Foundations

Understanding AGI requires a strong foundation in mathematics, computer science, and cognitive theory. This section builds the theoretical groundwork necessary for all subsequent topics.

What You'll Learn

• Mathematical prerequisites for AGI research
• Core computational theories and models
• Knowledge representation fundamentals
• Learning theory and optimization
• Cognitive architectures
• Information theory
• Logic systems and inference

Prerequisites

Basic understanding of:

• Linear algebra
• Calculus
• Probability and statistics
• Data structures and algorithms
• Programming (Python recommended)

Learning Path

1. Mathematical Foundations - Linear algebra, calculus, optimization theory
2. Computational Models - Turing machines, lambda calculus, complexity theory
3. Knowledge Representation - Ontologies, semantic networks, graphs
4. Learning Theory - Statistical learning, PAC learning, deep learning basics
5. Logic and Reasoning - First-order logic, modal logic, probabilistic reasoning
6. Cognitive Architectures - SOAR, ACT-R, symbolic vs subsymbolic systems
7. Information Theory - Entropy, mutual information, complexity measures
8. Search and Planning - Heuristic search, planning algorithms, optimization

Each topic provides both theoretical understanding and practical applications relevant to AGI development.

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Next: Mathematical Foundations