Engineering intelligent systems systems engineering and design with artificial intelligence, visual modeling, and systems thinking
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 an...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Incorporated
[2022]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009703309306719 |
Tabla de Contenidos:
- 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.
