From Mirror to Mind, From Neurons to MindReading - AI Isn't Magic, It's The Math They Don't Show You

This exploration details the underlying mathematical principles of Artificial Intelligence, contrasting historical symbolic approaches with modern connectionist models. The presentation aims to demystify complex AI systems, such as large language models, by focusing on the algorithms that minimize error through mathematical rules rather than simple imitation. It examines the progression from basic artificial neurons to sophisticated, trillion-parameter models, referencing concepts like the "Blind Hiker" algorithm powering transformer architectures.

The content focuses on the fundamental anatomy of modern Artificial Intelligence, explaining the mathematical framework behind how these systems learn and operate. It touches upon significant developments in neural networks, including the mechanics of backpropagation and gradient descent, while also addressing potential future limitations posed by data availability, referred to as the "Data Wall."