The AI University Map of AI

A complete, visual, cross-linked body of knowledge covering every area of artificial intelligence — from foundations to frontier. Free, and growing.

This is an ontology of AI: the whole field organised into connected areas you can explore. Start with the map, then follow the links. Practitioner topics feed directly into our courses.

The whole field at a glance

flowchart TB
  AI([Artificial Intelligence]):::root
  subgraph Learn [Learning paradigms]
    ML[Machine Learning]
    DL[Deep Learning]
    RL[Reinforcement Learning]
    CAU[Causal Inference]
  end
  subgraph Mod [Modalities & generation]
    NLP[NLP & LLMs]
    CV[Computer Vision]
    SP[Speech & Audio]
    GEN[Generative AI]
    MM[Multimodal AI]
  end
  subgraph Sys [Systems & reasoning]
    AG[Agents & Autonomy]
    RO[Robotics]
    KR[Knowledge & Reasoning]
    IR[Retrieval & Search]
    REC[Recommenders]
    TS[Time Series]
  end
  subgraph Infra [Compute & practice]
    DO[Data & MLOps]
    HW[Hardware & Compute]
    EDGE[Edge AI]
    EVAL[Evaluation]
  end
  subgraph Resp [Responsible AI]
    SAF[Safety & Alignment]
    ETH[Ethics & Governance]
    INT[Interpretability]
    PRIV[Privacy & Security]
  end
  AI --> FOUND[Foundations]
  AI --> Learn
  AI --> Mod
  AI --> Sys
  AI --> Infra
  AI --> Resp
  AI --> SCI[AI for Science]
  AI --> APP[Applications]
  AI --> TOOL[Tools & Ecosystem]
  AI --> BUILD[Building with AI]
  classDef root fill:#4f46e5,color:#fff,stroke:#3730a3,stroke-width:2px;

Explore every area

• Foundations of AI

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

• Machine Learning

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  Algorithms that improve at a task by learning patterns from data instead of being explicitly programmed.

• Deep Learning

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  Machine learning with many-layered neural networks that learn representations directly from raw data.

• NLP & Large Language Models

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  Getting machines to understand and generate human language — now dominated by large language models.

• Computer Vision

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  Teaching machines to interpret images and video — from recognition to generation.

• Generative AI

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  Models that create new content — text, images, audio, video, and code — rather than only classifying it.

• Reinforcement Learning

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  Learning to act by maximizing cumulative reward through interaction with an environment.

• AI Agents & Autonomy

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  Systems that plan and take actions toward goals — using tools, memory, and (often) other agents.

• Robotics & Embodied AI

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  AI that senses and acts in the physical world through bodies — robots, drones, and vehicles.

• Speech & Audio AI

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  Understanding and generating sound — speech, music, and everything in between.

• Knowledge & Reasoning

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  Representing knowledge explicitly and reasoning over it — the symbolic tradition and its fusion with learning.

• Data & MLOps

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  The engineering that turns models into reliable products — data pipelines, deployment, and monitoring.

• AI Safety, Alignment & Ethics

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  Making AI systems reliable, fair, and aligned with human values — and governing their use.

• Applications & Industry

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  Where AI creates value — a tour of the fields being reshaped by it.

• Tools & Ecosystem

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  The frameworks, platforms, hardware, and benchmarks practitioners actually use.

• Building with AI

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  The practitioner track — how to actually build useful AI products. This is where our courses go deep.

• Multimodal AI

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  Models that perceive and reason across more than one kind of data at once — text, images, audio, and video together.

• Recommender Systems

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  The AI behind personalization — deciding what to show, suggest, or rank for each user. The quiet workhorse of the internet.

• AI Hardware & Compute

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  The silicon and systems that make modern AI possible — and the single biggest practical constraint on what gets built.

• Evaluation & Benchmarks

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  How we measure whether AI actually works — the science, and real difficulty, of knowing if a model is any good.

• Interpretability & Explainability

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  Opening the black box — understanding why a model made a prediction, and what it has actually learned inside.

• Privacy & Security in AI

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  Protecting data and defending models — training on sensitive data safely, and keeping AI systems robust against attack.

• AI Ethics & Governance

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  The societal side of AI — fairness, accountability, and the laws and norms now shaping how AI can be built and used.

• AI for Science

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  AI as a scientific instrument — accelerating discovery in biology, chemistry, physics, mathematics, and beyond.

• Causal Inference

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  Moving beyond correlation to cause — the tools for asking 'what if?' and 'why?', not just 'what is likely?'.

• Time Series & Forecasting

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  Learning from data that unfolds over time — predicting the future and spotting the unusual.

• Information Retrieval & Search

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  Finding the right information in huge collections — the foundation of search engines and of retrieval-augmented generation.

• Edge & On-Device AI

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  Running AI where the data is — on phones, sensors, and microcontrollers — without a round trip to the cloud.

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Contribute & follow along

New areas and deep-dives are added continually. Published by AI University.