AI Mathematically Explained
AI industry claims vs. mathematical reality
What This Is About This repository proves that modern AI systems are classical mathematics in disguise. Examples:
RAG = Bayes' theorem (1763) Transformers = Kernel methods + attention Neural training = Gradient descent on loss landscapes Embeddings = Matrix factorization techniques
Each "breakthrough" gets a mathematical proof showing its classical origins.
Structure
/rag-bayesian/ # RAG = Bayes' theorem
/transformer-kernels/ # Attention = kernel methods
/neural-optimization/ # Training = optimization theory
/embedding-factorization/ # Word vectors = matrix math
/utils/ # Mathematical tools
Quick Start
bashgit clone https://github.com/MLDreamer/AIMathematicallyexplained
cd ai-mathematically-explained
pip install -r requirements.txt
python examples/rag_demo.py
