AI for Good
John Wiley & Sons Inc (Verlag)
978-1-394-23587-2 (ISBN)
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.
JUAN M. LAVISTA FERRES, PHD, MS, is the Microsoft Chief Data Scientist and the Director of the AI for Good Lab at Microsoft. WILLIAM B. WEEKS, MD, PHD, MBA, is the Director of AI for Health at Microsoft.
Foreword xix
Brad Smith, Vice Chair and President of Microsoft
Introduction xxiii
William B. Weeks, MD, PhD, MBA
A Call to Action xxvi
Juan M. Lavista Ferres
Part I: Primer on Artificial Intelligence and Machine Learning 1
Chapter 1: What Is Artificial Intelligence and How Can It Be Used for Good? 3
William B. Weeks
What Is Artificial Intelligence? 5
What If Artificial Intelligence Were Used to Improve Societal Good? 6
Chapter 2: Artificial Intelligence: Its Application and Limitations 9
Juan M. Lavista Ferres
Why Now? 11
The Challenges and Lessons Learned from Using Artificial Intelligence 13
Large Language Models 24
Chapter 3: Commonly Used Processes and Terms 33
William B. Weeks and Juan M. Lavista Ferres
Common Processes 33
Commonly Used Measures 35
The Structure of the Book 37
Part II: Sustainability 39
Chapter 4: Deep Learning with Geospatial Data 41
Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Amrita Gupta, Girmaw Adebe Tadesse, Akram Zaytar, and Gilles Quentin Hacheme
Executive Summary 41
Why Is This Important? 42
Methods Used 43
Findings 44
Discussion 46
What We Learned 46
Chapter 5: Nature-Dependent Tourism 48
Darren Tanner and Mark Spalding
Executive Summary 48
Why Is This Important? 49
Methods Used 50
Findings 52
Discussion 52
What We Learned 55
Chapter 6: Wildlife Bioacoustics Detection 57
Zhongqi Miao
Executive Summary 57
Why Is This Important? 58
Methods Used 59
Findings 61
Discussion 64
What We Learned 65
Chapter 7: Using Satellites to Monitor Whales from Space 66
Caleb Robinson, Kim Goetz, and Christin Khan
Executive Summary 66
Why Is This Important? 67
Methods Used 67
Findings 69
Discussion 70
What We Learned 71
Chapter 8: Social Networks of Giraffes 73
Juan M. Lavista Ferres, Derek Lee, and Monica Bond
Executive Summary 73
Why Is This Important? 75
Methods Used 78
Findings 79
Discussion 84
What We Learned 86
Chapter 9: Data-driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara 88
Akram Zaytar, Gilles Hacheme, Girmaw Abebe Tadesse, Caleb Robinson, Rahul Dodhia, and Juan M. Lavista Ferres
Executive Summary 88
Why Is This Important? 90
Methods Used 90
Findings 92
Discussion 94
What We Learned 96
Chapter 10: Mapping Industrial Poultry Operations at Scale 97
Caleb Robinson and Daniel Ho
Executive Summary 97
Why Is This Important? 98
Methods Used 98
Findings 100
Discussion 102
What We Learned 104
Chapter 11: Identifying Solar Energy Locations in India 105
Anthony Ortiz and Joseph Kiesecker
Executive Summary 105
Why Is This Important? 106
Methods Used 107
Findings 109
Discussion 110
What We Learned 111
Chapter 12: Mapping Glacial Lakes 113
Anthony Ortiz, Kris Sankaran, Finu Shrestha, Tenzing Chogyal Sherpa, and Mir Matin
Executive Summary 113
Why Is This Important? 114
Methods Used 115
Findings 117
Discussion 120
What We Learned 123
Chapter 13: Forecasting and Explaining Degradation of Solar Panels with AI 124
Felipe Oviedo and Tonio Buonassisi
Executive Summary 124
Why Is This Important? 125
Methods Used 126
Findings 128
Discussion 131
What We Learned 132
Part III: Humanitarian Action 133
Chapter 14: Post-Disaster Building Damage Assessment 135
Shahrzad Gholami
Executive Summary 135
Why Is This Important? 136
Methods Used 137
Findings 140
Discussion 143
What We Learned 144
Chapter 15: Dwelling Type Classification 146
Md Nasir and Anshu Sharma
Executive Summary 146
Why Is This Important? 147
Methods Used 148
Findings 149
Discussion 151
What We Learned 153
Chapter 16: Damage Assessment Following the 2023 Earthquake in Turkey 155
Caleb Robinson, Simone Fobi, and Anthony Ortiz
Executive Summary 155
Why Is This Important? 156
Methods Used 157
Findings 159
Discussion 162
What We Learned 162
Chapter 17: Food Security Analysis 164
Shahrzad Gholami, Erwin w. Knippenberg, and James Campbell
Executive Summary 164
Why Is This Important? 165
Methods Used 166
Findings 171
Discussion 175
What We Learned 177
Chapter 18: BankNote-Net: Open Dataset for Assistive Universal Currency Recognition 178
Felipe Oviedo and Saqib Shaikh
Executive Summary 178
Why Is This Important? 179
Methods Used 180
Findings 182
Discussion 185
What We Learned 186
Chapter 19: Broadband Connectivity 187
Mayana Pereira, Amit Misra, and Allen Kim
Executive Summary 187
Why Is This Important? 188
Methods Used 189
Findings 190
Discussion 192
What We Learned 193
Chapter 20: Monitoring the Syrian War with Natural Language Processing 194
Rahul Dodhia and Michael Scholtens
Executive Summary 194
Why Is This Important? 195
Methods Used 197
Findings 198
Discussion 200
What We Learned 200
Chapter 21: The Proliferation of Misinformation Online 202
Will Fein, Mayana Pereira, Jane Wang, Kevin Greene, Lucas Meyer, Rahul Dodhia, and Jacob Shapiro
Executive Summary 202
Why Is This Important? 203
Methods Used 204
Findings 208
Discussion 210
What We Learned 211
Chapter 22: Unlocking the Potential of AI with Open Data 213
Anthony Cintron Roman and Kevin Xu
Executive Summary 213
Why Is This Important? 214
Methods Used 215
Findings 216
Discussion 219
What We Learned 220
Part IV: Health 222
Chapter 23: Detecting Middle Ear Disease 225
Yixi Xu and Al-Rahim Habib
Executive Summary 225
Why Is This Important? 226
Methods Used 227
Findings 230
Discussion 232
What We Learned 233
Chapter 24: Detecting Leprosy in Vulnerable Populations 235
Yixi Xu and Ann Aerts
Executive Summary 235
Why Is This Important? 236
Methods Used 237
Findings 238
Discussion 239
What We Learned 240
Chapter 25: Automated Segmentation of Prostate Cancer Metastases 241
Yixi Xu
Executive Summary 241
Why Is This Important? 242
Methods Used 243
Findings 245
Discussion 249
What We Learned 250
Chapter 26: Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings 252
Anthony Ortiz, Juan M. Lavista Ferres, Guillermo Monteoliva, and Maria Ana Martinez-Castellanos
Executive Summary 252
Why Is This Important? 253
Methods Used 255
Findings 259
Discussion 260
What We Learned 262
Chapter 27: Long-Term Effects of COVID-19 264
Meghana Kshirsagar and Sumit Mukherjee
Executive Summary 264
Why Is This Important? 265
Methods Used 267
Findings 269
Discussion 274
What We Learned 275
Chapter 28: Using Artificial Intelligence to Inform Pancreatic Cyst Management 277
Juan M. Lavista Ferres, Felipe Oviedo, William B. Weeks, Elliot Fishman, and Anne Marie Lennon
Executive Summary 277
Why Is This Important? 278
Methods Used 279
Findings 281
Discussion 283
What We Learned 285
Chapter 29: NLP-Supported Chatbot for Cigarette Smoking Cessation 287
Jonathan B. Bricker, Brie Sullivan, Marci Strong, Anusua Trivedi, Thomas Roca, James Jacoby, Margarita Santiago-Torres, and Juan M. Lavista Ferres
Executive Summary 287
Why Is This Important? 289
Methods Used 291
Findings 294
Discussion 299
What We Learned 301
Chapter 30: Mapping Population Movement Using Satellite Imagery 303
Tammy Glazer, Gilles Hacheme, Amy Michaels, and Christopher J.L. Murray
Executive Summary 303
Why Is This Important? 304
Methods Used 306
Findings 312
Discussion 315
What We Learned 317
Chapter 31: The Promise of AI and Generative Pre-Trained Transformer Models in Medicine 318
William B. Weeks
What Are GPT Models and What Do They Do? 318
GPT Models in Medicine 319
Conclusion 327
Part V: Summary, Looking Forward, And Additional Resources 329
Epilogue: Getting Good at AI for Good 331
The AI for Good Lab
Communication 332
Data 333
Modeling 335
Impact 337
Conclusion 340
Key Takeaways 340
AI and Satellites: Critical Tools to Help Us with Planetary Emergencies 342
Will Marshall and Andrew Zolli
Amazing Things in the Amazon 344
Quick Help Saving Lives in Disaster Response 346
Additional Resources 348
Lucia Ronchi Darre
Endnotes 351
Acknowledgments 353
About the Editors 358
About the Authors 361
Microsoft’s AI for Good Lab 361
Collaborators 369
Index 382
| Erscheinungsdatum | 03.04.2024 |
|---|---|
| Vorwort | Brad Smith |
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 163 x 231 mm |
| Gewicht | 658 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Gesundheitswesen | |
| Technik | |
| ISBN-10 | 1-394-23587-9 / 1394235879 |
| ISBN-13 | 978-1-394-23587-2 / 9781394235872 |
| Zustand | Neuware |
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