Zero to AI
A non-technical, hype-free guide to prospering in the AI era
Nicolò Valigi and Gianluca Mauro
  • April 2020
  • ISBN 9781617296062
  • 264 pages
  • printed in black & white

The best book, currently, on realistic AI applications: factual, accurate, and hype-free.

Alain Couniot, Sopra Steria Benelux
How can artificial intelligence transform your business? In Zero to AI, you’ll explore a variety of practical AI applications you can use to improve customer experiences, optimize marketing, help you cut costs, and more. In this engaging guide written for business leaders and technology pros alike, authors and AI experts Nicolò Valigi and Gianluca Mauro use fascinating projects, hands-on activities, and real-world explanations to make it clear how your business can benefit from AI.

About the Technology

There’s no doubt that artificial intelligence has made some impressive headlines recently, from besting chess and Go grand masters to producing uncanny deep fakes that blur the lines of reality. But what can AI do for you? If you want to understand how AI will impact your business before you invest your time and money, this book is for you.

About the book

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 detailed table of contents

1 An introduction to artificial intelligence

1.1 The path to modern AI

1.2 The engine of the AI revolution: machine learning

1.3 What is artificial intelligence, after all?

1.4 Our teaching method

Summary

Part 1: Understanding AI

2 Artificial intelligence for core business data

2.1 Unleashing AI on core business data

2.2 Using AI with core business data

2.2.1 The real estate marketplace example

2.2.2 Adding AI capabilities to FutureHouse

2.2.3 The machine learning advantage

2.2.4 Applying AI to general core business data

2.3 Case studies

2.3.1 How Google used AI to cut its energy bill

2.3.2 How Square used AI to lend billions to small businesses

2.3.3 Case studies lessons

2.4 Evaluating performance and risk

Summary

3 AI for sales and marketing

3.1 Why AI for sales and marketing

3.2 Predicting churning customers

3.3 Using AI to boost conversion rates and upselling

3.4 Performing automated customer segmentation

3.4.1 Unsupervised learning (or clustering)

3.4.2 Unsupervised learning for customer segmentation

3.5 Measuring performance

3.5.1 Classification algorithms

3.5.2 Clustering algorithms

3.6 Tying ML metrics to business outcomes and risks

3.7 Case studies

3.7.1 AI to refine targeting and positioning: Opower

3.7.2 AI to anticipate customer needs: Target

Summary

4 AI for media

4.1 Improving products with computer vision

4.2 Using AI for image classification: deep learning?

4.3 Using transfer learning with small datasets

4.4 Face recognition: teaching computers to recognize people

4.5 Using content generation and style transfer

4.6 What to watch out for

4.7 AI for audio

4.8 Case study: optimizing agriculture with deep learning

4.8.1 Case questions

4.8.2 Case discussion

Summary

5 AI for natural language

5.1 The allure of natural language understanding

5.2 Breaking down NLP: Measuring complexity

5.3 Adding NLP capabilities to your organization

5.3.1 Sentiment analysis

5.3.2 From sentiment analysis to text classification

5.3.3 Scoping a NLP classification project

5.3.5 Natural conversation

5.3.6 Designing products that overcome technology limitations

5.4 Case study: Translated

5.4.1 Case questions

5.4.2 Case discussion

Summary

6 AI for content curation and community building

6.1 The curse of choice

6.2 Driving engagement with recommender systems

6.2.1 Content-based systems beyond simple features

6.2.2 The limitations of features and similarity

6.3 The wisdom of crowds: collaborative filtering

6.4 Recommendations gone wrong

6.4.1 The recommender system dream

6.5 Case study: Netflix saves $1 billion a year

6.5.1 Netflix’s recommender system

6.5.2 Recommendations and user experience

6.5.3 The business value of recommendations

6.5.4 Case questions

6.5.5 Case discussion

Summary

Part 2: Building AI

7 Ready—​finding AI opportunities

7.1 Don’t fall for the hype: Business-driven AI innovation

7.2 Invention: Scouting for AI opportunities

7.3 Prioritization: Evaluating AI projects

7.4 Validation: Analyzing risks

7.5 Deconstructing an AI product

7.6 Translating an AI project into ML-friendly terms

7.7 Exercises

7.7.1 Improving customer targeting

7.7.2 Automating industrial processes

7.7.3 Helping customers choose content

Summary

8 Set—​preparing data, technology, and people

8.1 Data strategy

8.1.1 Where do I get data?

8.1.2 How much data do I need?

8.2 Data quality

8.3 Recruiting an AI team

Summary

9 Go --AI implementation strategy

9.1 Buying or building AI

9.1.1 The Buy option: Turnkey solutions

9.1.2 The Borrow option: ML platforms

9.1.3 The Build option: Roll up your sleeves

9.2 Using the Lean Strategy

9.2.1 Starting from Buy solutions

9.2.2 Moving up to Borrow solutions

9.2.3 Doing things yourself: Build solutions

9.3 Understanding the virtuous cycle of AI

9.4 Managing AI projects

9.5 When AI fails

9.5.1 Anki

9.5.2 Lighthouse AI

9.5.3 IBM Watson in Oncology

9.5.4 Emotional diary

9.5.5 Angry phone calls

9.5.6 Underperforming sales

Summary

10 What lies ahead

10.1 How AI threatens society

10.1.1 Bias and fairness

10.1.2 AI and jobs

10.1.3 The AI filter bubble

10.1.4 When AI fails: Corner cases and adversarial attacks

10.1.5 When the artificial looks real: AI-generated fake content

10.2 Opportunities for AI in society

10.2.1 Democratization of technology

10.2.2 Massive scale

10.3 Opportunities for AI in industries

10.3.1 Social media networks

10.3.2 Health care

10.3.3 Energy

10.3.4 Manufacturing

10.3.5 Finance

10.3.6 Education

10.4 What about general AI?

10.5 Closing thoughts

Summary

What's inside

  • Identifying where AI can help your organization
  • Designing an AI strategy
  • Evaluating project scope and business impact
  • Using AI to boost conversion rates, curate content, and analyze feedback
  • Understanding how modern AI works and what it can/can’t do

About the reader

For anyone who wants to gain an understanding of practical artificial intelligence and learn how to design and develop projects with high business impact.

About the authors

Gianluca Mauro and Nicolò Valigi are the cofounders of AI Academy, a company specializing in AI trainings and consulting.

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