How to Use the Map
The map rewards exploration. A few interactions you might miss:
- Click a node's name or circle to expand or collapse its branch.
- Hover over any node to see its definition and model examples.
- Click the hover panel to copy the definition with an academic reference already attached - perfect for notes and citations.
Stay Updated
Follow the evolution of the AI Knowledge Map. New concepts, tools, and frameworks are added regularly:
Feedback
Found an inaccuracy? Have a concept worth adding? Suggestions and contributions are warmly welcomed - this map grows stronger with the AI community behind it.
Roadmap
What's coming next:
- Full-text search across all concepts
- Glossary view as alphabetical fallback
- Evaluation metrics deep-dives - dedicated explanations for each model evaluation metric, covering when to use it, common pitfalls, and how to interpret it in real scenarios
- Hands-on Google Colab notebooks - simple, ready-to-run training examples for the most common model types, so you can experiment with the concepts you just learned about
About the Project
The AI Knowledge Map is a curated, openly accessible visualization of the artificial intelligence landscape - from foundational concepts to tools, ethics, and governance.
The project began after my postgraduate studies in Data Science and Artificial Intelligence at Universidade Federal do Espírito Santo (UFES), motivated by a simple observation: the field has plenty of textbooks and plenty of marketing material, but few resources sit comfortably between the two. This map aims for that middle ground.
Definitions in AI vary across authors and schools of thought, so I deliberately favored broader, more generalist descriptions over rigid ones. The goal is clarity for newcomers without misleading specialists.
A few concepts were also reorganized to make navigation more intuitive - for instance, the relationship between Deep Learning and Neural Networks. These rearrangements are pedagogical: they preserve the underlying theory while improving how the knowledge is explored visually.
Finally, a heartfelt thanks to OSINT Framework - for the inspiration, and for the open-source foundation that helped get this project off the ground 😉.