Field
Foundations
The words underneath everything else.
This field defines the terms the rest of the site assumes. It's also where the honest answers are most uncomfortable.
Intelligence has no agreed definition — over seventy published ones and counting — which is why arguments about whether AI is "really" intelligent never resolve. Artificial intelligence is defined by its frontier, so the definition moves every time the machines succeed: chess was the benchmark until a computer won, then it was "just search". AGI is a hypothetical nobody can measure, and seventy years of predictions about it have been wrong in the same direction.
The practical entries are here too: the difference between training and inference — which sets your entire cost structure — and the three ways machines learn (machine learning, deep learning, reinforcement learning).
Start with Artificial Intelligence, then Intelligence if you want to know why nobody can define it.
7 concepts in this field
Machine Learning
Getting computers to learn patterns from data and improve at a task, instead of being explicitly programmed with rules.
Deep Learning
Machine learning using neural networks with many layers — the approach behind nearly every recent AI breakthrough.
Reinforcement Learning
Learning by trial and error through rewards — the way you'd train a pet, applied to software.
Artificial Intelligence
The field of making machines do things that seem to require intelligence — a definition that has moved every time the machines succeed.
AGI (Artificial General Intelligence)
A hypothetical system with broad human-level capability across domains — undefined enough that people can argue about whether it's arrived.
Training vs Inference
Building the model versus using it — two completely different activities with different costs, hardware, and constraints.
Intelligence
The word underneath "artificial intelligence" — used constantly, defined by nobody, and the reason the field's biggest arguments never resolve.