Dear friends,
Amidst rising worry about AI harms both realistic (like job loss) and unrealistic (like human extinction), It’s critical to understand AI’s potential to do tremendous good. Our new specialization, AI for Good is designed to empower both technical and nontechnical people to identify, scope, and build impactful AI projects.
In this series of courses, you’ll learn when and how to use AI effectively for positive impact in situations where stakes are high and human lives may hang in the balance. AI for Good presents a practical framework for applying machine learning to socially important projects (and products of any kind). It illustrates this framework with several real-world examples of AI projects that are improving climate change, disaster response, and public health.
AI for Good is designed to be useful whether or not you have coding experience. It does include Python code examples that you can execute and interact with to gain deeper insight into different applications. However, it doesn’t assume previous experience with AI or programming. So please recommend this to your nontechnical friends!
There’s often a huge gap between training a model that does well on a test set and one that actually works on real data and affects real people. This specialization will help you tell the difference, so your projects reach people and better their lives.
AI for Good is taught by Robert Monarch, who has applied AI in public health and disaster response for over 20 years. He has founded AI startups and shipped successful AI products at Amazon, Google, Microsoft, and Apple. He’ll show you how to move your own AI projects through the stages of exploration, design, implementation, and evaluation.
AI is experiencing a time of rapid growth, and the AI community’s role in making sure it does significant good is more important than ever. I hope you’ll check out AI for Good!
Do good,
Andrew
P.S. We also have a new short course: “Understanding and Applying Text Embeddings with Vertex AI,” developed in collaboration with Google Cloud and taught by Nikita Namjoshi and me. Learn the fundamentals of text embeddings — an essential piece of the GenAI developer’s toolkit — and apply them to classification, outlier detection, text clustering, and semantic search. You’ll also learn how to combine text generation and semantic search to build a question-answering system. Please join us!