AI in Business: Creating Value Responsibly ISE

Höfundar: Patrick Johanns, James Chaffee, Jackie Rees Ulmer (Útgáfa: 0)
AI in Business: Creating Value Responsibly ISE

Kaup valmöguleikar

Gervigreind (AI) er að umbreyta atvinnugreinum og því er mikilvægt að nemendur öðlist þekkingu til að skilja möguleika hennar og nýta þá á áhrifaríkan hátt. Það eykur hæfni þeirra til að takast á við kröfur atvinnulífsins. AI in Business er mikilvæg leiðarvísir um síbreytilegt hlutverk gervigreindar í nútímafyrirtækjum. Bókin fjallar um umbreytandi áhrif gervigreindar við fjölbreyttar viðskiptaaðstæður og útskýrir tæknileg hugtök á skýran og aðgengilegan hátt. Meðal umfjöllunarefna eru gagnastjórnun og skapandi gervigreind.

Nánar um bókina

Útgefandi
McGraw-Hill Higher Education (International)
ISBN
9781265658632
Print ISBN
9781265658632
Format
ePub
Útgáfa
0
Höfundar
Patrick Johanns, James Chaffee, Jackie Rees Ulmer
Tungumál
English
Útgefið
2026-01-06
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Frontmatter
  • Cover Page
  • Title Page
  • Copyright Page
  • Dedications
  • About the Authors
  • Patrick (Pat) Johanns
  • James Chaffee
  • Jackie Rees Ulmer
  • A Letter to New Adopters
  • Guided Tour
  • Learning Objectives
  • Focus on Ethics and Sustainability
  • Visual Diagrams
  • Summary of Key Points
  • Quick Wins
  • Case Studies
  • Connect
  • Acknowledgments
  • Brief Contents
  • Detailed Table of Contents
  • Chapter 1: Overview of AI in Business
  • Chapter 1: Overview of AI in Business
  • Introduction
  • Types of Artificial Intelligence
  • Machine Learning
  • Neural Networks
  • Generative AI
  • Other Applications of AI in Business
  • The Use of Artificial Intelligence in Virtual Reality
  • Computer Vision and Its Role in Business Applications
  • Speech Recognition and Its Business Impact
  • Data Science and AI
  • Ethical Considerations in AI
  • Data Ownership, Privacy, and Consent
  • Environmental Sustainability
  • Economic Inequality and AI Access
  • Bias and Discrimination
  • Manipulation and Deception
  • Intellectual Property and AI Creativity
  • Transparency and Accountability
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 1.1: The Portrait of Edmond De Belamy—Intellectual Property and AI Creativity
  • U.S. and French Copyright Laws
  • Discussion Questions
  • Case References
  • Case Study 1.2: Coded Bias
  • Discussion Questions
  • Case References
  • Case Study 1.3: Closing the Loop—How H&M and HKRITA Use AI to Revolutionize Textile Recycling
  • What Happens after Sorting?
  • Insights
  • AI’s Growing Presence in Physical, Traditionally Manual Industries
  • The Potential of AI for Sustainability and Circular Economies
  • The Importance of Data-Rich Environments
  • AI in Long-Term Environmental Goals
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 2: The Rise of AI: Innovation and Impact
  • Chapter 2: The Rise of AI: Innovation and Impact
  • Introduction
  • Foundations of AI (Pre–1950s)
  • Artistic Foundations
  • Scientific Foundations: Early Thinkers and Theories
  • The Dawn of AI (1950–1970)
  • The Turing Test: Can Machines Think?
  • The Dartmouth Conference
  • Early AI Programs
  • Emerging Computer Technologies
  • Impact on Business Practices
  • The Age of Experimentation (1970–1985)
  • Expert Systems
  • Advancements in Neural Networks
  • The Microprocessor Revolution and Technological Foundations
  • Impact of AI on Business from 1970–1985
  • The AI Winter and Resurgence (1985–2000)
  • The Resurgence: Seeds of Renewal
  • Technological Foundations
  • AI and Robotics in Business (1985–2000)
  • The Age of Big Data and Machine Learning (2000–2020)
  • Technological Foundations
  • Business Applications
  • The Generative AI Revolution (2020–Present)
  • Scientific and Technological Advancements
  • AI and the Internet of Things: A Seamless, Intelligent World
  • Ethical, Safety, and Privacy Concerns in AI
  • AI’s Future: Opportunities and Challenges
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Case Study 2.1: AI and COVID-19
  • AI’s Role in the COVID-19 Response
  • The Pandemic’s Impact on AI Adoption
  • Discussion Questions
  • Case References
  • Case Study 2.2: Project Innereye: Expanding AI in Medicine
  • Discussion Questions
  • Case References
  • Case Study 2.3: Pioneering Women’s Sports Analytics with AI
  • AI’s Journey in Sports Analytics
  • A Historical First for Women’s Sports
  • Impact on the Game
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 3: Data Management for AI
  • Chapter 3: Data Management for AI
  • Introduction
  • Data for AI
  • Data Structure and Types
  • Sources of Data for AI
  • Data Security
  • Data Security Challenges
  • AI Data Security Principles
  • Data Quality and Integrity
  • High Quality Data: Characteristics and Methods
  • Data Cleaning
  • Building a Data-Ready Organization
  • Data Regulations
  • Broad Governmental Policies
  • Industry-Specific Data and AI Regulations
  • Big Data and Data Management
  • Larger Data Centers
  • Distributed Cloud-Based Storage Systems
  • Sustainability in Storage
  • Emerging Technologies in AI Data Management
  • Blockchain
  • AI-Powered Cybersecurity
  • Edge Computing and Storage
  • Federated Learning
  • Ethical Considerations in AI Data Collection and Use
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 3.1: Amazon—AI in the Hiring Process
  • The Start of the Journey
  • Clues of Bias
  • Repercussions and Repairs
  • Discussion Questions
  • Case References
  • Case Study 3.2: Darktrace—AI in Network Security
  • Benefits and Challenges
  • Broader Implications
  • Discussion Questions
  • Case References
  • Case Study 3.3: Tradelens—Blockchain as a Solution, But Why Did It Fail?
  • Blockchain as a Solution
  • An Unexpected Failure
  • Lessons from Failure
  • Conclusion
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 4: Machine Learning
  • Chapter 4: Machine Learning
  • Introduction
  • How Machine Learning Models Learn
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Machine Learning in Business Functions
  • Data Management and Preprocessing
  • Prediction and Forecasting
  • Classification and Risk Assessment
  • Customer Segmentation and Personalization
  • Process Optimization and Automation
  • Comparing Standard Machine Learning Algorithms
  • Ethical Risks of Machine Learning in Business
  • Algorithmic Bias in Machine Learning
  • Strategies for Reducing Bias in Machine Learning Systems
  • Other Concerns
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 4.1: Netflix Recommendation System
  • Netflix’s History
  • The Heart of Netflix: Its Recommender Grid
  • The Data Driving the System
  • Challenges and Ethical Considerations
  • Business Impact
  • Discussion Questions
  • Case References
  • Case Study 4.2: Flight Delays at JFK
  • Implementation
  • Discussion Questions
  • Case References
  • Case Study 4.3: UPS Vehicle Routing
  • Challenges for Logistics Companies
  • UPS and ORION: AI for Efficiency
  • The Left-Turn Problem
  • Real-Time Optimization and Cost Savings
  • Conclusion
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 4S: Machine Learning Technical Supplement
  • Chapter 4S: Machine Learning Technical Supplement
  • Principal Component Analysis (PCA)
  • How PCA Works
  • Business Applications of PCA
  • Advantages of PCA
  • Limitations of PCA
  • K-Means Clustering
  • How k-Means Clustering Works
  • Business Applications of k-Means Clustering
  • Advantages of k-Means Clustering
  • Limitations of k-Means Clustering
  • K-Nearest Neighbors (K-NN)
  • How k-NN Works
  • Business Applications of k-NN
  • Advantages of k-NN
  • Limitations of k-NN
  • Linear Regression
  • How Linear Regression Works
  • Business Applications of Linear Regression
  • Advantages of Linear Regression
  • Limitation of Linear Regression
  • Decision Trees
  • How Decision Trees Work
  • Business Applications of Decision Trees
  • Advantages of Decision Trees
  • Limitations of Decision Trees
  • Random Forests
  • How Random Forests Work
  • Business Applications of Random Forests
  • Advantages of Random Forests
  • Limitations of Random Forests
  • Logistic Regression
  • How Logistic Regression Works
  • Business Applications of Logistic Regression
  • Advantages of Logistic Regression
  • Limitations of Logistic Regression
  • Support Vector Machines (SVMs)
  • How SVM Works
  • Business Applications of SVMs
  • Advantages of SVMs
  • Limitations of SVMs
  • Collaborative Filtering
  • How Collaborative Filtering Works
  • Business Applications of Collaborative Filtering
  • Advantages of Collaborative Filtering
  • Limitations of Collaborative Filtering
  • Time Series Analysis
  • How Time Series Analysis Works
  • Business Applications of Time Series Analysis
  • Advantages of Time Series Analysis
  • Limitations of Time Series Analysis
  • Metrics for the Quality of Algorithms
  • Confusion Matrix
  • Metrics
  • Summary
  • Algorithm Selection Questions
  • Metrics and Evaluation Questions
  • Chapter 5: Neural Networks and Deep Learning
  • Chapter 5: Neural Networks and Deep Learning
  • Introduction
  • Clarifying Neural Networks and Deep Learning
  • The Growing Importance of Neural Networks
  • Business Applications of Neural Networks
  • Why Businesses Are Adopting Neural Networks
  • Real-World Impacts
  • Deep Learning
  • Deep Learning’s Role in Business
  • Deep Learning in Natural Language Processing
  • NLP Applications in Business
  • Limitations of Neural Networks and Deep Learning
  • Emerging Capabilities in Deep Learning
  • Foundation Models
  • Multimodal Learning
  • Few-Shot and Zero-Shot Learning
  • Energy-Efficient Deep Learning (Green AI)
  • Deep Learning for Personalized Adaptive Systems
  • Deep Learning and Virtual Reality
  • Growth of Neural Network Models
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 5.1: IBM Watson Openscale—Explainable AI
  • IBM Watson and OpenScale
  • Challenges and Tradeoffs
  • Conclusion
  • Discussion Questions
  • Case References
  • Case Study 5.2: Tesla Autopilot—The Promise and Peril of Deep Learning–Driven Autonomy
  • The Market for Autonomous Vehicles
  • Deep Learning and Tesla’s Autonomy Strategy
  • Challenges in Technology and Regulation
  • Legal Difficulties
  • Conclusion
  • Discussion Questions
  • Case References
  • Case Study 5.3: Facial Recognition Technology and the Detroit Lions
  • Technology Overview
  • Applications and Business Value
  • Discussion Questions
  • Case Reference
  • Endnotes
  • Chapter 5S: Neural Networks and Deep Learning Concepts Technical Supplement
  • Chapter 5S: Neural Networks and Deep Learning Concepts Technical Supplement
  • Traditional Machine Learning vs. Deep Learning
  • How Neural Networks Work
  • Neural Network Architecture
  • Activation Functions
  • Forward Propagation and Backpropagation
  • Training with Stochastic Gradient Descent
  • Office Building Analogy
  • Overfitting
  • Deep Learning Model Types
  • Convolutional Neural Networks: Specialists in Visual Data
  • Recurrent Neural Networks: Specialists in Sequences and Time Series
  • Transformers and Attention Mechanisms
  • What Are Transformers?
  • How Attention Mechanisms Work
  • Transformers in Modern AI Applications
  • Conclusion
  • Endnotes
  • Chapter 6: Generative AI in Business: Transforming Industries with Innovative Technology
  • Chapter 6: Generative AI in Business: Transforming Industries with Innovative Technology
  • Introduction
  • What Is Generative AI?
  • Predictive vs. Generative AI
  • Types of Generative AI
  • How Generative AI Works
  • Training Generative Models
  • Text Generation: Step by Step
  • Image Generation
  • Multimodal and Video Generation
  • Business Applications of Generative AI
  • Text-Based Applications
  • Custom GPTs
  • Chatbots and Customer Engagement
  • Generative Visual Content and Virtual Try-On
  • Product Design
  • Generative AI in Contract Review
  • Training and Onboarding
  • Multimodal and Interactive Experiences
  • Specialized Use Cases across Industries
  • Risks and Limitations of Generative AI
  • Hallucinations and Factual Inaccuracy
  • Bias and Fairness
  • Intellectual Property and Legal Concerns
  • Transparency and Explainability
  • Ethical Use and Governance
  • Work Role Changes
  • Business Models in Generative AI
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 6.1: When AI Makes False Claims—The OpenAI Libel Controversy
  • Discussion Questions
  • Case References
  • Case Study 6.2: AI Voice Cloning
  • How It Works
  • Uses—and Abuses
  • When Voice Cloning Helps
  • Grief and the Gray Areas
  • Legal and Ethical Challenges
  • Discussion Questions
  • Case References
  • Case Study 6.3: Revolutionizing Soccer Tactics with TacticAI
  • The Role of TacticAI
  • How It Works
  • Business and Competitive Value
  • Potential Risks and Limitations
  • The Future of Sports AI
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 6S: Generative AI Technical Supplement
  • Chapter 6S: Generative AI Technical Supplement
  • Key Technical Components
  • Tokenization and Embeddings
  • Transformer Architectures and Autoregressive Generation
  • Core Generative Model Types
  • Transformers
  • Diffusion Models
  • Generative Adversarial Networks
  • Variational Autoencoders
  • Recurrent Neural Networks
  • How Generative AI Balances Creativity and Coherence
  • Sampling Strategies in Generative AI
  • Generative AI Isn’t Done Evolving
  • Endnotes
  • Chapter 7: Strategic AI Implementation: People, Process, and Purpose
  • Chapter 7: Strategic AI Implementation: People, Process, and Purpose
  • Introduction
  • Assessing Organizational Readiness for AI
  • Data Readiness
  • Integrating Data across the Organization
  • Technological Readiness
  • Business Process Readiness
  • Workforce Readiness
  • Cultural and Leadership Readiness
  • Identifying Business Functions Ready for AI Integration
  • Customer Service
  • Marketing
  • Operations and Supply Chain Management
  • Other Departmental Applications
  • Aligning AI Projects with Business Objectives
  • Implementing AI Solutions
  • Selecting the Right AI Tools
  • Selecting the Right AI Vendors
  • Monitoring and Measuring AI Effectiveness
  • Innovation and Creativity Metrics
  • Operational Efficiency Metrics
  • Customer Experience Metrics
  • Business Outcome Metrics
  • Balanced Scorecard Approach
  • Basic Ethical, Legal, and Regulatory Considerations in AI Implementation
  • Curate Diverse and Ethical Training Data Biases
  • Ensure Explainability
  • Promote Transparency
  • Continually Assess Risks and Benefits
  • Ensure Accountability
  • Plan for Workforce Impact
  • Legal and Regulatory Considerations
  • A Final Note
  • Special Considerations for Generative AI
  • Ethical Foundations for Generative AI
  • Reducing Hallucinations and Misinformation
  • Managing Workforce Disruptions
  • Safeguarding User Consent and Control
  • Navigating Copyright and Intellectual Property Risks
  • Legal Accountability in Generative AI Systems
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 7.1: Mastercard Fraud Detection
  • AI as a Solution
  • Implementation Decision
  • The Launch of Decision Intelligence
  • Outcomes
  • AI Expansion beyond Fraud
  • Takeaways
  • Discussion Questions
  • Case References
  • Case Study 7.2: Pfizer-BioNTech COVID-19 Vaccine
  • A Compressed Timeline—Enabled by AI
  • AI Contributions across the Development Pipeline
  • Why It Worked
  • Ethical and Operational Considerations
  • Conclusion
  • Discussion Questions
  • Case References
  • Case Study 7.3: The New York Times and Google Cloud AI
  • Digitization: A Human-Driven Process
  • AI Vendor Choice
  • From Preservation to Personalization
  • Discussion Questions
  • Case References
  • Endnotes
  • Chapter 8: The Art of Prompt Engineering with FOCUS
  • Chapter 8: The Art of Prompt Engineering with FOCUS
  • Introduction
  • The Focus Framework for Effective Prompts
  • General Principles of Prompt Crafting
  • Hard Prompts: Benchmarks for AI
  • Iterative Refinement
  • Prompt Refinement Example
  • Understanding AI Models
  • Closed-Domain Chatbots
  • Open-Domain Chatbots
  • How Chatbots Explain Themselves
  • Advanced Prompting Techniques
  • Task Decomposition
  • Additional Techniques and Strategies
  • Communicating Effectively with Business AI Tools
  • Personas in Prompt Engineering
  • Choosing the Right Persona
  • Using Personas Effectively
  • Potential Risks of Persona-Driven Prompts
  • Strategies for Verifying AI Responses
  • Ethical Considerations In Prompt Engineering
  • Ethical Prompt Crafting
  • Verification
  • Ethical Use of Generative AI Output
  • Intellectual Property and Creativity
  • Intellectual Property and AI
  • Ethical Prompting Checklist
  • The End Is Just the Beginning
  • Summary of Key Points and Quick Wins
  • QUICK WINS
  • Key Terms
  • Discussion Questions
  • Exercises
  • Case Study 8.1: 2023 Writers Guild of America Strike
  • Writers’ Concerns about AI
  • The Studios’ View
  • Legal and Ethical Implications
  • Resolution and Aftermath
  • Discussion Questions
  • Case References
  • Case Study 8.2: Generative-AI Art in Board Games
  • Jenna’s Decision: Legends Unbound
  • Business Opportunities
  • Ethical Challenges
  • Conclusion
  • Discussion Questions
  • Case References
  • Case Study 8.3: Hallucinating the Law
  • What Went Wrong?
  • Could a Persona Have Helped?
  • Open vs. Closed Chatbots: Choosing the Right Tool
  • Prompt Engineering Lessons
  • The Legal and Ethical Fallout
  • The Bigger Picture
  • Discussion Questions
  • Case References
  • Endnotes
  • AI in Action: The Singapore Case Series
  • AI in Action: The Singapore Case Series
  • INTRODUCTION
  • A Seamless Morning in Singapore’s Smart Nation
  • Smart Home Start
  • Stepping into the Community
  • Public Infrastructure in Sync
  • Commute with Intelligence
  • Smart Commerce and Customer Experience
  • Workplace and Reflection
  • Discussion Questions
  • Case References
  • Foundations of a Smart Nation
  • A Glimpse of the Future
  • The Three Pillars of the Smart Nation Vision
  • Challenges and Opportunities in 2014
  • Singapore’s Strategic Shift toward AI
  • Discussion Questions
  • Case References
  • Singapore’s Data Engine
  • The Foundation of Data Infrastructure
  • Governance and Trust
  • From Data to Impact
  • Looking Ahead
  • Discussion Questions
  • Case References
  • Singapore’s Intelligent Spaces
  • Ari at the Bosch Campus
  • Singapore’s Green Plan and the Role of Machine Learning
  • The Sensor-Driven Transformation
  • Training the Model
  • Scaling toward a Smart Nation
  • Discussion Questions
  • Singapore’s Intelligent Transportation System
  • A Road That Thinks Ahead
  • Neural Networks in the Infrastructure
  • Making a Case for the Human Impact
  • A System That Sees Too Much?
  • A System That Finds Parking Too
  • A System That Clears the Way
  • The Future in the Foundations
  • Discussion Questions
  • Case References
  • AI in Urban Design
  • A Smarter Start to Design
  • Prompting Smarter Urban Design
  • Bias in the Training Data
  • Ethical and Technical Questions
  • The Bigger Picture
  • Discussion Questions
  • Case References
  • Personalizing Government: The LifeSG Project
  • A New Kind of Public Service
  • From Moments of Life to LifeSG: A Platform Evolves
  • Pilot Integration and AI Functionality
  • Reshaping Public Service Roles
  • Addressing Explainability and Trust
  • Citizen Adoption and Community Engagement
  • Smart Cities around the World
  • Discussion Questions
  • Case References
  • Telling the Smart Nation Story
  • Ari’s New Assignment
  • Calling in AI for Assistance
  • Applying the FOCUS Framework
  • The Hallucination Test
  • Breaking Down the Project
  • Ari at the Podium
  • Discussion Questions
  • Endnotes
  • Accessibility Content: Text Alternatives for Images
  • LO Extended Description (Frontmatter)
  • Basic ethical Extended Description (Frontmatter)
  • Sustainability Extended Description (Frontmatter)
  • Figure 6.3 Extended Description (Frontmatter)
  • Figure 8.1 Extended Description (Frontmatter)
  • Summary Extended Description (Frontmatter)
  • Figure 1.1 Extended Description (Chapter 1)
  • Figure 1.2 Extended Description (Chapter 1)
  • Figure 1.3 Extended Description (Chapter 1)
  • Unfigure 1.1 Extended Description (Chapter 1)
  • Unfigure 2.1 Extended Description (Chapter 2)
  • Figure 2.1 Extended Description (Chapter 2)
  • Unfigure 2.2 Extended Description (Chapter 2)
  • Figure 3.1 Extended Description (Chapter 3)
  • Figure 3.2 Extended Description (Chapter 3)
  • Figure 3.3 Extended Description (Chapter 3)
  • Figure 3.4 Extended Description (Chapter 3)
  • Figure 3.5 Extended Description (Chapter 3)
  • Figure 3.6 Extended Description (Chapter 3)
  • Figure 4.1 Extended Description (Chapter 4)
  • Figure 4.2 Extended Description (Chapter 4)
  • Figure 4.3 Extended Description (Chapter 4)
  • Figure 4.4 Extended Description (Chapter 4)
  • Figure 4S.1 Extended Description (Chapter 4S)
  • Figure 4S.2 Extended Description (Chapter 4S)
  • Figure 4S.3 Extended Description (Chapter 4S)
  • Figure 4S.4 Extended Description (Chapter 4S)
  • Figure 4S.5 Extended Description (Chapter 4S)
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  • Figure 4S.12 Extended Description (Chapter 4S)
  • Figure 5.2 Extended Description (Chapter 5)
  • Figure 5.3 Extended Description (Chapter 5)
  • Figure 5S.1 Extended Description (Chapter 5S)
  • Figure 6.1 Extended Description (Chapter 6)
  • Figure 6.2 Extended Description (Chapter 6)
  • Figure 6.3 Extended Description (Chapter 6)
  • Figure 6.4 Extended Description (Chapter 6)
  • Figure 6S.1 Extended Description (Chapter 6)
  • Figure 6S.2 Extended Description (Chapter 6)
  • Figure 6S.3 Extended Description (Chapter 6)
  • Figure 7.2 Extended Description (Chapter 7)
  • Figure 8.1 Extended Description (Chapter 8)
  • Figure 8.2 Extended Description (Chapter 8)
  • Figure 8.3 Extended Description (Chapter 8)