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Full Description
A detailed guide to the evolution of engineering in the face of intelligent systems, including artificial intelligence
In The AI-Enabled Engineer: A Comprehensive Framework for Engineering Excellence in the Age of Intelligence , veteran senior systems engineer Kevin Cai delivers an expert discussion of the evolution of modern engineering. From computer applications and the expansion of the internet to data digitalization and the emergence of contemporary artificial intelligence, the author offers practical insights into foundational concepts and promising technological trends that could define the discipline in the coming years.
The author balances its coverage of hardware, software, and AI technologies, providing an up-to-date guide to assist young engineers as they navigate complex engineering challenges. He emphasizes the broader societal and technological implications of new developments in intelligent systems, exploring the social responsibilities of practicing engineers.
The book also provides:
Expert guidance for developing and implementing complex methodological framework
An insightful linking of foundational theoretical engineering principles with real-world applications
Practical discussions of likely future developments in technology and engineering practice
Original treatments of engineering's evolving role in societal innovation
Perfect for engineering students in graduate and doctoral programs, The AI-Enabled Engineer will also benefit systems engineers working in the technology and communications industries, hardware and software integration specialists, and professionals involved with AI and emerging technologies.
Contents
About the Author xi
Foreword xiii
Preface xv
Acknowledgments xix
1 Introduction 1
1.1 Intent of This Book 3
1.1.1 Engineering Principles, Methods, and Approaches 3
1.1.2 Distinctive Approaches 5
1.1.2.1 A Combinational Angle of Perspectives 5
1.1.2.2 Engineering in the Age of Intelligence 5
1.1.2.3 Principles and Approaches Duality 6
1.1.2.4 Engineering's Social Onus 6
1.1.2.5 Hardware's Purposes and Evolution 6
1.1.2.6 Engineering Equations Train AI 6
1.1.2.7 Future Engineering Pathways 7
1.1.2.8 New Competitiveness Definition 8
1.1.2.9 Introduction of Engineering Applications to Social Domains 8
1.1.2.10 Prospecting Future Technologies 8
1.1.2.11 Terminologies 8
1.1.3 Important Distinctions 12
1.1.3.1 Distinguishing the AI Era from the Age of Intelligence 12
1.1.3.2 Understanding Overlapping Timeframes 12
1.2 Outlines 13
2 Engineering Methods 15
2.1 Mathematical Methods 15
2.2 Experimental Methods 18
2.3 Approximation Methods 20
2.4 Observation Methods 22
2.5 System Methods 24
2.5.1 Disciplinary Variations in System Methods 28
2.5.1.1 Mechanical Engineering 28
2.5.1.2 Electrical Engineering 28
2.5.1.3 Civil Engineering 29
2.5.1.4 Biomedical Engineering 29
2.5.1.5 Interdisciplinary System 30
2.5.1.6 Emerging Trends in System Methods 30
2.6 Hybrid Methods 31
2.6.1 AI Integration and the Future of Engineering Methodology (Present-) 35
2.7 Data-driven Methods 36
2.8 Chain of Thought (CoT) 38
3 Engineering Principles 41
3.0 First Principles 41
3.0.1 First Principles in Integrated Systems—Fundamental Approaches to Complex Engineering Challenges 44
3.0.1.1 Automotive Integrated Systems 44
3.0.1.2 Medical Diagnostic Integrated Systems 44
3.0.1.3 Aerospace Integrated Systems 44
3.0.1.4 Robotics and Autonomous Systems 44
3.0.1.5 Renewable Energy Systems 45
3.1 Non-absoluteness 45
3.2 Trial and Error 48
3.3 Open-Mindedness 50
3.4 Being Critical 52
3.5 Conservative Thinking 56
3.6 Practicality 58
3.6.1 Practicality as a Guiding Principle 59
3.7 Creativity 60
3.7.1 What Inspires Creativity? 61
3.7.2 How to Facilitate Creativity? 61
3.7.3 How to Evaluate Creativity? 61
3.7.4 Creativity as a Vital Engineering Principle 62
3.8 Non-overreliance on Technology 62
3.8.1 The Long-Term Implications 63
3.8.2 Preserving the Human Element in Engineering 64
3.9 Logical Thinking 64
3.9.1 Toward a Principle of Comprehensive Logical Reasoning 67
3.10 Engineering Prospecting 68
3.11 Majority Rule 71
3.12 Optimization 74
3.12.1 AI-Driven Optimization Approaches in Modern Engineering Systems 77
3.13 Simplification 78
3.14 Continuous Improvement 82
3.15 Digital System Innovation: Continuous Improvement in Software and Digital Platforms 85
3.15.1.1 Strategic Planning in Digital Ecosystems 85
3.15.1.2 Market Testing and Adaptive Development 85
3.15.1.3 Incubation of Technological Novelties 85
3.15.1.4 Engineering Training and Skill Development 85
3.15.1.5 Logistical Preparation in Digital Scaling 86
3.15.1.6 Financial Sustainability through Innovative Models 86
3.15.1.7 Competitive Dynamics in Digital Innovation 86
3.16 Pioneering 86
3.17 Thoroughness 89
3.18 Contingency Plans 92
3.19 Mission Orientation 95
3.20 Evidence-Based Decision-Making 97
3.21 Environment and Sustainability 100
3.22 AI-Driven Environmental Engineering—Transforming Sustainability Through Intelligent Design 104
3.23 First-order Thinking 104
3.23.1 Problem Statement 104
3.23.2 Integration of Engineering Principles 104
3.23.3 Engineering Maturity 105
3.23.4 Priority Setting 105
3.23.5 Resource Focus (80/20 Rule) 105
3.23.6 Business Habit Formation 105
3.23.7 Critical Focus Maintenance 105
3.23.8 Complex System Management 105
3.24 Conclusion 107
4 Engineering Approaches 109
4.1 Prototyping 110
4.2 Staging 114
4.3 Specification 119
4.4 Segmenting and Modularity 123
4.5 Field-Specific Examples of System Decomposition 126
4.6 Documenting 127
4.7 Spectrum Limiting 131
4.8 Validating 134
4.8.1 Validation Strategies for Complex Integrated Systems 137
4.8.1.1 Advanced Approaches in Modern Engineering 137
4.8.1.2 Multidimensional Validation Frameworks 137
4.8.1.3 Digital Twin Validation Technologies 137
4.8.1.4 Probabilistic Validation Methodologies 138
4.8.1.5 AI-Enhanced Validation Strategies 138
4.8.1.6 Cross-Disciplinary Validation Integration 138
4.8.1.7 Ethical and Safety Validation 138
4.8.1.8 Challenges in Integrated Systems Validation 138
4.8.1.9 Future Validation Paradigms 139
4.8.1.10 Conclusion 139
4.9 Interdisciplinary Interaction 139
4.10 Cost-Effectiveness 142
4.11 Reliability 145
4.12 Durability 148
4.13 Conservative Thinking as an Approach 151
4.14 Time-to-Market 154
4.14.1 AI's Transformative Impact on Engineering Development Cycles 157
4.14.1.1 Rapid Prototyping and Design Optimization 158
4.14.1.2 Predictive Development Modeling 158
4.14.1.3 Automated Testing and Validation 158
4.14.1.4 Resource Optimization and Allocation 158
4.14.1.5 Continuous Improvement and Knowledge Management 158
4.14.1.6 Risk Mitigation and Contingency Planning 159
5 Engineering Applications 161
5.1 Hardware 161
5.1.1 Purposes 162
5.1.2 Transformation 165
5.1.2.1 Mechanical Engineering 165
5.1.2.2 Electrical Engineering 166
5.1.2.3 Civil Engineering 167
5.1.2.4 Interdisciplinary Convergence 167
5.1.3 Pathways 168
5.1.4 Approaches 171
5.2 Real-World Examples of Successful Hardware-Software Integration 174
5.2.1 Competitiveness 175
5.2.2 Customer Satisfaction 178
5.2.3 Business Viability 182
5.2.4 Environmental Responsibility 185
5.2.5 Product Development Prediction 191
5.2.5.1 Predictive Capabilities in Product Development 191
5.2.5.2 AI's Transformative Impact 192
5.2.5.3 Real Case Study: Microsoft and PNNL's AI-Powered Battery Breakthrough 193
5.3 Software 193
5.3.1 Performance Characteristics 195
5.3.1.1 AI-Enabled Performance Improvements 199
5.3.2 Architecture and Design 199
5.3.3 Reliability and Fault Tolerance 202
5.3.4 Hardware-Software Security Integration 203
5.3.4.1 Software's Critical Role in Hardware Security 206
5.3.5 Software-Driven Resource Management 207
5.3.6 Software Testing and Validation 209
5.3.7 Software Development and System Maintenance 210
5.4 Engineering Application Examples 212
5.4.1 Sensor Technology 213
5.4.1.1 Types of Sensors 214
5.4.1.2 Integration Methods 215
5.4.1.3 Application Examples 216
5.4.2 Networking and Communication 216
5.4.2.1 Network Types 218
5.4.2.2 Key Characteristics 218
5.4.2.3 Integration Examples 219
5.4.3 Software Architecture 220
5.4.3.1 Design Patterns 220
5.4.3.2 Critical Features 221
5.4.3.3 Security Architecture 221
5.4.3.4 System Trade-offs 222
5.4.3.5 Implementation Examples 222
5.4.4 Intelligent Control Systems 222
5.4.4.1 Core Components 223
5.4.4.2 AI/ML Component Validation 223
5.4.4.3 Key Capabilities 224
5.4.4.4 Application Examples 224
5.4.5 Cross-Cutting Themes 225
5.4.6 Future Development 227
6 Non-engineering Applications 233
6.1 Survey Design 235
6.1.1 Problem Definition in Policy Surveys 237
6.1.1.1 Question Construction 238
6.1.1.2 Expected Results 239
6.1.1.3 Survey Goals 239
6.2 Objectivity Establishment 241
6.3 Objectivity Blueprint 244
6.4 Policymaking 250
7 Future Engineering Trends 255
7.1 AI Development 255
7.1.1 Present and Emerging Directions (5-10 Years) 256
7.1.2 Mid-term Development Focus 257
7.1.3 Long-term Directions 257
7.1.4 AI Training—New Directions 258
7.1.5 AI Applications in Engineering 261
7.1.6 AI-Robotics Fusion in Everyday Applications 261
7.2 Science, Engineering, and Social Sciences 262
7.3 Emerging Technologies 265
7.3.1 Quantum Computing 267
7.3.1.1 The Quantum-Classical Hybrid Paradigm 267
7.3.1.2 Transformative Applications 268
7.3.1.3 Technical Challenges and Solutions 269
7.3.1.4 The Road Ahead 270
7.3.2 Bio-inspired Engineering 271
7.3.2.1 Bio-Inspired Biomass Processing: Nature's Solutions for Sustainable Energy 273
7.3.3 Sustainability Engineering 275
7.3.4 Optoelectronics 276
7.3.4.1 Integrated Photonic Systems 276
7.3.4.2 Optical Communications Evolution 277
7.3.4.3 Photonic Sensing and Imaging 278
7.3.4.4 Photonic-Electronic Integration 278
7.3.4.5 Emerging Frontiers 279
7.3.4.6 Engineering Challenges and Recent Breakthroughs 279
7.3.4.7 Integration with Other Technologies 280
7.4 Engineering Practice in Complex Systems 280
8 Reflections and Looking Forward 283
8.1 Key Insights and Future Directions 283
8.2 Engineering Framework: Functional Categories and System Integration 284
8.2.1 Fundamental Analysis and Practical Support Categories (Chapter 2) 285
8.2.2 Mental Processing and Implementation Categories (Chapter 3) 285
8.2.3 Strategic Approaches as Lifecycle Integration (Chapter 4) 286
8.2.4 Framework Architecture for the Age of Intelligence 286
9 Appendix—Additional Reading and Discussion 289
9.1 Emerson's Twelve Principles of Efficiency 289
9.2 Additional System Properties 291
9.3 Oppenheimer's The Open Mind Book 292
9.4 Jevons Paradox 293
9.5 Marcuse's One-Dimensional Man Book 294
9.6 Differences Between System Engineering and Hardware/Software Engineering 295
9.7 The Intricate Tapestry ofWork Norms, Company Cultures, and Engineering
Principles 296
Bibliography 297
Index 307
