This book provides a systematic exploration of the fundamental theories, technological integration, and practical applications of two frontier technologies: artificial intelligence and blockchain. Grounded in rigorous theoretical analysis and enriched with numerous real-world case studies, it comprehensively demonstrates the innovative potential of AI empowered by blockchain, covering key technologies such as data privacy protection, distributed model training, and smart contract optimization.

The text delves into the practical applications of these integrated technologies across diverse fields including healthcare, fintech, the Internet of Things (IoT), and data trading platforms. Furthermore, it offers insights into future technological directions such as quantum computing, large models, and decentralized autonomous organizations.

Chapter 1 introduces the background and overview. Chapters 2 and 3 systematically present the fundamentals of AI and blockchain technologies, covering knowledge representation, machine learning, deep learning, consensus mechanisms, smart contracts, and cryptocurrencies. Chapters 4 to 6 focus on the modes of integration and specific applications combining AI and blockchain, including smart contract optimization, data privacy protection, and Decentralized Autonomous Organizations (DAOs), with particular emphasis on how these technologies mutually enhance each other and jointly foster cross-industry applications. Chapters 7 to 11 introduce practical application scenarios for AI and blockchain technologies in areas such as anti-fraud, Web 3.0, healthcare, IoT, and data trading, analyzing the associated challenges and opportunities. Chapter 12 concentrates on the deep integration of large model technology with blockchain, discussing how to leverage the potential of these two technologies in complex application scenarios and envisioning future technological developments. Chapters 13 and 14 summarize the book's content, discuss current open research questions, and outline future trends.

Structured with clear progression and featuring high-quality diagrams, this book balances theoretical depth with practical guidance. It serves as an advanced learning resource for AI and blockchain technologies, suitable for upper-level undergraduate or graduate students in computer science and AI-related disciplines. It is also an essential reference for academic researchers and engineering professionals in related fields.

LV Hairong is an Associate Researcher in the Department of Automation at Tsinghua University, serving as the Associate Director of the Information Processing Institute and the Assistant Director of the Fundamental Model Laboratory for Life Sciences. He has been recognized as an expert under the Fujian Province "100 Talents Program".

YANG Yang is a Senior Research Scientist at Singapore Management University and a Senior Member of IEEE. He has been consecutively ranked in the global top 2% of scientists for five years.

ZHENG Xianghan is a Researcher and Doctoral Supervisor at Fuzhou University, and serves as a Council Member of the IEEE International Conference on Cloud Computing.

Chapter 1: Introduction

1.1 Background and Motivation

1.1.1 The Combined Potential of AI and Blockchain

1.1.2 Current Challenges and Opportunities

1.2 Book Structure and Main Content

Chapter 2: Artificial Intelligence Technologies

2.1 Knowledge Representation

2.1.1 Knowledge Representation and AI

2.1.2 Knowledge Representation Methods

2.1.3 Knowledge Representation Learning

2.1.4 Applications of Knowledge Representation

2.2 Machine Learning

2.2.1 History of Machine Learning

2.2.2 Supervised Machine Learning

2.2.3 Unsupervised Machine Learning

2.2.4 Machine Learning Applications

2.3 Deep Learning

2.3.1 History of Deep Learning

2.3.2 Deep Neural Networks

2.3.3 Deep Learning Applications

2.3.4 Future Prospects of Deep Learning

2.4 Reinforcement Learning

2.4.1 Reinforcement Learning Fundamentals

2.4.2 Deep Reinforcement Learning

2.4.3 Reinforcement Learning from Human Feedback

2.4.4 Reinforcement Learning Applications

2.5 Knowledge Graphs

2.5.1 Knowledge Graph Fundamentals

2.5.2 Knowledge Graph Construction Methods

2.5.3 Knowledge Graph Applications

2.5.4 Integration of Knowledge Graphs and AI

2.5.5 Future Development of Knowledge Graphs

2.6 AI Ethics and Safety

2.6.1 Ethical Risks in AI

2.6.2 Data Security and Data Privacy

2.6.3 Intellectual Property Issues from AI

2.6.4 Laws and Regulations for AI Ethics

2.7 Chapter Summary

2.8 Further Reading

2.9 Chapter Exercises

Chapter 3: Blockchain Technology

3.1 Blockchain Fundamentals

3.1.1 The Concept of Blockchain

3.1.2 Components of Blockchain

3.1.3 How Blockchain Works

3.1.4 Public vs. Private Blockchains

3.2 History and Development of Blockchain

3.2.1 The Origin of Blockchain

3.2.2 Evolution and Blockchain 2.0

3.2.3 Innovation and Blockchain 3.0

3.3 Blockchain Consensus Mechanisms

3.3.1 The Concept of Consensus

3.3.2 Proof of Work

3.3.3 Proof of Stake

3.3.4 Emerging Consensus Mechanisms

3.4 Smart Contracts in Blockchain

3.4.1 The Concept of Smart Contracts

3.4.2 Principles and Characteristics of Smart Contracts

3.4.3 Smart Contract Programming

3.4.4 Smart Contract Deployment and Invocation Examples

3.5 Digital Currency and Cryptocurrency in Blockchain

3.5.1 Concepts of Digital Currency and Cryptocurrency

3.5.2 Mainstream Cryptocurrencies

3.5.3 Blockchain Applications in Finance and Currency

3.5.4 Cryptocurrency Regulation and Risks

3.6 Privacy Protection and Anonymity in Blockchain

3.6.1 Common Blockchain Attacks and Security Threats

3.6.2 Identity Authentication and Access Control

3.6.3 Anonymity and Privacy Protection

3.7 Chapter Summary

3.8 Further Reading

3.9 Chapter Exercises

Chapter 4: Integration of AI and Blockchain Technologies

4.1 Importance of AI-Blockchain Integration

4.1.1 Limitations of Blockchain and Smart Contracts

4.1.2 Opportunities Presented by Intelligent Blockchain

4.1.3 The Necessity of Combining Blockchain and AI

4.2 Blockchain Applications in AI

4.2.1 Using Blockchain for Distributed Machine Learning

4.2.2 Data Security and Privacy Protection in Blockchain-based Distributed Learning

4.3 AI Applications in Blockchain

4.3.1 Using AI for Intelligent Blockchain Data Analysis

4.3.2 Using AI for Blockchain Security Analysis

4.3.3 Using AI for Smart Contract Optimization

4.4 Chapter Summary

4.5 Further Reading

4.5.1 Future Directions for AI-Blockchain Integration

4.5.2 Emerging Technical Challenges in Interdisciplinary Integration

4.6 Chapter Exercises

Chapter 5: Blockchain-Empowered AI Technologies

5.1 Blockchain-based Deep Learning and Reinforcement Learning

5.1.1 Blockchain-Enhanced Deep Learning

5.1.2 Blockchain-Empowered Reinforcement Learning

5.1.3 Blockchain-based Deep Reinforcement Learning

5.2 Blockchain-based Decentralized AI Technologies

5.2.1 Overview of Decentralized AI

5.2.2 Decentralized Intelligent Decision-Making

5.2.3 Decentralized Federated Learning

5.3 Blockchain-based Privacy-Preserving AI Technologies

5.3.1 Overview of Privacy-Preserving AI

5.3.2 Blockchain-based Privacy-Preserving Machine Learning

5.3.3 Blockchain-based Privacy-Preserving Smart Contracts

5.4 Smart Contract-based AI Technologies

5.4.1 Smart Contract-based Model Training

5.4.2 Sharing and Trading ML Models via Smart Contracts

5.4.3 Security and Trustworthiness of Smart Contracts and AI Decisions

5.5 Chapter Summary

5.6 Further Reading

5.7 Chapter Exercises

Chapter 6: AI-Driven Blockchain Technologies

6.1 AI-Driven Distributed Ledger Mining Technologies

6.1.1 Real-time Dynamic Analysis

6.1.2 Ledger Sentiment Perception

6.1.3 Audit Tracing

6.1.4 Tokenization and Digital Asset Management

6.1.5 Network Scalability and Performance

6.2 AI-Driven Decentralized Autonomous Organizations

6.2.1 Edge AI for DAO Governance and Automated Decision-Making

6.2.2 Centralized AI-Driven Smart Contract Interaction

6.2.3 Adaptive AI-Linked DAO Shared Intelligence

6.3 AI-Driven Blockchain Data Mining

6.3.1 On-Chain Data Mining

6.3.2 On-Chain and Off-Chain Integrated Data Analysis

6.3.3 Cross-Chain Integrated Data Analysis

6.4 AI-Driven Smart Contract Technologies

6.4.1 NLP-based Contract Generation

6.4.2 Smart Contract Vulnerability Detection and Repair

6.4.3 Self-Learning Methods for Smart Contract Optimization

6.4.4 AI Oracle-Driven Smart Contracts

6.5 Chapter Summary

6.6 Further Reading

6.7 Chapter Exercises

Chapter 7: AI Applications in Blockchain Anti-Fraud

7.1 Analysis of Ponzi Schemes in Blockchain

7.1.1 Problem Overview: Ponzi Schemes

7.1.2 AI Modeling

7.1.3 Case Analysis

7.1.4 Development Trends

7.2 Analysis of Phishing Scams in Blockchain

7.2.1 Problem Overview: Phishing Scams

7.2.2 AI Modeling

7.2.3 Case Analysis

7.2.4 Development Trends

7.3 Analysis of Honeypot Scams in Blockchain

7.3.1 Problem Overview: Honeypot Scams

7.3.2 AI Modeling

7.3.3 Case Analysis

7.3.4 Development Trends

7.4 Analysis of ICO Scams in Blockchain

7.4.1 Problem Overview: ICO Scams

7.4.2 AI Modeling

7.4.3 Case Analysis

7.4.4 Development Trends

7.5 Chapter Summary

7.6 Further Reading

7.7 Chapter Exercises

Chapter 8: Web 3.0 Integration and Applications with Blockchain and AI

8.1 Web 3.0 Technology

8.1.1 The Concept of Web 3.0

8.1.2 Core Technologies of Web 3.0

8.1.3 Decentralized Applications

8.2 Web 3.0 and Blockchain Integration

8.2.1 Blockchain Technology Applications in Web 3.0

8.2.2 Decentralized Finance and the Crypto Economy

8.2.3 Decentralized Autonomous Organizations and Governance

8.2.4 Non-Fungible Tokens

8.3 Web 3.0 and AI Integration

8.3.1 Opportunities in Web 3.0 and AI Integration

8.3.2 Decentralized AI Computing Protocols

8.3.3 Web 3.0 Empowered Generative AI

8.3.4 Challenges in Web 3.0 and AI Integration

8.4 Chapter Summary

8.5 Further Reading

8.6 Chapter Exercises

Chapter 9: Synergistic Applications of AI and Blockchain in Healthcare

9.1 Evolution of Healthcare Data Management

9.1.1 Informatization Management in the Healthcare Industry

9.1.2 The Cornerstone of Smart Healthcare: Healthcare Big Data

9.1.3 Status and Challenges in Healthcare Big Data Management and Analysis

9.1.4 Utilizing AI and Blockchain for Healthcare Big Data Management and Analysis

9.2 AI and Blockchain-Driven Data Analysis and Information Flow: A Cardiovascular Medicine Data Example

9.2.1 AI and Blockchain Ensuring Data Quality, Consistency, and Privacy Security

9.2.2 AI Technology Applications in Cardiovascular Medicine Data Analysis

9.2.3 Blockchain Technology Applications in Cardiovascular Medicine Data Privacy and Security

9.2.4 The Synergistic Role of AI and Blockchain in Cardiovascular Medicine Data Analysis and Information Flow

9.3 Healthcare Data Sharing Platforms

9.3.1 Status and Challenges of Healthcare Data Sharing

9.3.2 Data Privacy and Security Issues in Healthcare Data Sharing

9.3.3 Architectural Design of Intelligent Healthcare Data Sharing Platforms

9.3.4 Business Model Analysis of Intelligent Healthcare Data Sharing Platforms

9.3.5 Intelligent Design of Healthcare Data Sharing Platforms

9.3.6 AI Privacy Security Computing in Intelligent Healthcare Data Sharing Platforms

9.3.7 Blockchain Technology in Intelligent Healthcare Data Sharing Platforms

9.3.8 Incentive Mechanisms in Intelligent Healthcare Data Sharing Platforms

9.4 AI and Blockchain Technologies Advancing Personalized Medicine

9.4.1 Using AI, Blockchain, and Wearable Technology for Chronic Disease Management

9.4.2 Synergistic Application of AI and Blockchain in Disease Self-Detection

9.5 Multi-Center Medical Research Platforms Based on Federated Learning and Blockchain

9.5.1 Overview of Federated Learning and Distributed Machine Learning

9.5.2 Broad Applications of Federated Learning and Blockchain in Healthcare

9.5.3 Privacy Architecture Design Based on Federated Learning and Blockchain in Multi-Center Research Platforms

9.5.4 Distributed Data Access Design for Multi-Center Research Platforms

9.5.5 Distributed Machine Learning Model Architecture for Multi-Center Research Platforms

9.5.6 Blockchain-based Federated Learning Algorithm Design for Multi-Center Research Platforms

9.6 Chapter Summary

9.7 Further Reading

9.8 Chapter Exercises

Chapter 10: AI and Blockchain Applications in the Internet of Things

10.1 IoT Technology and Applications

10.1.1 IoT Basic Concepts

10.1.2 IoT Core Technologies

10.1.3 IoT Application Examples

10.1.4 IoT Industry Development Trends

10.2 IoT + AI

10.2.1 Edge Computing

10.2.2 Intelligent Control Technology

10.2.3 Predictive Maintenance

10.2.4 Smart IoT Applications in Different Scenarios

10.3 IoT + Blockchain

10.3.1 Core Technologies in Blockchain IoT

10.3.2 Blockchain Solutions for IoT Security

10.3.3 Summary and Outlook

10.4 City Brain Applications Integrating IoT, AI, and Blockchain

10.4.1 Overall Architecture of City Brain

10.4.2 Development Status of City Brain

10.4.3 Typical Application Scenarios of City Brain

10.4.4 Problems and Challenges

10.5 Chapter Summary

10.6 Further Reading

10.7 Chapter Exercises

Chapter 11: AI and Blockchain Applications in Data Trading Platforms

11.1 Evolution and Status of Data Trading Platforms

11.1.1 The Rise of Data Trading Platforms

11.1.2 Challenges Facing Data Trading Platforms

11.2 Core Concepts and Key Technologies of Data Trading Platforms

11.2.1 Data Classification and Grading

11.2.2 Data Resource Sharing and Opening

11.2.3 Data Asset Pricing Strategies and Methods

11.2.4 Design Ideas for Data Trading Platforms

11.3 Synergistic Application of AI and Blockchain in Data Trading Platforms

11.3.1 Technical Synergy Mechanism of AI and Blockchain

11.3.2 Application Scenarios of AI and Blockchain-based Data Trading Platforms

11.3.3 Future Data Trading Models Based on AI and Blockchain

11.3.4 AI and Blockchain-based Data Pricing and Trading Strategies

11.4 Security and Privacy Assurance in Data Trading Platforms

11.4.1 AI and Blockchain Data Architecture Based on Privacy Protection

11.4.2 Smart Contracts and Automated Security Mechanisms

11.4.3 Building a Decentralized Data Trading Ecosystem

11.4.4 AI-Driven Data Contracts and Value Sharing Models

11.5 Hands-on Data Trading Platform Architecture Design

11.5.1 Design Ideas

11.5.2 Model Transformation

11.5.3 Functional Architecture

11.5.4 Business Architecture

11.5.5 Technical Architecture

11.5.6 Data Architecture

11.5.7 Security Architecture

11.5.8 AI and Blockchain Implementation Practice

11.6 Case Studies and Future Outlook

11.6.1 Guiyang Big Data Exchange

11.6.2 Future Outlook

11.7 Chapter Summary

11.8 Further Reading

11.9 Chapter Exercises

Chapter 12: Large Models and Blockchain

12.1 Large AI Model Fundamentals

12.1.1 Development History of Large Models

12.1.2 Basic Principles of Large Language Models

12.1.3 Multimodal Large Models

12.1.4 Large Model Training and Datasets

12.1.5 Large Model Applications

12.2 Large Model-Driven Blockchain Technologies

12.2.1 Large Model-Driven Blockchain Technology Architecture

12.2.2 Large Model-Driven Automatic Smart Contract Generation

12.2.3 Case Analysis of Large Model-Driven Smart Contracts

12.2.4 Large Model-Based Blockchain Transaction Anomaly Detection

12.2.5 Case Analysis of Large Model-Based Transaction Anomaly Detection

12.2.6 Large Model-Based Blockchain Ethical Risk Identification

12.3 Blockchain-Empowered Large Model Technologies

12.3.1 Large Model Training and Deployment

12.3.2 Blockchain-based Cloud Computing in Large Models

12.3.3 Dynamic Large Models

12.3.4 Training and Use of Dynamic Large Models on Blockchain

12.4 Advantages and Challenges of Large Model and Blockchain Integration

12.4.1 Large Models, Blockchain, and Data Privacy Security

12.4.2 Advantages and Challenges of Decentralized Large Models on Blockchain

12.4.3 Process Transparency for Large Models and Blockchain

12.4.4 Future Development Directions for Large Models and Blockchain

12.5 Chapter Summary

12.6 Further Reading

12.7 Chapter Exercises

Chapter 13: Open Research Areas in AI and Blockchain

13.1 Blockchain-based Federated Learning

13.1.1 Blockchain-based Distributed Federated Learning

13.1.2 Blockchain-based Privacy Security Mechanisms for Federated Learning

13.1.3 Applications of Blockchain-based Federated Learning in Different Fields

13.2 Blockchain-based Swarm Intelligence

13.2.1 Blockchain-based Swarm Intelligence Networks

13.2.2 Blockchain-based Crowdsensing

13.2.3 Blockchain-based Swarm Intelligence Contracts

13.3 Blockchain Technology Resistant to Malicious Machine Learning

13.3.1 Blockchain Systems Resistant to ML Malicious Nodes

13.3.2 Applications of Blockchain-based Anti-Machine Learning in Industrial Control

13.4 AI-based Blockchain Informatization, Modeling, and Automation

13.4.1 AI-based Informatization of Blockchain

13.4.2 AI-based Modeling of Blockchain

13.4.3 AI-based Automation of Blockchain

13.5 AI and Blockchain-based Intelligent Management

13.5.1 AI and Blockchain-based Smart Manufacturing

13.5.2 AI and Blockchain-based Asset Management

13.5.3 AI and Blockchain-based Enterprise Management

13.6 Further Reading

13.7 Chapter Exercises

Chapter 14: Summary and Outlook

14.1 Full Book Summary

14.2 Future Trends in AI and Blockchain

14.2.1 Proliferation of Decentralization and Distributed Intelligence

14.2.2 AI-Driven Autonomous Economies

14.2.3 Fully Automated Smart Contracts and Trustless Environment Applications

14.2.4 Decentralized Training and Deployment of Large-Scale AI Models

14.2.5 AI-Driven Decentralized Finance and Dynamic Financial Systems

14.2.6 Personal Data Sovereignty and Value Distribution on Blockchain

14.2.7 Cross-Chain Interoperability and Global Data Flow

14.2.8 Social Impact and Ethical Challenges of AI and Blockchain

References