Additional Resources

—Lucia Ronchi Darre

We began this book by defining artificial intelligence as a machine's ability to learn. So, it is only fair that in closing this book, we offer our human readers the same opportunity to learn by directing them to additional resources.

When it comes to learning more about AI and its potential for positive real-world impact, a beginner-friendly starting point is the AI for Good Specialization1 developed by DeepLearning.AI in partnership with our research lab. This specialization comprises three courses that explore how AI can address challenges in critical areas like public health, climate change, and disaster management. These courses provide a step-by-step framework for developing AI projects, present real-world case studies, and offer ample opportunities for practicing data analysis and AI modeling. They also delve into the limitations, concerns, and ethical questions surrounding AI—equally important, if not more so, than its potential for good.

Our readers might find this course particularly relevant as it instructs on common AI tools—such as topic modeling, computer vision, time series, and neural networks—applied to problem spaces akin to those discussed in this book. For instance, in the AI and Disaster Management course, readers can construct an image classification pipeline for damage assessment after a natural disaster, like the projects detailed in Chapters 14, “Post-Disaster Building Damage Assessment,” and 16, “Damage Assessment ...

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