AI for Good

AI for Good

by Juan M. Lavista Ferres, William B. Weeks, Brad Smith
Released April 2024
Publisher(s): Wiley
ISBN: 9781394235872

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Book description

FOREWORD BY BRAD SMITH, VICE CHAIR AND PRESIDENT OF MICROSOFT

Discover how AI leaders and researchers are using AI to transform the world for the better

In AI for Good: Applications in Sustainability, Humanitarian Action, and Health, a team of veteran Microsoft AI researchers delivers an insightful and fascinating discussion of how one of the world's most recognizable software companies is tackling intractable social problems with the power of artificial intelligence (AI). In the book, you’ll see real in-the-field examples of researchers using AI with replicable methods and reusable AI code to inspire your own uses.

The authors also provide:

  • Easy-to-follow, non-technical explanations of what AI is and how it works
  • Examples of the use of AI for scientists working on mitigating climate change, showing how AI can better analyze data without human bias, remedy pattern recognition deficits, and make use of satellite and other data on a scale never seen before so policy makers can make informed decisions
  • Real applications of AI in humanitarian action, whether in speeding disaster relief with more accurate data for first responders or in helping address populations that have experienced adversity with examples of how analytics is being used to promote inclusivity
  • A deep focus on AI in healthcare where it is improving provider productivity and patient experience, reducing per-capita healthcare costs, and increasing care access, equity, and outcomes
  • Discussions of the future of AI in the realm of social benefit organizations and efforts
Beyond the work of the authors, contributors, and researchers highlighted in the book, AI For Good begins with a foreword from Microsoft Vice Chair and President Brad Smith. There, Smith details the Microsoft rationale behind the creation of and continued investment in the AI for Good Lab. The vision is one of hope with AI saving lives in disasters, improving health care globally, and Microsoft's mission to make sure AI's benefits are available to all.

An essential guide to impactful social change with artificial intelligence, AI for Good is a must-read resource for technical and non-technical professionals interested in AI’s social potential, as well as policymakers, regulators, NGO professionals, and non-profit volunteers.

Table of contents

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Foreword
  5. Introduction
    1. A Call to Action
  6. Part I: Primer on Artificial Intelligence and Machine Learning
    1. Chapter 1: What Is Artificial Intelligence and How Can It Be Used for Good?
      1. What Is Artificial Intelligence?
      2. What If Artificial Intelligence Were Used to Improve Societal Good?
    2. Chapter 2: Artificial Intelligence: Its Application and Limitations
      1. Why Now?
      2. The Challenges and Lessons Learned from Using Artificial Intelligence
      3. Large Language Models
    3. Chapter 3: Commonly Used Processes and Terms
      1. Common Processes
      2. Commonly Used Measures
      3. The Structure of the Book
  7. Part II: Sustainability
    1. Chapter 4: Deep Learning with Geospatial Data
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    2. Chapter 5: Nature-Dependent Tourism
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    3. Chapter 6: Wildlife Bioacoustics Detection
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    4. Chapter 7: Using Satellites to Monitor Whales from Space
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    5. Chapter 8: Social Networks of Giraffes
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    6. Chapter 9: Data-driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    7. Chapter 10: Mapping Industrial Poultry Operations at Scale
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    8. Chapter 11: Identifying Solar Energy Locations in India
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    9. Chapter 12: Mapping Glacial Lakes
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    10. Chapter 13: Forecasting and Explaining Degradation of Solar Panels with AI
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
  8. Part III: Humanitarian Action
    1. Chapter 14: Post-Disaster Building Damage Assessment
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    2. Chapter 15: Dwelling Type Classification
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    3. Chapter 16: Damage Assessment Following the 2023 Earthquake in Turkey
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    4. Chapter 17: Food Security Analysis
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    5. Chapter 18: BankNote-Net: Open Dataset for Assistive Universal Currency Recognition
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    6. Chapter 19: Broadband Connectivity
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    7. Chapter 20: Monitoring the Syrian War with Natural Language Processing
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    8. Chapter 21: The Proliferation of Misinformation Online
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    9. Chapter 22: Unlocking the Potential of AI with Open Data
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
  9. Part IV: Health
    1. Chapter 23: Detecting Middle Ear Disease
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    2. Chapter 24: Detecting Leprosy in Vulnerable Populations
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    3. Chapter 25: Automated Segmentation of Prostate Cancer Metastases
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    4. Chapter 26: Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    5. Chapter 27: Long-Term Effects of COVID-19
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    6. Chapter 28: Using Artificial Intelligence to Inform Pancreatic Cyst Management
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    7. Chapter 29: NLP-Supported Chatbot for Cigarette Smoking Cessation
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    8. Chapter 30: Mapping Population Movement Using Satellite Imagery
      1. Executive Summary
      2. Why Is This Important?
      3. Methods Used
      4. Findings
      5. Discussion
      6. What We Learned
    9. Chapter 31: The Promise of AI and Generative Pre-Trained Transformer Models in Medicine
      1. What Are GPT Models and What Do They Do?
      2. GPT Models in Medicine
      3. Conclusion
  10. Part V: Summary, Looking Forward, and Additional Resources
    1. Epilogue: Getting Good at AI for Good
      1. Communication
      2. Data
      3. Modeling
      4. Impact
      5. Conclusion
      6. Key Takeaways
  11. AI and Satellites: Critical Tools to Help Us with Planetary Emergencies
    1. Amazing Things in the Amazon
    2. Quick Help Saving Lives in Disaster Response
  12. Additional Resources
    1. Endnotes
  13. Acknowledgments
  14. About the Editors
  15. About the Authors
    1. Microsoft's AI for Good Lab
    2. Collaborators
  16. Index
  17. Copyright
  18. Dedication
  19. End User License Agreement

Product information

  • Title: AI for Good
  • Author(s): Juan M. Lavista Ferres, William B. Weeks, Brad Smith
  • Release date: April 2024
  • Publisher(s): Wiley
  • ISBN: 9781394235872