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Foundations of AI

What artificial intelligence is, where it came from, and the ideas every other area builds on.

Artificial intelligence is the effort to build machines that can do things we usually think need a mind — recognising a face, understanding a sentence, planning a route, or learning from mistakes. For most of its history, people tried to hand-write every rule a machine should follow. Today the winning idea is different: instead of being told the rules, a system is shown thousands of examples and works the rules out for itself. Think of how you learned to recognise dogs — nobody gave you a checklist of dog features; you just saw many dogs until the pattern clicked. That is machine learning, the engine behind most modern AI. Everything else in this map — vision, language, robots, safety — builds on this one shift from programming rules to learning from examples.

The main ideas

  • What is AI — The study and engineering of systems that perform tasks we associate with intelligence — perception, reasoning, learning, and action.
  • AI vs ML vs Deep Learning — AI is the broad goal; machine learning is the dominant approach (learning from data); deep learning is ML with many-layered neural networks.
  • History of AI — From the 1956 Dartmouth workshop through symbolic AI, expert systems, the AI winters, the deep-learning boom, to today's foundation models.
  • Intelligence & agency — Agents perceive an environment and act to achieve goals; rationality, autonomy, and the debate over what 'understanding' means.
  • AGI, ASI & the Turing test — Narrow AI (today) vs artificial general intelligence vs superintelligence, and how we (imperfectly) measure progress.
  • Math foundations — Linear algebra, probability & statistics, calculus, optimization, and information theory — the language models are written in.

Machine Learning · Knowledge & Reasoning · AI Safety, Alignment & Ethics

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