You've covered a lot of ground. Here's what it looks like when it all connects.
Artificial intelligence is the broad project of building systems that can do things that typically require human intelligence. But the version that exists today doesn't reason from principles or draw on lived experience. What it does is recognize patterns, at enormous scale, across enormous amounts of data. That's the engine underneath everything.
Machine learning is how the engine gets built. Instead of following rules a programmer wrote, a machine learning system is trained on examples. It adjusts itself, iteratively, until it gets good at the task. The learning is real, even if it doesn't look like human learning.
Neural networks are the architecture that underlies most of what you'll encounter. They process information through layers, each one building on the last. Deep learning is what happens when those networks go many layers deep, and that depth is what unlocked the capabilities that made AI feel genuinely new: recognizing images, understanding speech, generating language, writing code.
And yet: these systems fail in predictable ways. They hallucinate. They're brittle at the edges of their training. They reflect the biases in the data they learned from. These aren't bugs to be patched; they're structural properties of how the systems work. Knowing that is part of the mental model too.
That's the foundation. It won't answer every question you'll have about AI, but it will make everything else make sense. The sections ahead get more specific: the types of AI you'll actually encounter, how these systems are built and trained, what they're being used for, and what it means to use them well. You're ready.
