FOREWORD BY BRAD SMITH, VICE CHAIR AND PRESIDENT OF MICROSOFT<\/p>\n
Discover how AI leaders and researchers are using AI to transform the world for the better<\/p>\n
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.<\/p>\n
The authors also provide:
\n* Easy-to-follow, non-technical explanations of what AI is and how it works
\n* 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
\n* 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
\n* 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
\n* Discussions of the future of AI in the realm of social benefit organizations and efforts
\nBeyond 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.<\/p>\n
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.<\/p>\n
Foreword xix
\nBrad Smith, Vice Chair and President of Microsoft
\nIntroduction xxiii
\nWilliam B. Weeks, MD, PhD, MBA
\nA Call to Action xxvi
\nJuan M. Lavista Ferres
\nPart I: Primer on Artificial Intelligence and Machine Learning 1
\nChapter 1: What Is Artificial Intelligence and How Can It Be Used for Good? 3
\nWilliam B. Weeks
\nWhat Is Artificial Intelligence? 5
\nWhat If Artificial Intelligence Were Used to Improve Societal Good? 6
\nChapter 2: Artificial Intelligence: Its Application and Limitations 9
\nJuan M. Lavista Ferres
\nWhy Now? 11
\nThe Challenges and Lessons Learned from Using Artificial Intelligence 13
\nLarge Language Models 24
\nChapter 3: Commonly Used Processes and Terms 33
\nWilliam B. Weeks and Juan M. Lavista Ferres
\nCommon Processes 33
\nCommonly Used Measures 35
\nThe Structure of the Book 37
\nPart II: Sustainability 39
\nChapter 4: Deep Learning with Geospatial Data 41
\nCaleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Amrita Gupta, Girmaw Adebe Tadesse, Akram Zaytar, and Gilles Quentin Hacheme
\nExecutive Summary 41
\nWhy Is This Important? 42
\nMethods Used 43
\nFindings 44
\nDiscussion 46
\nWhat We Learned 46
\nChapter 5: Nature-Dependent Tourism 48
\nDarren Tanner and Mark Spalding
\nExecutive Summary 48
\nWhy Is This Important? 49
\nMethods Used 50
\nFindings 52
\nDiscussion 52
\nWhat We Learned 55
\nChapter 6: Wildlife Bioacoustics Detection 57
\nZhongqi Miao
\nExecutive Summary 57
\nWhy Is This Important? 58
\nMethods Used 59
\nFindings 61
\nDiscussion 64
\nWhat We Learned 65
\nChapter 7: Using Satellites to Monitor Whales from Space 66
\nCaleb Robinson, Kim Goetz, and Christin Khan
\nExecutive Summary 66
\nWhy Is This Important? 67
\nMethods Used 67
\nFindings 69
\nDiscussion 70
\nWhat We Learned 71
\nChapter 8: Social Networks of Giraffes 73
\nJuan M. Lavista Ferres, Derek Lee, and Monica Bond
\nExecutive Summary 73
\nWhy Is This Important? 75
\nMethods Used 78
\nFindings 79
\nDiscussion 84
\nWhat We Learned 86
\nChapter 9: Data-driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara 88
\nAkram Zaytar, Gilles Hacheme, Girmaw Abebe Tadesse, Caleb Robinson, Rahul Dodhia, and Juan M. Lavista Ferres
\nExecutive Summary 88
\nWhy Is This Important? 90
\nMethods Used 90
\nFindings 92
\nDiscussion 94
\nWhat We Learned 96
\nChapter 10: Mapping Industrial Poultry Operations at Scale 97
\nCaleb Robinson and Daniel Ho
\nExecutive Summary 97
\nWhy Is This Important? 98
\nMethods Used 98
\nFindings 100
\nDiscussion 102
\nWhat We Learned 104
\nChapter 11: Identifying Solar Energy Locations in India 105
\nAnthony Ortiz and Joseph Kiesecker
\nExecutive Summary 105
\nWhy Is This Important? 106
\nMethods Used 107
\nFindings 109
\nDiscussion 110
\nWhat We Learned 111
\nChapter 12: Mapping Glacial Lakes 113
\nAnthony Ortiz, Kris Sankaran, Finu Shrestha, Tenzing Chogyal Sherpa, and Mir Matin
\nExecutive Summary 113
\nWhy Is This Important? 114
\nMethods Used 115
\nFindings 117
\nDiscussion 120
\nWhat We Learned 123
\nChapter 13: Forecasting and Explaining Degradation of Solar Panels with AI 124
\nFelipe Oviedo and Tonio Buonassisi
\nExecutive Summary 124
\nWhy Is This Important? 125
\nMethods Used 126
\nFindings 128
\nDiscussion 131
\nWhat We Learned 132
\nPart III: Humanitarian Action 133
\nChapter 14: Post-Disaster Building Damage Assessment 135
\nShahrzad Gholami
\nExecutive Summary 135
\nWhy Is This Important? 136
\nMethods Used 137
\nFindings 140
\nDiscussion 143
\nWhat We Learned 144
\nChapter 15: Dwelling Type Classification 146
\nMd Nasir and Anshu Sharma
\nExecutive Summary 146
\nWhy Is This Important? 147
\nMethods Used 148
\nFindings 149
\nDiscussion 151
\nWhat We Learned 153
\nChapter 16: Damage Assessment Following the 2023 Earthquake in Turkey 155
\nCaleb Robinson, Simone Fobi, and Anthony Ortiz
\nExecutive Summary 155
\nWhy Is This Important? 156
\nMethods Used 157
\nFindings 159
\nDiscussion 162
\nWhat We Learned 162
\nChapter 17: Food Security Analysis 164
\nShahrzad Gholami, Erwin w. Knippenberg, and James Campbell
\nExecutive Summary 164
\nWhy Is This Important? 165
\nMethods Used 166
\nFindings 171
\nDiscussion 175
\nWhat We Learned 177
\nChapter 18: BankNote-Net: Open Dataset for Assistive Universal Currency Recognition 178
\nFelipe Oviedo and Saqib Shaikh
\nExecutive Summary 178
\nWhy Is This Important? 179
\nMethods Used 180
\nFindings 182
\nDiscussion 185
\nWhat We Learned 186
\nChapter 19: Broadband Connectivity 187
\nMayana Pereira, Amit Misra, and Allen Kim
\nExecutive Summary 187
\nWhy Is This Important? 188
\nMethods Used 189
\nFindings 190
\nDiscussion 192
\nWhat We Learned 193
\nChapter 20: Monitoring the Syrian War with Natural Language Processing 194
\nRahul Dodhia and Michael Scholtens
\nExecutive Summary 194
\nWhy Is This Important? 195
\nMethods Used 197
\nFindings 198
\nDiscussion 200
\nWhat We Learned 200
\nChapter 21: The Proliferation of Misinformation Online 202
\nWill Fein, Mayana Pereira, Jane Wang, Kevin Greene, Lucas Meyer, Rahul Dodhia, and Jacob Shapiro
\nExecutive Summary 202
\nWhy Is This Important? 203
\nMethods Used 204
\nFindings 208
\nDiscussion 210
\nWhat We Learned 211
\nChapter 22: Unlocking the Potential of AI with Open Data 213
\nAnthony Cintron Roman and Kevin Xu
\nExecutive Summary 213
\nWhy Is This Important? 214
\nMethods Used 215
\nFindings 216
\nDiscussion 219
\nWhat We Learned 220
\nPart IV: Health 222
\nChapter 23: Detecting Middle Ear Disease 225
\nYixi Xu and Al-Rahim Habib
\nExecutive Summary 225
\nWhy Is This Important? 226
\nMethods Used 227
\nFindings 230
\nDiscussion 232
\nWhat We Learned 233
\nChapter 24: Detecting Leprosy in Vulnerable Populations 235
\nYixi Xu and Ann Aerts
\nExecutive Summary 235
\nWhy Is This Important? 236
\nMethods Used 237
\nFindings 238
\nDiscussion 239
\nWhat We Learned 240
\nChapter 25: Automated Segmentation of Prostate Cancer Metastases 241
\nYixi Xu
\nExecutive Summary 241
\nWhy Is This Important? 242
\nMethods Used 243
\nFindings 245
\nDiscussion 249
\nWhat We Learned 250
\nChapter 26: Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings 252
\nAnthony Ortiz, Juan M. Lavista Ferres, Guillermo Monteoliva, and Maria Ana Martinez-Castellanos
\nExecutive Summary 252
\nWhy Is This Important? 253
\nMethods Used 255
\nFindings 259
\nDiscussion 260
\nWhat We Learned 262
\nChapter 27: Long-Term Effects of COVID-19 264
\nMeghana Kshirsagar and Sumit Mukherjee
\nExecutive Summary 264
\nWhy Is This Important? 265
\nMethods Used 267
\nFindings 269
\nDiscussion 274
\nWhat We Learned 275
\nChapter 28: Using Artificial Intelligence to Inform Pancreatic Cyst Management 277
\nJuan M. Lavista Ferres, Felipe Oviedo, William B. Weeks, Elliot Fishman, and Anne Marie Lennon
\nExecutive Summary 277
\nWhy Is This Important? 278
\nMethods Used 279
\nFindings 281
\nDiscussion 283
\nWhat We Learned 285
\nChapter 29: NLP-Supported Chatbot for Cigarette Smoking Cessation 287
\nJonathan B. Bricker, Brie Sullivan, Marci Strong, Anusua Trivedi, Thomas Roca, James Jacoby, Margarita Santiago-Torres, and Juan M. Lavista Ferres
\nExecutive Summary 287
\nWhy Is This Important? 289
\nMethods Used 291
\nFindings 294
\nDiscussion 299
\nWhat We Learned 301
\nChapter 30: Mapping Population Movement Using Satellite Imagery 303
\nTammy Glazer, Gilles Hacheme, Amy Michaels, and Christopher J.L. Murray
\nExecutive Summary 303
\nWhy Is This Important? 304
\nMethods Used 306
\nFindings 312
\nDiscussion 315
\nWhat We Learned 317
\nChapter 31: The Promise of AI and Generative Pre-Trained Transformer Models in Medicine 318
\nWilliam B. Weeks
\nWhat Are GPT Models and What Do They Do? 318
\nGPT Models in Medicine 319
\nConclusion 327
\nPart V: Summary, Looking Forward, And Additional Resources 329
\nEpilogue: Getting Good at AI for Good 331
\nThe AI for Good Lab
\nCommunication 332
\nData 333
\nModeling 335
\nImpact 337
\nConclusion 340
\nKey Takeaways 340
\nAI and Satellites: Critical Tools to Help Us with Planetary Emergencies 342
\nWill Marshall and Andrew Zolli
\nAmazing Things in the Amazon 344
\nQuick Help Saving Lives in Disaster Response 346
\nAdditional Resources 348
\nLucia Ronchi Darre
\nEndnotes 351
\nAcknowledgments 353
\nAbout the Editors 358
\nAbout the Authors 361
\nMicrosoft’s AI for Good Lab 361
\nCollaborators 369
\nIndex 382<\/p>\n","protected":false},"excerpt":{"rendered":"
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