AI for good: applications in sustainability, humanitarian action, and health
Publisher
John Wiley & Sons, Inc
Publication Date
[2024]
Language
English
Description
FOREWORD BY BRAD SMITH, VICE CHAIR AND PRESIDENT OF MICROSOFTDiscover 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
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.
More Details
Contributors
ISBN
9781394235889
Table of Contents
From the eBook
Cover
Title Page
Copyright Page
Contents
Foreword
Introduction
A Call to Action
Part I Primer on Artificial Intelligence and Machine Learning
Chapter 1 What Is Artificial Intelligence and How Can It Be Used for Good?
What Is Artificial Intelligence?
What If Artificial Intelligence Were Used to Improve Societal Good?
Chapter 2 Artificial Intelligence: Its Application and Limitations
Why Now?
The Challenges and Lessons Learned from Using Artificial Intelligence
Models Can Be Fooled by Bias
Predictive Power Does Not Imply Causation
AI Algorithms Can Discriminate
Models Can Cheat (the Problem with Shortcut Learning)
Models Do Not Generalize to Out-of-Distribution Cases
Models Can Be Gamed
Some Tools Can Be Used as Weapons
Models Can Create an Illusion of Certainty
AI Expertise Alone Cannot Solve World Problems
Conclusion
Large Language Models
Understanding Language Models
The Training Process: Learning Language Through Data
Historical Perspective: Two Decades of Evolution
The Generative Aspect of GPT
Pre-training: The P in GPT and Beyond
Transformers: The T in GPT and Its Revolutionary Impact
Limitations of LLMs
Demystifying AI's Intelligence
Understanding Truth
The Phenomenon of LLM Hallucinations
The Impact of LLMs
LLMs and the Power for Good
LLMs as a Language Aid
LLMs for Democratizing Coding
LLMs in Areas Like Medicine
Chapter 3 Commonly Used Processes and Terms
Common Processes
Commonly Used Measures
The Structure of the Book
Part II Sustainability
Chapter 4 Deep Learning with Geospatial Data
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 5 Nature-Dependent Tourism
Executive Summary
Why Is This Important?
Methods Used
Findings.
Discussion
What We Learned
Chapter 6 Wildlife Bioacoustics Detection
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 7 Using Satellites to Monitor Whales from Space
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 8 Social Networks of Giraffes
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 9 Data-driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 10 Mapping Industrial Poultry Operations at Scale
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 11 Identifying Solar Energy Locations in India
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 12 Mapping Glacial Lakes
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 13 Forecasting and Explaining Degradation of Solar Panels with AI
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Part III Humanitarian Action
Chapter 14 Post-Disaster Building Damage Assessment
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 15 Dwelling Type Classification
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 16 Damage Assessment Following the 2023 Earthquake in Turkey
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion.
What We Learned
Chapter 17 Food Security Analysis
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 18 BankNote-Net: Open Dataset for Assistive Universal Currency Recognition
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 19 Broadband Connectivity
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 20 Monitoring the Syrian War with Natural Language Processing
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 21 The Proliferation of Misinformation Online
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 22 Unlocking the Potential of AI with Open Data
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Part IV Health
Chapter 23 Detecting Middle Ear Disease
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 24 Detecting Leprosy in Vulnerable Populations
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 25 Automated Segmentation of Prostate Cancer Metastases
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 26 Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings
Executive Summary
Why Is This Important?
Methods Used
Retinal Image Selector
ROP Classifier and Model Calibration
Mobile ROP Application Development
Findings
Discussion
What We Learned
Chapter 27 Long-Term Effects of COVID-19.
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 28 Using Artificial Intelligence to Inform Pancreatic Cyst Management
Executive Summary
Why Is This Important?
Methods Used
Findings
Discussion
What We Learned
Chapter 29 NLP-Supported Chatbot for Cigarette Smoking Cessation
Executive Summary
Why Is This Important?
Methods Used
Findings
Final Version of QuitBot
Quit Efficacy Randomized Controlled Trial
Discussion
What We Learned
Chapter 30 Mapping Population Movement Using Satellite Imagery
Executive Summary
Why Is This Important?
Methods Used
Geographic Focus
Building Density Estimated from Remote Sensing Data
Estimating People per Structure
Findings
Discussion
What We Learned
Chapter 31 The Promise of AI and Generative Pre-Trained Transformer Models in Medicine
What Are GPT Models and What Do They Do?
GPT Models in Medicine
Radiology
Patient Self-Care Management and Informed Decision-Making
Public Health
Conclusion
Part V Summary, Looking Forward, and Additional Resources
Epilogue: Getting Good at AI for Good
Communication
Setting Realistic Expectations for AI
Confronting Technical Limitations
Project Scoping and Implementation
Data
Adapting to Previously Collected Datasets
Creating Training and Test Sets with the Application Scenario in Mind
Modeling
Incorporating Domain Expertise
Model Development with Resource Constraints
Evaluation and Metrics
Humans in the Loop
Impact
Uphill Path to Deployment and Adoption
Measuring Impact
Conclusion
Key Takeaways
AI and Satellites: Critical Tools to Help Us with Planetary Emergencies
Amazing Things in the Amazon
Quick Help Saving Lives in Disaster Response
Additional Resources.
Endnotes
Acknowledgments
About the Editors
About the Authors
Microsoft's AI for Good Lab
Collaborators
Index
EULA.
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