Blockchain Tethered AI: Trackable, Traceable Artificial Intelligence and Machine Learning

Description

Remove your doubts about AI and explore how this technology can be future-proofed using blockchain's smart contracts and tamper-evident ledgers. With this practical book, system architects, software engineers, and systems solution specialists will learn how enterprise blockchain provides permanent provenance of AI, removes the mystery, and allows you to validate AI before it's ever used. Authors Karen Kilroy, Lynn Riley, and Deepak Bhatta explain that AI's ability to change itself through program synthesis could take the technology beyond human control. With this book, you'll learn an efficient way to solve this problem by building simple blockchain controls for verifying, tracking, tracing, auditing, and even reversing AI. Blockchain tethered AI interweaves the MLOps process with blockchain so that an MLOps system requires blockchain to function, which in turn tethers AI. This guide shows you how. You will: • Learn how to create and power AI marketplaces with blockchain • Understand why and how to implement on-chain AI governance • Control AI by learning methods to tether it to blockchain networks • Use blockchain crypto anchors to detect common AI hacks • Learn methods for reversing tethered AI Cover Copyright Table of Contents Preface Why Does AI Need to Be Tethered? What You Will Learn Why We Wrote This Book A Note to Future Generations Summary Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgements Chapter 1. Why Build a Blockchain Truth Machine for AI? Dissecting AI’s Trust Deficit Machine Learning Concerns Opaque Box Algorithms Genetic Algorithms Data Quality, Outliers, and Edge Cases Supervised Versus Unsupervised ML Reinforcement Learning and Deep Learning Program Synthesis Superintelligent Agents Technological Singularity Attacks and Failures Model/Data Drift Adversarial Data Attacks Risk and Liability Blockchain as an AI Tether Enterprise Blockchain Distributed, Linked Blocks Trust and Transparency Defining Your Use Case Audit Trail Local Memory Bank Shared Memory Bank Four Controls Case Study: Oracle AIoT and Blockchain What’s Next? Chapter 2. Blockchain Controls for AI Four Blockchain Controls Blockchain Control 1: Pre-establishing Identity and Workflow Criteria for People and Systems Establishing Identity Predetermining Workflow Among Participants Blockchain Control 2: Distributing Tamper-Evident Verification Using Crypto Anchors to Verify Data Sets, Models, and Pipelines Using Blockchain to Detect Common AI Hacks Understanding Federated Learning and Blockchain Understanding Model Marketplaces Blockchain Control 3: Governing, Instructing, and Inhibiting Intelligent Agents Establishing a Governance Group Implementing On-Chain Governance Developing Compliant Intelligent Agents Blockchain Control 4: Showing Authenticity Through User-Viewable Provenance Deciding Whether to Trust AI Summary Chapter 3. User Interfaces Design Thinking Web Interfaces Blockchain Tethered AI User Interfaces BTA User Mockups Functionality Traceability and Transparency Smartphone and Tablet Apps Email and Text Notifications Spreadsheets Third-Party Systems Working with APIs Integrated Hardware Third-Party Services and Tools System Security AI Security Database Security Blockchain Security Additional Security Summary Chapter 4. Planning Your BTA BTA Architecture Sample Model AI Factsheet: Traffic Signs Detection Model How the Model Works Tethering the Model Subscribing Controlling Access Organization Units Staffings Users Analyzing the Use Case Participants Assets Transactions Smart Contracts Audit Trail Summary Chapter 5. Running Your Model Exercise: Oracle Cloud Setup Creating a Cloud Provider Account Creating a Compartment Creating a Bucket Creating a Pre-authenticated Request Creating Oracle Groups Creating IDCS Groups Mapping Oracle Groups Creating a Policy Generating a Secret Key Exercise: Building and Training a Model Exploring the Model Repository Installing Python and PyTorch Starting the Notebook Configuring Boto3 Running Your Notebook Checking the Bucket Optimizing Hyperparameters Learning Rate for Training a Neural Network Number of Training Epochs Used Size of the Training Batches Size of the Hidden Layers Understanding Metrics Accuracy Loss Precision Recall F1 Score Summary Chapter 6. Instantiating Your Blockchain Exercise: Setting Up Hyperledger Fabric Installing Node.js, npm, and NestJS Understanding Hyperledger Fabric 2.0 Required Nodes Installing, Configuring, and Launching the Blockchain Creating and Joining Channels Creating Channels Joining Channels Configuring Anchor Peers Using Chaincodes Understanding Response Struct Using GetTxDateTime Project (project) Model Version (model-version) Model Review (model-review) Model Artifact (model-artifact) Model Experiment (model-experiment) Setting Up the Blockchain Connector Creating Multiple Blockchain Connectors Setting Up the Oracle Connector Configuring Your env File with Your OCI Variables Starting the Oracle Connector More About Integrating Blockchain and the Application Layer Blockchain Connector query OC User Service OC Group Summary Chapter 7. Preparing Your BTA Exercise: Installing and Launching Your BTA Installing the BTA Backend Understanding Your BTA Backend’s env File Understanding Your environment.ts File Launching the BTA Frontend Exercise: Creating Users and Permissions Using MailCatcher Configuring the Super Admin Creating a New Subscription Account in Your BTA Configuring Organization Admin’s Node Configuring Organization Admin’s Channel Verifying the Subscription Activating Your Organization Admin Configuring Access for Your AI Team Summary Chapter 8. Using Your BTA Exercise: Recording Critical AI Touchpoints to Blockchain Adding a New Project Adding a New Version Understanding How Training and Testing Data Use Blockchain Understanding How Models and Algorithms Use Blockchain Understanding How Inputs and Outputs Use Blockchain Understanding How Performance Metrics Use Blockchain Understanding How New Model Versions Use Blockchain Understanding How the Uploads Work Reviewing and Approving the Model Adding AI’s Purpose and Intended Domain Exercise: Auditing Your BTA Tracking Your Model’s Training and Test Data Sets Tracing Your Inputs and Outputs Verifying Performance Metrics Tracing Identity of People and AI Systems Tracking and Tracing Model Development Identifying Tampering Reversing Your Blockchain Tethered Model Checking the Training Data Sets Checking the Algorithms Retraining the Model Summary Index About the Authors Colophon