Artificial Intelligence Fundamentals (ASQ's Pocket Guide)

Name: Artificial Intelligence Fundamentals (ASQ's Pocket Guide)
Brand: ASQ Quality Press
Price: 48.99 EUR
Availability: OnlineOnly

Jayet Moon(Autor*in)

ASQ Quality Press

1. Auflage

Erschienen am 15. April 2025

188 Seiten

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978-1-63694-206-3 (ISBN)

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Inhalt

Intro
Title page
Copyright
Contents
List of Figures and Tables
Acknowledgments
Acronyms
Introduction
Chapter 1_Introduction to AI and ML in Quality Management
THE NEXT STEP IN THE EVOLUTION OF QUALITY
UNDERSTANDING AI AND ML BASICS
WHY AI/ML MATTERS FOR QUALITY PROFESSIONALS
REAL-WORLD EXAMPLES OF AI/ML INQUALITY MANAGEMENT
SELF-DRIVING CARS: HOW TESLA FULLSELF DRIVING (FSD) USES AI
Chapter 2_The Foundations of AI and ML for Quality Professionals
HISTORY
TYPES OF AI
TYPES OF MACHINE LEARNING
NATURAL LANGUAGE PROCESSING AND LARGE LANGUAGE MODELS
BASIC COMPONENTS OF AI/ML SYSTEMS
THE MODEL
EXAMPLE
THE ROLE OF DATA IN AI AND ML
Chapter 3_Tools and Use Cases for AI/ML in Quality
POPULAR TOOLS
APPLYING AI/ML IN QUALITY MANAGEMENT
CASE STUDY: COMPUTER VISION FOR VISUAL INSPECTIONS
Chapter 4_How to Create Your Own ChatGPT Without Coding
CUSTOMIZING "MY GPT" ON CHATGPT.COM
STEPS TO CREATE YOUR "AGENT" USING COPILOT STUDIO:
USING AZURE AI TO CREATE AND DEPLOY A MODEL
KEY CONSIDERATIONS FOR CUSTOMIZING CHATGPT
Chapter 5_Mastering Prompt Engineering
UNDERSTANDING THE IMPORTANCE OF PROMPTS
PRINCIPLES OF EFFECTIVE PROMPT ENGINEERING
PROMPT TECHNIQUES AND FRAMEWORKS
COMMON CHALLENGES WITH LLM RESPONSES
Chapter 6_Integrating AI/ML Solutions into Quality Workflows
BASIC AI QUALITY INTEGRATION
ADVANCED AI QUALITY INTEGRATION
BENEFITS OF AI FOR QUALITY PROFESSIONALS
KEY STEPS FOR INTEGRATION
OVERCOMING CHALLENGES
Chapter 7_Measuring the ROI of AI/ML in Quality Management
STEPS TO MEASURE ROI
Chapter 8_Leadership's Role in Fostering a Culture of AI Innovation
THE ROLE OF LEADERSHIP IN DRIVING AI/ML INNOVATION
BUILDING AI LITERACY
CREATING AN ENVIRONMENT OF EXPERIMENTATION
BEST PRACTICES FOR SUSTAINING AI-DRIVEN INNOVATION
Chapter 9_Data as the Cornerstone of Quality
COLLECTING QUALITY DATA: BEST PRACTICES
DATA CLEANING AND PREPROCESSING FOR AI/ML
ENSURING DATA INTEGRITY AND RELIABILITY
CASE STUDY: DATA PREPARATION FOR PREDICTIVE MAINTENANCE
Chapter 10_Predictive Quality Analytics
INTRODUCTION TO PREDICTIVE ANALYTICS
KEY COMPONENTS OF PREDICTIVE ANALYTICS FOR QUALITY
USE CASES FOR PREDICTIVE ANALYTICS IN QUALITY
CASE STUDIES: USING AI FOR PROACTIVE QUALITY ASSURANCE
BEST PRACTICES FOR IMPLEMENTING PREDICTIVE ANALYTICS
Chapter 11_AI for Root Cause Analysis and Non-Conformance Management
AUTOMATING ROOT CAUSE IDENTIFICATION WITH AI
DO IT YOURSELF
ADVANCED RCA FOR CONNECTED AI
AI-DRIVEN NON-CONFORMANCE REPORTING AND TRACKING
DO IT YOURSELF
TREND ANALYSIS TO PREVENT RECURRING ISSUES
ADVANCED CASE STUDY: AI-ENHANCED NON-CONFORMANCE MANAGEMENT
Chapter 12 Machine Learning in Visual Inspection
USING COMPUTER VISION FOR AUTOMATED INSPECTIONS
CASE STUDY: AI-POWERED INSPECTION IN AUTOMOTIVE MANUFACTURING
Chapter 13_Process Optimization and Lean AI
USING AI TO IDENTIFY BOTTLENECKS IN PROCESSES
ENHANCING LEAN AND SIX SIGMA INITIATIVES WITH ML
VALUE STREAM MAPPING WITH AI-DRIVEN INSIGHTS
EXAMPLES OF AI TOOLS FOR VSM
Chapter 14_AI-Powered Supply Chain Quality
MANAGING SUPPLIER QUALITY WITH AI
PREDICTIVE ANALYTICS FOR SUPPLY CHAIN DISRUPTIONS
ENHANCING TRACEABILITY AND RECALL READINESS
CASE STUDY: AI-DRIVEN SUPPLY CHAIN RESILIENCE IN ELECTRONICS MANUFACTURING
Chapter 15_Risk Management with AI
RISK MANAGEMENT AND AI LIFE CYCLE
LIFE CYCLE PHASE 1: INCEPTION
PHASE 2: DESIGN AND DEVELOPMENT
PHASE 3: VERIFICATION AND VALIDATION
PHASE 4: DEPLOYMENT
PHASE 5: OPERATION AND MONITORING
PHASE 6: CONTINUOUS VALIDATION AND RE-EVALUATION
PHASE 7: RETIREMENT
ENTERPRISE RISK AND RISK REGISTER
CYBERSECURITY RISK MANAGEMENT
AI IN FMEA AND HAZARD ANALYSIS
AI-DRIVEN RISK CONTROLS
REAL-TIME RISK MONITORING AND MITIGATION
CASE STUDY: AI-POWERED FMEA IN PHARMACEUTICALS
Chapter 16_Training and Knowledge Management in the AI Era
AI TOOLS FOR EMPLOYEE TRAINING AND CERTIFICATION
CREATING A KNOWLEDGE REPOSITORY WITH AI
Chapter 17_Ethical Considerations and Challenges
DATA PRIVACY AND SECURITY
BIAS IN AI/ML MODELS: IDENTIFICATION AND MITIGATION
BALANCING AUTOMATION WITH HUMAN OVERSIGHT
CASE STUDY: ENSURING ETHICAL AI INMEDICAL DEVICE MANUFACTURING
Chapter 18_AI and Reliability
AI APPLICATIONS IN RELIABILITY MANAGEMENT
ENHANCING RELIABILITY DESIGN WITH AI
REAL-TIME RELIABILITY MONITORING
CASE STUDY: AI-DRIVEN RELIABILITY IN AEROSPACE
Chapter 19_The AI Life Cycle
Appendix A_List of Large Language Models (LLMs)
Appendix B_Build a Convolutional Neural Network (CNN) to Detect Defects Using Image Processing
Appendix C_OpenCV Example of AI Vision Detecting Surface Defects on a Conveyor Belt
Glossary
Endnotes

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