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Foundations of AI

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

Foundations of AI is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  AI([Artificial Intelligence]):::a --> ML[Machine Learning]:::b --> DL[Deep Learning]:::c --> FM[Foundation Models]:::d
  classDef a fill:#4f46e5,color:#fff,stroke:#3730a3;
  classDef b fill:#6366f1,color:#fff,stroke:#4338ca;
  classDef c fill:#818cf8,color:#111,stroke:#4338ca;
  classDef d fill:#c7d2fe,color:#111,stroke:#6366f1;
timeline
  title A brief history of AI
  1950 : Turing test
  1956 : Dartmouth workshop coins 'AI'
  1980s : Expert systems boom
  1997 : Deep Blue beats Kasparov
  2012 : AlexNet — deep learning takes off
  2017 : Transformers ('Attention is All You Need')
  2022 : Generative AI goes mainstream

Key topics

  • 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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