Details

AI for Good

Applications in Sustainability, Humanitarian Action, and Health
1. Aufl.

von: Juan M. Lavista Ferres, William B. Weeks, Brad Smith
19,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	23.01.2024
ISBN/EAN:	9781394235896
Sprache:	englisch
Anzahl Seiten:	432

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Titelbeschreibung

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: <ul> <li>Easy-to-follow, non-technical explanations of what AI is and how it works</li> <li>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</li> <li>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</li> <li>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</li> <li>Discussions of the future of AI in the realm of social benefit organizations and efforts</li> </ul> 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.

Inhaltsverzeichnis

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

Autorenportrait

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.

Back cover copy

SEE HOW MICROSOFT’S AI FOR GOOD LAB IS TRANSFORMING THE WORLD FOR THE BETTER WITH AI AI for Good: Applications in Sustainability, Humanitarian Action, and Health is an incisive and fascinating new look at how professionals at the Microsoft AI for Good Lab are tackling age-old, global problems with cutting edge new tech. Authors Juan Lavista Ferres and Bill Weeks discuss some of the existential problems of our era—including illness and disease, climate change, sustainability, emergency preparedness and response, inaccessibility, and disinformation—and show you replicable methods for tackling these issues. In the book, you’ll learn what AI is and how it works before you dive into real-world applications of the technology. You’ll also examine where the future is taking us, as the authors discuss the possible and likely applications of artificial intelligence to tomorrow’s problems. Finally, the book offers replicable methods that end-users can employ as they address social problems themselves. A can’t-miss roadmap to impactful social change with AI, AI for Good is an indispensable resource for both technical and non-technical professionals, policymakers, regulators, professionals working at non-governmental organizations, and nonprofit directors and volunteers.