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.
22 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.
Benchmark
A standard test used to compare AI systems — indispensable for progress, and routinely mistaken for a measure of the thing it approximates.
Scaling Laws
The finding that model performance improves predictably with size, data and compute — the empirical result that justified spending billions, and it isn't a law.
Benchmark Contamination
When the test is in the training data — the problem that makes most published model scores impossible to fully trust.
Markov Decision Process
The formal frame underneath all of reinforcement learning — built on an assumption that's almost always false, and it works anyway.
Reward Function
The number that tells an agent what you want — and the hardest thing to write correctly in all of AI.
Q-Learning
Learning the value of every action in every state, by bootstrapping off your own estimates — which converges beautifully in theory and diverges in practice.
Policy Gradient
Learning the behaviour directly instead of learning values — the method behind RLHF, and its entire difficulty is variance.
PPO
The policy gradient method that trains language models — and a careful study found its gains came from the implementation details, not the idea in the paper.
Exploration vs Exploitation
Take the best thing you know, or look for something better — the trade-off underneath every learning system, with a known optimal answer that almost nobody uses.
Turing Test
The 1950 proposal that a machine should count as thinking if it can pass for human in conversation — a test of deception, which Turing said plainly and everyone forgot.
Symbolic AI
The idea that intelligence is symbol manipulation, and you build it by writing down what you know — the paradigm that ruled AI for thirty years and lost.
Expert System
Encoding a specialist's knowledge as rules — AI's first commercial success, and its collapse taught the field something it's currently relearning.
AI Winter
The periods when AI's promises outran its results and the money left — twice, and the question of whether the pattern is over is genuinely open.
Search Algorithm
Systematically exploring possibilities to find a good one — AI's oldest technique, its most complete success, and nobody calls it AI anymore.
Emergence
Abilities that appear suddenly at scale rather than improving gradually — the most cited claim about large models, and a NeurIPS best paper says it's a measurement artefact.