.1 The Main Purpose of Systems Engineering 6.2 Getting the Requirements Right: A Parable 6.2.1 A Parable of Systems Engineering 6.3 Building a Home: A Journey of Requirements and Design 6.4 Where Requirements Come From and a Koan 6.4.1 A Requirements Koan 6.5 The Magic of Use Cases 6.6 The Essence of a Use Case 6.7 Use Case vs. Functions: A Parable 6.8 Identifying Actors 6.8.1 Actors Are Outside the System 6.8.2 Actors Interact with the System 6.8.3 Actors Represent Roles 6.8.4 Finding the Real Actors 6.8.5 Identifying Nonhuman Actors 6.8.6 Do We Have ALL the Actors? 6.9 Identifying Use Cases 6.10 Use Case Flows of Events 6.10.1 Balancing Work Up‐Front with Speed 6.10.2 Use Case Flows and Scenarios 6.10.3 Writing Alternate Flows 6.10.4 Include and Extend with Use Cases 6.11 Examples of Use Cases 6.11.1 Example Use Case 1: Request Customer Service from Acme Library Support 6.11.2 Example Use Case 2: Ensure Network Stability 6.11.3 Example Use Case 3: Search for Boat in Inventory 6.12 Use Cases with Human Activity Systems 6.13 Use Cases as a Superpower References Chapter 7 Picturing Systems with Model Based Systems Engineering 7.1 How Humans Build Things 7.2 C: Context 7.2.1 Actors for the VX 7.2.2 Actors for the Home System 7.3 U: Usage 7.4 S: States and Modes 7.5 T: Timing 7.6 A: Architecture 7.7 R: Realization 7.8 D: Decomposition 7.9 Conclusion Chapter 8 A Time for Timeboxes and the Use of Usage Processes 8.1 Problems in Time Modeling: Concurrency, False Precision, and Uncertainty 8.1.1 Concurrency 8.1.2 False Precision 8.1.3 Uncertainty 8.2 Processes and Use Cases 8.3 Modeling: Two Paradigms 8.3.1 The Key Observation 8.3.2 Source of the Problem 8.4 Process and System Paradigms 8.5 A Closer Examination of Time 8.6 The Need for a New Approach 8.7 The Timebox 8.8 Timeboxes with Timelines 8.8.1 Thinking in Timeboxes 8.9 The Usage Process 8.10 Pilot Project Examples 8.10.1 Pilot Project: The Hunt for Red October 8.10.2 Pilot Project: FAA 8.10.3 Pilot Project: IBM Agile Process 8.11 Summary: A New Paradigm Modeling Approach 8.11.1 The Impact of New Paradigm Models 8.11.2 The Future of New Paradigm Models References Part III Systems Thinking for Intelligent Systems Chapter 9 Solving Hard Problems with Systems Thinking 9.1 Human Activity Systems and Systems Thinking 9.2 The Central Insight of Systems Thinking 9.3 Solving Problems with Systems Thinking 9.4 Identify a Problem 9.5 Find the Real Problem 9.6 Identify the System 9.7 Understanding the System 9.7.1 Rocks Are Hard 9.7.2 Heart and Soul 9.7.3 Confusing Cause and Effect 9.7.4 Logical Fallacies 9.8 System Archetypes 9.8.1 Tragedy of the Commons 9.8.2 The Rich Get Richer 9.9 Intervening in a System 9.10 Testing Implementing Intervention Incrementally 9.11 Systems Thinking and the World Chapter 10 People Systems: A New Way to Understand the World 10.1 Reviewing Types of Systems 10.2 People Systems 10.3 People Systems and Psychology 10.4 Endowment Effect 10.5 Anchoring 10.6 Functional Architecture of a Person 10.7 Example: The Problem of Pollution 10.8 Speech Acts 10.8.1 People System Archetypes 10.8.1.1 Demand Slowing 10.8.1.2 Customer Service 10.9 Seeking Quality 10.10 Job Hunting as a People System 10.10.1 Who Are You? 10.10.2 What Do You Want to Do? 10.10.3 For Whom? 10.10.4 Pick a Few 10.10.5 Go Straight to the Hiring Manager 10.10.6 Follow Through 10.10.7 Broaden Your View 10.10.8 Step Two 10.11 Shared Service Monopolies References Index EULA