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Zero to AI

Author Nicolo Valigi , Gianluco Mauro
Release Date: 2020/05/01
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Book Description

Zero to AI uses clear examples and jargon-free explanations to show the practical benefits of AI. Each chapter explores a real-world case study demonstrating how companies like Google and Netflix use AI to shape their industries. You begin at the beginning, with a primer on core AI concepts and realistic business outcomes. To help you prepare for the transition, the book breaks down a successful AI implementation, including advice on hiring the right team and making decisions about resources, risks, and costs.

Table of Contents

  1. Zero to AI
  2. Copyright 2020 ©Manning Publications Co.
  3. brief contents
  4. contents
  5. Preface
    1. acknowledgments
    2. about this book
      1. Who should read this book
      2. liveBook discussion forum
      3. Other online resources
    3. about the authors
  6. Chapter 1. An introduction to artificial intelligence
    1. 1.1 The path to modern AI
    2. 1.2 The engine of the AI revolution: machine learning
    3. 1.3 What is artificial intelligence, after all?
    4. 1.4 Our teaching method
    5. Summary
  7. Part 1. Understanding AI
  8. Chapter 2. Artificial intelligence for core business data
    1. 2.1 Unleashing AI on core business data
    2. 2.2 Using AI with core business data
      1. 2.2.1 The real estate marketplace example
      2. 2.2.2 Adding AI capabilities to FutureHouse
      3. 2.2.3 The machine learning advantage
      4. 2.2.4 Applying AI to general core business data
    3. 2.3 Case studies
      1. 2.3.1 How Google used AI to cut its energy bill
      2. 2.3.2 How Square used AI to lend billions to small businesses
      3. 2.3.3 Case studies lessons
    4. 2.4 Evaluating performance and risk
    5. Summary
  9. Chapter 3. AI for sales and marketing
    1. 3.1 Why AI for sales and marketing
    2. 3.2 Predicting churning customers
    3. 3.3 Using AI to boost conversion rates and upselling
    4. 3.4 Performing automated customer segmentation
      1. 3.4.1 Unsupervised learning (or clustering)
      2. 3.4.2 Unsupervised learning for customer segmentation
    5. 3.5 Measuring performance
      1. 3.5.1 Classification algorithms
      2. 3.5.2 Clustering algorithms
    6. 3.6 Tying ML metrics to business outcomes and risks
    7. 3.7 Case studies
      1. 3.7.1 AI to refine targeting and positioning: Opower
      2. 3.7.2 AI to anticipate customer needs: Target
    8. Summary
  10. Chapter 4. AI for media
    1. 4.1 Improving products with computer vision
    2. 4.2 Using AI for image classification: deep learning?
    3. 4.3 Using transfer learning with small datasets
    4. 4.4 Face recognition: teaching computers to recognize people
    5. 4.5 Using content generation and style transfer
    6. 4.6 What to watch out for
    7. 4.7 AI for audio
    8. 4.8 Case study: optimizing agriculture with deep learning
      1. 4.8.1 Case questions
      2. 4.8.2 Case discussion
    9. Summary
  11. Chapter 5. AI for natural language
    1. 5.1 The allure of natural language understanding
    2. 5.2 Breaking down NLP: Measuring complexity
    3. 5.3 Adding NLP capabilities to your organization
      1. 5.3.1 Sentiment analysis
      2. 5.3.2 From sentiment analysis to text classification
      3. 5.3.3 Scoping a NLP classification project
      4. 5.3.4 Document search
      5. 5.3.5 Natural conversation
      6. 5.3.6 Designing products that overcome technology limitations
    4. 5.4 Case study: Translated
      1. 5.4.1 Case questions
      2. 5.4.2 Case discussion
    5. Summary
  12. Chapter 6. AI for content curation and community building
    1. 6.1 The curse of choice
    2. 6.2 Driving engagement with recommender systems
      1. 6.2.1 Content-based systems beyond simple features
      2. 6.2.2 The limitations of features and similarity
    3. 6.3 The wisdom of crowds: collaborative filtering
    4. 6.4 Recommendations gone wrong
      1. 6.4.1 The recommender system dream
    5. 6.5 Case study: Netflix saves $1 billion a year
      1. 6.5.1 Netflix’s recommender system
      2. 6.5.2 Recommendations and user experience
      3. 6.5.3 The business value of recommendations
      4. 6.5.4 Case questions
      5. 6.5.5 Case discussion
    6. Summary
  13. Part 2. Building AI
  14. Chapter 7. Ready—finding AI opportunities
    1. 7.1 Don’t fall for the hype: Business-driven AI innovation
    2. 7.2 Invention: Scouting for AI opportunities
    3. 7.3 Prioritization: Evaluating AI projects
    4. 7.4 Validation: Analyzing risks
    5. 7.5 Deconstructing an AI product
    6. 7.6 Translating an AI project into ML-friendly terms
    7. 7.7 Exercises
      1. 7.7.1 Improving customer targeting
      2. 7.7.2 Automating industrial processes
      3. 7.7.3 Helping customers choose content
    8. Summary
  15. Chapter 8. Set—preparing data, technology, and people
    1. 8.1 Data strategy
      1. 8.1.1 Where do I get data?
      2. 8.1.2 How much data do I need?
    2. 8.2 Data quality
    3. 8.3 Recruiting an AI team
    4. Summary
  16. Chapter 9. Go—AI implementation strategy
    1. 9.1 Buying or building AI
      1. 9.1.1 The Buy option: Turnkey solutions
      2. 9.1.2 The Borrow option: ML platforms
      3. 9.1.3 The Build option: Roll up your sleeves
    2. 9.2 Using the Lean Strategy
      1. 9.2.1 Starting from Buy solutions
      2. 9.2.2 Moving up to Borrow solutions
      3. 9.2.3 Doing things yourself: Build solutions
    3. 9.3 Understanding the virtuous cycle of AI
    4. 9.4 Managing AI projects
    5. 9.5 When AI fails
      1. 9.5.1 Anki
      2. 9.5.2 Lighthouse AI
      3. 9.5.3 IBM Watson in Oncology
      4. 9.5.4 Emotional diary
      5. 9.5.5 Angry phone calls
      6. 9.5.6 Underperforming sales
    6. Summary
  17. Chapter 10. What lies ahead
    1. 10.1 How AI threatens society
      1. 10.1.1 Bias and fairness
      2. 10.1.2 AI and jobs
      3. 10.1.3 The AI filter bubble
      4. 10.1.4 When AI fails: Corner cases and adversarial attacks
      5. 10.1.5 When the artificial looks real: AI-generated fake content
    2. 10.2 Opportunities for AI in society
      1. 10.2.1 Democratization of technology
      2. 10.2.2 Massive scale
    3. 10.3 Opportunities for AI in industries
      1. 10.3.1 Social media networks
      2. 10.3.2 Health care
      3. 10.3.3 Energy
      4. 10.3.4 Manufacturing
      5. 10.3.5 Finance
      6. 10.3.6 Education
    4. 10.4 What about general AI?
    5. 10.5 Closing thoughts
    6. Summary
  18. index
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