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Zero to AI
A non-technical, hype-free guide to prospering in the AI era
Nicolò Valigi and Gianluca Mauro
  • MEAP began August 2019
  • Publication in April 2020 (estimated)
  • ISBN 9781617296062
  • 270 pages (estimated)
  • printed in black & white

The best book I've found on realistic AI applications: factual, accurate and hype-free.

Alain Couniot
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.
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

1.5 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 their data centers’ energy bill by 40%

2.3.2 How Square used AI to lend billions to small businesses

2.3.3 Case studies learnings

2.4 Evaluating performance and risk

2.5 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 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 Machine Learning metrics to business outcomes and risks

3.7 AI for sales and marketing case studies

3.7.1 AI to refine targeting and positioning - Opower

3.7.2 AI to anticipate customers’ needs: Target

3.8 Summary

4 AI for media

4.1 Improving products with Computer Vision

4.2 AI for image classification: What is Deep Learning?

4.3 Small datasets and Transfer Learning

4.4 Face recognition: teaching computers to recognize people

4.5 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

4.9 Summary

5 AI for natural language

5.1 The allure of Natural Language Understanding

5.2 Breaking down Natural Language Processing: 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.com

5.5 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

6.5 Case study: How Netflix saves $1bn a year using Recommender Systems

6.5.1 Case Questions

6.5.2 Case Discussion

6.6 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 Validate: 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

7.8 Summary

8 Set - preparing data, technology and people

8.1 Data Strategy

8.2 Where do I get data?

8.3 How much data do I need?

8.4 Data quality

8.5 Recruiting an AI team

8.6 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 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 The virtuous cycle of AI

9.4 Managing AI projects

9.5 When AI fails

9.6 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 Healthcare

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

10.6 Summary

About the Technology

Artificial Intelligence is a broad term for computer systems. Unprecedented access to raw data and affordable computing power, along with incredible advances in AI, put these smart, powerful, machine learning systems within reach of nearly any organization. Businesses in every industry are using AI to streamline processes, personalize marketing, improve customer engagement, and grow their bottom lines.

About the book

Zero to AI teaches business leaders, entrepreneurs, and decision makers how to improve the success and efficiency of their businesses by taking advantage of state-of-the-art AI technologies. After a brief introduction to artificial intelligence, you’ll explore examples that demonstrate how you can use AI for analyzing business data, predicting customer buying trends, deciphering text and images, and much more.

The book is filled with extensive, real-world case studies. As you go, you’ll learn how Google applied AI models to improve on century-old engineering rules to save energy (and money) in its data centers. You’ll look under the hood of the models that power Netflix’s video recommendations and see how they compare with the algorithms that Target uses to prepare their customized promotions. For each case study, the authors discuss the best plan of attack, the necessary resources, the possible risk factors, and likely business benefits of the specific AI application. When you’re done, you’ll have a complete roadmap for realizing the vast potential of AI in your own organization!

What's inside

  • Identifying opportunities for applying AI in your organization
  • Designing an AI strategy
  • Hiring AI talent
  • Managing an AI project
  • Using AI for boosting conversion rates
  • Content curation and community building with AI
  • Image classification and object recognition
  • Interesting real-world case studies from companies like Google, Square, Netflix, and Target

About the reader

Written for business leaders, entrepreneurs, and decision makers as well as technology implementers looking for a big-picture view of AI. No prior programming or machine learning knowledge is required.

About the authors

Nicolò Valigi and Gianluca Mauro co-founded AI Academy, a company that advises on AI strategy and runs workshops teaching the concepts covered in this book.

Nicolò is a Software Engineer that enjoys making intelligent robots. He has an MBA and also won a Fulbright scholarship for Entrepreneurship. He writes about technology and AI, and is a regular speaker at international conferences.

Gianluca, also a Fulbright Scholar, is an independent consultant who has shared his expertise on innovation and data science with companies ranging from idea-phase startups to multinational corporations.

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