Shannon Entropy Defines Information, Cross-entropy Trains All LLMs
techNmak · x · 2026-08-21
Reviewing Claude Shannon's foundational contributions to the digital age and AI:
- Boolean Algebra & Circuits: His thesis proved the mathematical identity of Boolean algebra and electrical circuits, establishing the logic base for digital computers.
- Perfect Secrecy: Proved the one-time pad provides unbreakable encryption, laying the groundwork for modern cryptography.
- Information Theory: The 1948 paper introduced Shannon Entropy $H = -\Sigma p(x) \log p(x)$, defining the "bit" and channel capacity.
In AI Today: Shannon entropy directly derives Cross-entropy loss (the training function for all classifiers and LLMs), information gain in decision trees, and Perplexity for LLM evaluation. Every neural network training runs Shannon's formula.
Related event: Claude Shannon: The Father of Information Theory Behind the AI Era(2 posts)→
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