Artificial Intelligence Across Industries — Volume II: How AI Is Transforming Engineering, Manufacturing, and the Physical World

Artificial Intelligence Across Industries — Volume II: How AI Is Transforming Engineering, Manufacturing, and the Physical World

Author: Eric Halden

Description: For decades, artificial intelligence delivered its most visible wins on screens—product recommendations, fraud alerts, chatbots. The harder frontier is code meeting concrete, steel, and deep-sea pressure: a factory robot can assemble a phone in seconds but still fumbles a randomly scattered screwdriver; an autonomous truck handles a clear highway but hesitates at a rainy construction zone. That gap between digital intelligence and physical reality is the real problem of industrial AI. Artificial Intelligence Across Industries — Volume II: How AI Is Transforming Engineering, Manufacturing, and the Physical World takes you deep into that gap, showing how AI moves beyond dashboards to control robots, optimize factories, design the chips that power AI, drive vehicles, and monitor bridges, grids, and farms. It begins with the foundations of physical AI, then moves through robotics, semiconductor design, transportation, construction, smart cities, energy, mining, agriculture, ocean engineering, defense, and convergence of these technologies. It is not an algorithm catalog; it is an engineer's guide to cyber-physical systems. The book is structured for both breadth and depth, spanning twelve parts and twenty-five chapters, with three strands that run throughout: • From sensor to action: perception, prediction, planning, and control inside physical AI systems. • The connected factory: machine vision, predictive maintenance, digital twins, and edge AI in production. • Moving through land, air, water, and underground: autonomous vehicles, drones, ships, mining, and defense systems. Halden grounds every topic in the physics of the environment—latency in milliseconds, dust, vibration, GPS-denied areas, and safety-critical decisions. He explains why a model that works in a clean data center may fail in a harbor, a mine, or a congested city, and where human expertise remains irreplaceable. Each chapter follows a consistent pattern: the physical problem, the traditional engineering limit, the AI intervention, and the operational trade-offs or failure modes that still exist. This is for engineering professionals, technical managers, industrial strategists, and advanced STEM students who need a no-hype view of AI in the physical world. If you design, operate, or invest in physical systems, you'll get a practical framework to separate production-ready automation from lab-only promises. Consider autonomous driving: the book treats it not as a demo but as an entire stack—sensor fusion, behavior prediction, motion planning, safety validation—so you can evaluate claims like an engineer. With its 12-part, 25-chapter structure and roughly 140,000 words, Volume II offers a thorough, realistic tour of physical AI—from the factory floor to the ocean floor. By the end, you won't just see what AI can accomplish; you'll understand why its real-world success depends on bending to physics, safety, and messiness. That is the kind of understanding that turns curiosity into engineering judgment—and that judgment is exactly what this book is designed to build.

AI summary: This book provides a comprehensive overview of how artificial intelligence is transforming engineering, manufacturing, and physical industries. It covers topics such as smart factories, digital twins, robotics, autonomous vehicles, and AI in infrastructure, focusing on the practical constraints of physics, safety, and real-world applications. The book is aimed at engineering professionals and technical managers who need a realistic, no-hype understanding of AI in physical systems.

Best for

Engineering professionals, technical managers, industrial strategists, and advanced STEM students.

Reader persona

A manufacturing engineer or technical manager seeking a practical understanding of how AI applies to physical systems like factories, robotics, and infrastructure to make informed decisions.

Search intent

To understand how AI is applied in engineering and physical industries, gaining practical knowledge to evaluate and implement AI solutions.

Unique angle

Unlike AI books focused on software or data science, this volume specifically addresses the challenges of deploying AI in physical systems, grounding each topic in the constraints of real-world engineering.

Content type

non-fiction engineering guide

Quick summary

• Artificial Intelligence Across Industries — Volume II explores how AI is applied to physical systems including manufacturing, robotics, transportation, and infrastructure.
• The book provides an engineer's perspective on AI, covering perception, prediction, and control in industrial applications.
• It offers a practical framework for evaluating AI readiness in engineering projects, highlighting both opportunities and failure modes.
• Designed for engineering professionals, the book emphasizes real-world constraints like latency, safety, and data quality over algorithmic theory.
• It includes detailed case studies on smart factories, autonomous vehicles, and digital twins to illustrate AI adoption strategies.

Key topics: Physical AI, Smart Manufacturing, Industrial Robotics, Digital Twins, Autonomous Vehicles, AI in Infrastructure, Cyber-Physical Systems, Predictive Maintenance, Edge AI, AI in Energy and Mining

Entities: artificial intelligence, machine learning, cyber-physical systems, industrial IoT, computer vision, digital twins, autonomous vehicles, robotics, smart factories, predictive maintenance, edge computing, 5G/6G networks

Needs addressed

• Understanding how AI can be deployed in safety-critical physical environments
• Evaluating the practical limitations of AI in manufacturing and engineering
• Implementing digital twins and predictive maintenance for operational efficiency
• Navigating the integration of AI with existing industrial systems
• Assessing the readiness of AI technologies for industrial adoption

Read if

• Engineering professionals in manufacturing, robotics, and automotive sectors
• Technical managers responsible for industrial digital transformation
• Industrial strategists evaluating AI investment opportunities
• Advanced STEM students specializing in AI, robotics, or systems engineering
• Consultants and analysts covering industrial AI markets

May not fit if

• Complete beginners unfamiliar with basic engineering concepts
• Readers looking for detailed code examples or algorithm implementations
• Those seeking an academic, theoretical treatment of AI rather than an applied engineering perspective

Table of contents

1. Introduction (introduction)
2. The Rise of Industrial Intelligence (part)
3. From Automation to Intelligent Industry (chapter)
4. How Industrial Automation Changed Engineering (section)
5. Why Rule-Based Systems Are No Longer Enough (section)
6. The Explosion of Sensor and Machine Data (section)
7. From Automation to Prediction and Adaptation (section)
8. Edge AI, Cloud AI, and Industrial Computing (section)
9. The Rise of Physical AI (section)
10. Toward AI-Native Industrial Systems (section)
11. How AI Enters the Physical World (chapter)
12. From Sensors to Decisions (section)
13. Perception, Prediction, Planning, and Control (section)
14. Real-Time Constraints (section)
15. Human Expertise in Industrial AI (section)
16. Safety-Critical Decision Making (section)
17. Digital Twins and Simulation (section)
18. From Assistance to Autonomy (section)
19. Manufacturing and the Intelligent Factory (part)
20. Building the AI-Powered Factory (chapter)
21. The Factory Before AI (section)
22. Why Modern Manufacturing Produces Too Much Data for Humans Alone (section)
23. Seeing Defects with Machine Vision (section)
24. Predicting Equipment Failure (section)
25. Optimizing Production Lines (section)
26. AI for Process Control (section)
27. From Smart Factories to Adaptive Factories (section)
28. The Road Toward Autonomous Manufacturing (section)
29. Digital Twins and Industrial Simulation (chapter)
30. Why Engineers Build Virtual Copies of Physical Systems (section)
31. From CAD Models to Living Digital Twins (section)
32. Connecting Sensors to Simulation (section)
33. AI for Faster Engineering Simulation (section)
34. Predicting Failures Before They Happen (section)
35. Optimizing Machines and Production Systems (section)
36. Digital Twins Across the Product Lifecycle (section)
37. When the Virtual Factory Starts Making Decisions (section)
38. Industrial IoT, Edge AI, and Connected Operations (chapter)
39. How Factories Became Sensor Networks (section)
40. The Industrial Internet of Things (section)
41. Why Industrial AI Cannot Always Depend on the Cloud (section)
42. Edge AI on Machines and Production Lines (section)
43. SCADA, PLCs, and AI (section)
44. Predictive Monitoring at Scale (section)
45. Securing Connected Industrial Systems (section)
46. Toward the Self-Monitoring Factory (section)
47. Robotics and Physical AI (part)
48. Teaching Robots to Work (chapter)
49. Why Traditional Industrial Robots Were Powerful but Limited (section)
50. Giving Robots Vision (section)
51. Teaching Machines to Grasp and Manipulate Objects (section)
52. Learning from Demonstration (section)
53. Human–Robot Collaboration (section)
54. Warehouse and Logistics Robots (section)
55. Foundation Models for Robotics (section)
56. From Specialized Robots to General-Purpose Machines (section)
57. The Rise of Humanoid and General-Purpose Robots (chapter)
58. Why Build Robots in Human Form? (section)
59. Learning Locomotion and Balance (section)
60. Seeing and Understanding Human Environments (section)
61. Dexterous Manipulation (section)
62. Vision-Language-Action Models (section)
63. Training Robots in Simulation (section)
64. Humanoids in Factories and Warehouses (section)
65. How Close Are We to General-Purpose Robots? (section)
66. Semiconductor and Electronic Engineering (part)
67. Using AI to Design the Chips That Power AI (chapter)
68. Why Modern Chip Design Is Becoming Too Complex (section)
69. The EDA Revolution (section)
70. AI for Logic Design and Verification (section)
71. Floorplanning and Placement (section)
72. Power, Performance, and Area Optimization (section)
73. AI in Semiconductor Manufacturing (section)
74. Defect Detection and Yield Improvement (section)
75. The Feedback Loop Between AI and Chip Design (section)
76. Intelligent Transportation (part)
77. Teaching Vehicles to Drive Themselves (chapter)
78. Why Driving Is One of AI's Hardest Engineering Problems (section)
79. Seeing the Road (section)
80. Sensor Fusion (section)

Frequently asked questions