You're onboarding a new team member who's fresh into the AI space, and they're asking you to explain how AI, Machine Learning, and Deep Learning relate to each other. Do they nest inside each other? Are they the same thing? How do you diagram this so they get it? Which statement best captures the hierarchy?

AI is the overarching field. It's the dream: machines that can perform tasks humans do, understand language, play chess, recognize faces, whatever. For decades, AI researchers built rule-based systems — feed the computer rules, and it follows them. That worked for simple problems but didn't scale. Enter Machine Learning: instead of hand-coding rules, you give the system data and let it learn the patterns. "Here are 10,000 labeled images of cats and dogs; learn to tell them apart." ML is a method of achieving AI. Then Deep Learning came along — a subset of ML that uses artificial neural networks with multiple layers (hence "deep"). Neural networks are inspired by how brains work, and the deeper the network, the more complex patterns it can learn. Deep Learning crushed benchmarks in computer vision, natural language processing, speech recognition. Every large language model is a form of Deep Learning. So: AI is the domain and the goal. Machine Learning is a key technique for achieving AI. Deep Learning is a powerful subset of ML. You can have AI without Machine Learning (rule-based systems), you can have Machine Learning without Deep Learning (random forests, SVM, decision trees), but modern AI is increasingly powered by Deep Learning.