AI for good : applications in sustainability, humanitarian action, and health / edited by Juan M. Lavista Ferres, William B. Weeks.

Format:

Book

Author/Creator:

Lavista Ferres, Juan M., editor.

Contributor:

Weeks, William B., editor.

Language:

English

Subjects (All):

Artificial intelligence--Computer programs.

Artificial intelligence.

Artificial intelligence--Medical applications.

Artificial intelligence--Biological applications.

Artificial intelligence--Social aspects.

Physical Description:

1 online resource (432 pages)

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

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.

Contents:

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

Findings.

Discussion

Chapter 6 Wildlife Bioacoustics Detection

Chapter 7 Using Satellites to Monitor Whales from Space

Chapter 8 Social Networks of Giraffes

Chapter 9 Data-driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara

Chapter 10 Mapping Industrial Poultry Operations at Scale

Chapter 11 Identifying Solar Energy Locations in India

Chapter 12 Mapping Glacial Lakes

Chapter 13 Forecasting and Explaining Degradation of Solar Panels with AI

Part III Humanitarian Action

Chapter 14 Post-Disaster Building Damage Assessment

Chapter 15 Dwelling Type Classification

Chapter 16 Damage Assessment Following the 2023 Earthquake in Turkey

Discussion.

What We Learned

Chapter 17 Food Security Analysis

Chapter 18 BankNote-Net: Open Dataset for Assistive Universal Currency Recognition

Chapter 19 Broadband Connectivity

Chapter 20 Monitoring the Syrian War with Natural Language Processing

Chapter 21 The Proliferation of Misinformation Online

Chapter 22 Unlocking the Potential of AI with Open Data

Part IV Health

Chapter 23 Detecting Middle Ear Disease

Chapter 24 Detecting Leprosy in Vulnerable Populations

Chapter 25 Automated Segmentation of Prostate Cancer Metastases

Chapter 26 Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings

Retinal Image Selector

ROP Classifier and Model Calibration

Mobile ROP Application Development

Chapter 27 Long-Term Effects of COVID-19.

Executive Summary

Chapter 28 Using Artificial Intelligence to Inform Pancreatic Cyst Management

Chapter 29 NLP-Supported Chatbot for Cigarette Smoking Cessation

Final Version of QuitBot

Quit Efficacy Randomized Controlled Trial

Chapter 30 Mapping Population Movement Using Satellite Imagery

Geographic Focus

Building Density Estimated from Remote Sensing Data

Estimating People per Structure

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394235889

1394235887

OCLC:

1418977582