Practical Artificial Intelligence and Blockchain: A guide to converging blockchain and AI to build smart applications for new economies

Description

Learn how to use AI and blockchain to build decentralized intelligent applications (DIApps) that overcome real-world challenges Key Features Understand the fundamental concepts for converging artificial intelligence and blockchain Apply your learnings to build apps using machine learning with Ethereum, IPFS, and MoiBit Get well-versed with the AI-blockchain ecosystem to develop your own DIAppsBook Description AI and blockchain are two emerging technologies catalyzing the pace of enterprise innovation. With this book, you'll understand both technologies and converge them to solve real-world challenges. This AI blockchain book is divided into three sections. The first section covers the fundamentals of blockchain, AI, and affiliated technologies, where you'll learn to differentiate between the various implementations of blockchains and AI with the help of examples. The second section takes you through domain-specific applications of AI and blockchain. You'll understand the basics of decentralized databases and file systems and connect the dots between AI and blockchain before exploring products and solutions that use them together. You'll then discover applications of AI techniques in crypto trading. In the third section, you'll be introduced to the DIApp design pattern and compare it with the DApp design pattern. The book also highlights unique aspects of SDLC (software development lifecycle) when building a DIApp, shows you how to implement a sample contact tracing application, and delves into the future of AI with blockchain. By the end of this book, you'll have developed the skills you need to converge AI and blockchain technologies to build smart solutions using the DIApp design pattern. What you will learn Get well-versed in blockchain basics and AI methodologies Understand the significance of data collection and cleaning in AI modeling Discover the application of analytics in cryptocurrency trading Get to grips with open, permissioned, and private blockchains Explore the DIApp design pattern and its merit in digital solutions Find out how LSTM and ARIMA can be applied in crypto trading Use the DIApp design pattern to build a sample contact tracing application Get started with building your own DIApps across various domainsWho this book is for This book is for blockchain and AI architects, developers, data scientists, data engineers, and evangelists who want to harness the power of artificial intelligence in blockchain applications. If you are looking for a blend of theoretical and practical use cases to understand how to implement smart cognitive insights into blockchain solutions, this book is what you need! Knowledge of machine learning and blockchain concepts is required. Table of Contents Getting Started with Blockchain Introduction to the AI landscape Domain-Specific Applications of AI and Blockchain AI- and Blockchain-Driven Databases Empowering Blockchain using AI Cryptocurrency and Artificial Intelligence Development lifecycle of a DiApp Implementing DiApps Future of AI with Blockchain Appendix: Moving Forward - Resources for you Cover Title Page Copyright and Credits About Packt Foreword Contributors Table of Contents Preface Section 1: Overview of Blockchain Technology Chapter 1: Getting Started with Blockchain Technical requirements Blockchain versus distributed ledger technology versus distributed databases Comparing the technologies with examples Public versus private versus permissioned blockchains Comparing usage scenarios Privacy in blockchains Understanding Bitcoin A brief overview of Bitcoin Introduction to Ethereum A brief overview of Ethereum Introduction to Hyperledger Overview of the project Hyperledger Fabric Hyperledger Sawtooth Other Hyperledger frameworks and tools Other blockchain platforms – Hashgraph, Corda, and IOTA Consensus algorithms Proof of work Proof of stake Proof of burn Delegated Proof of Stake Proof of authority Practical Byzantine fault tolerance Proof of elapsed time RAFT Ternary augmented RAFT architecture Avalanche Building DApps with blockchain tools Blockchain toolchains and frameworks Developing smart contracts using IDEs and plugins The Remix IDE The EthFiddle IDE The YAKINDU plugin for Eclipse The Solidity plugin for Visual Studio Code The Etheratom plugin for Visual Studio Code Summary Chapter 2: Introduction to the AI Landscape Technical requirements AI – key concepts History of AI AI winter Types of AI Weak AI  Strong AI Super AI Forms of AI and approaches Statistical and expert systems Machine learning Supervised learning Unsupervised learning Reinforcement learning Neural networks Evolutionary computation Swarm computation AI in digital transformation Data extraction Data transformation Processing Storyboarding Data utilization Failure scenarios Business requirements Adaptability Skill gaps Process overhaul AI platforms and tools TensorFlow Microsoft Cognitive Toolkit IBM Watson Summary Section 2: Blockchain and Artificial Intelligence Chapter 3: Domain-Specific Applications of AI and Blockchain Technical requirements Applying AI and blockchain to healthcare Issues in the domain Emerging solutions in healthcare Retrospective Applying AI and blockchain to supply chains Issues in the domain Emerging solutions in the supply chain industry Retrospective Applying AI and blockchain to financial services Issues in the domain Emerging solutions in BFSI Retrospective Applying AI and blockchain to other domains Applying AI and blockchain to knowledge management Issues in the domain Emerging solutions in knowledge management Retrospective Applying AI and blockchain to real estate Issues in the domain Emerging solutions in real estate Retrospective Applying AI and blockchain to media Issues in the domain Emerging solutions in media Retrospective Applying AI and blockchain to identity management Issues in the domain Emerging solutions in identity management Retrospective Applying AI and blockchain to royalty management Issues in the domain Emerging solutions in royalty management Retrospective Applying AI and blockchain to information security Issues in the domain Emerging solutions in information security Retrospective Applying AI and blockchain to document management Issues in the domain Emerging solutions in document management Retrospective Summary Chapter 4: AI- and Blockchain-Driven Databases Technical requirements Centralized versus distributed data Motivations for using decentralized databases Contrast and analysis Blockchain data – big data for AI analysis Building better AI models using decentralized databases Immutability of data – increasing trust in AI training and testing Better control of data and model exchange Blockchain analytics Global databases IPFS MóiBit Solid Ocean Protocol Storj Swarm Data management in a DAO Aragon Bisq Emerging patterns for database solutions Enterprise Technical impediments Summary of the emerging pattern Financial services Technical impediments Summary of the emerging pattern Supply chain management Technical impediments Summary of the emerging pattern Healthcare Technical impediments Summary of the emerging pattern Summary Chapter 5: Empowering Blockchain Using AI The benefits of combining blockchain and AI About Aicumen Technologies Combining blockchain and AI in pandemic management Current issues in digital contact tracing Combining blockchain and AI in social finance Current issues in financing Combining blockchain and AI to humanize digital interactions Current issues with digital interactions The democratization of AI with decentralization Case study – the TEXA project Functions of TEXA Summary Chapter 6: Cryptocurrency and Artificial Intelligence Technical requirements The role of AI in cryptocurrency Cryptocurrency trading Issues and special considerations Benefits of AI in crypto trading Making price predictions with AI Issues with price prediction Benefits of AI in prediction Introduction to time series Time-series forecasting with ARIMA Applications of algorithmic or quant trading in cryptocurrency Arbitrage Market making Issues and special considerations Benefits of AI in trading data The future of cryptocurrencies in India Summary Section 3: Developing Blockchain Products Chapter 7: Development Life Cycle of a DIApp Technical requirements Applying SDLC practices in blockchains Ideation to productization Introduction to DIApps Comparing DIApps and DApps Challenges in the enterprise Solution architecture of a DApp Solution architecture of a DIApp  Key differences Designing a DIApp  Research Conceptualization Product-market fit Developing a DIApp Team formation Agile development Testing a DIApp Authoring the test cases Unit testing Integration testing Testing AI models Deploying a DIApp  Scaling the application for production Monitoring a DIApp  Explorers Summary Chapter 8: Implementing DIApps Technical requirements Evolution of decentralized applications Traditional web applications Decentralized applications Decentralized intelligent applications Contrast and analysis Building a sample DIApp Problem statement Current challenges Contact tracing Issues with contact tracing Solution approach Choosing the blockchain technology Choosing a decentralized database Choosing an AI technique The technical architecture of the sample DIApp Developing the smart contract Developing the client code for sensors Training the model Developing the backend Developing the frontend Testing the sample DIApp Deploying the sample DIApp Signing up for the Google Maps API Signing up for MóiBit Signing up for Infura Updating your local justfile Deploying smart contracts Deploying client code into sensors Deploying the backend API Deploying the web dashboard Retrospecting the sample DIApp Merits of the sample DIApp Limitations of the sample DIApp Future enhancements Summary Chapter 9: The Future of AI with Blockchain Technical requirements The convergence of AI and blockchain The future of converging AI and blockchain Converging AI and blockchain in enterprise Customer service As-is scenario To-be scenario Possible solution Performance management As-is scenario To-be scenario Possible solution Data security As-is scenario To-be scenario Possible solution Finance management As-is scenario To-be scenario Possible solution Converging AI and blockchain in government Taxation As-is scenario To-be scenario Possible solution Voting As-is scenario To-be scenario Possible solution Legislative reforms As-is scenario To-be scenario Possible solution Census As-is scenario To-be scenario Possible solution Converging AI and blockchain in financial services Insurance As-is scenario To-be scenario Possible solution Converging AI and blockchain in human resources Background checks As-is scenario To-be scenario Possible solution Converging AI and blockchain in healthcare Pharmacovigilance As-is scenario To-be scenario Possible solution Converging AI and blockchain in supply chain management Volatility As-is scenario To-be scenario Possible solution Converging AI and blockchain in other domains Law and order As-is scenario To-be scenario National security As-is scenario To-be scenario Environment conservation As-is scenario To-be scenario Agriculture As-is scenario To-be scenario Summary Appendix: Moving Forward - Resources for you Blockchain resources Awesome Blockchain News Communities Quintessential blogs Design Development and how-tos AI resources All-in-one list for beginning with AI Case studies Communities Quintessential blogs Research Development and how-tos Other Books You May Enjoy Index