From humanitarian and environmental concerns to advances in art and science, every area of life stands poised to make a quantum leap into the future. The problem? Too few of us really understand how AI works and how to integrate it into our policies and projects. In this book, Rahul Dodhia, Deputy Director of Microsoft's AI for Good Research Lab, offers a nontechnical exploration of artificial intelligence tools--how they're built, what they can and can't do, and the raw material that teaches them what they "know." Readers will also find an inventory of common challenges they might face when integrating AI into their work. You'll also read more on: - The potential for AI to solve longstanding issues and improve lives - Learn how you can tap into the power of AI, regardless of the size of your organization - Gain an understanding of how AI works and how to communicate with AI scientists to create new solutions - Understand the real risks of implementing AI and how to avoid potential pitfalls

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Cover -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- About the Author -- Introduction -- Chapter 1 A Brief History of Artificial Intelligence -- How Innovators Throughout History Paved the Way for Modern AI: From Babbage to Turing -- Charles Babbage -- Ada Lovelace -- John von Neumann -- Alan Turing -- The Emergence of Modern AI -- The Dartmouth Conference: A Turning Point -- From Optimism to Pessimism: The Story of the AI Winter -- The Rise of Expert Systems -- AI Revival: A Fitful Resurgence -- The Birth of Modern AI -- AI Today -- Driver of the 21st Century Economy -- Final Thoughts -- References -- Chapter 2 AI Explained: A Non-Technical Guide -- Definition of AI -- Machine Learning -- How Machines Learn -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Neural Networks -- Structure of a Neuron -- Layers in a Neural Network -- Training a Neural Network -- Backpropagation: Learning from Mistakes -- Beyond Backpropagation -- Common Deep Learning Models -- Convolutional Neural Networks (CNN) -- Recurrent Neural Networks (RNN) -- Transformers -- Foundational Models -- Handle with Caution -- Vision and Sound -- Explainable AI -- Final Thoughts -- References -- Chapter 3 AI for Good -- Responding to Natural Disasters -- Floods -- SEEDS -- Earthquake in Syria and Turkey -- Role of AI in Disaster Resiliency -- Data for Environment Monitoring -- Food and Water Security -- Emerging Uses of AI to Combat Food and Water Insecurity -- Medicine -- Medical Imaging -- Pathology -- Education -- Final Thoughts -- References -- Chapter 4 AI for Good: Pursuit of Scientific Knowledge -- Biodiversity -- Beluga Whales -- Rainforests -- Satellites -- Bioacoustics -- Camera Traps -- Proteomics -- Astronomy -- Final Thoughts -- References -- Chapter 5 When Good AI Goes Bad -- The Surveillance Society. The Dual Nature of AI Drones: Saving Lives or Restricting Freedom? -- The Quandary of Facial Recognition -- Predictive Policing: Safeguarding Society or Violating Rights? -- Surveillance Capitalism -- Magnifying Societal Ills -- Deepfakes and Disinformation -- Image Deepfakes -- Automated Misinformation -- Checks on Disinformation -- Evolution of Cybercrime -- Amplifying Discrimination and Social Biases -- Gender Bias -- Racial Bias -- Economic Bias -- Final Thoughts -- References -- Chapter 6 Putting Safeguards Around AI -- The Need for Ethical Development -- Safety and Security -- Accountability and Transparency -- Data Protection and Privacy -- Balancing Innovation and Regulation -- Economic and Social Impact -- Teachers and Educators -- Healthcare Professionals -- Office Workers -- Manufacturers and Factory Workers -- Transportation -- Farmers -- Creative Professionals -- AI Governance -- Military Weapons -- Finance -- Final Thoughts -- References -- Chapter 7 Getting the Best Out of Your AI Team -- Roles in an AI Team -- A Three-Way Conversation -- The Project Manager -- The Domain Expert -- The AI Expert -- Setting Expectations About AI -- Case Study: Breast Cancer Example -- Project Scoping -- The Reality of Running AI: Cost, Connectivity, and Context -- Understanding the Role of Environmental Context in AI Deployment -- Technology Resources -- Data: Quantity and Quality, Annotations, Biases -- Type of Data: Format and Relevance -- Quality of Data -- Quantity of Data -- Real-World Examples -- Modeling -- Accuracy -- Accuracy: Balancing Precision and Sensitivity -- Human-in-the-loop -- Consequences for Wrong Outcomes Are More Severe -- Final Thoughts -- References -- Chapter 8 The Future -- New Technologies -- Quantum Computing -- DNA Storage -- AI Teams in the Near Future -- AI-Specific Jobs -- Societal Change -- Final Thoughts -- References. Index -- EULA.