AI for good : applications in sustainability, humanitarian action, and health

AI for good applications in sustainability, humanitarian action, and health

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...

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Detalles Bibliográficos
Otros Autores: Lavista Ferres, Juan M., editor (editor), Weeks, William B., editor (-), Smith, Brad
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Artificial intelligence > Computer programs.
Artificial intelligence > Medical applications.
Artificial intelligence > Biological applications.
Artificial intelligence > Social aspects.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811331606719
Tabla de Contenidos:
  • 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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