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The AI Explosion
The theory behind modern AI: the universal approximation theorem and its limits, why depth exploits compositional structure far more efficiently than width, how networks untangle data into linearly separable representations, and why representational capacity alone was never enough without data.
3 articles
~33 min total
Machine LearningAIDeep LearningTheorySelf-Supervised LearningRepresentation Learning
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The AI Explosion, Part One: Why Neural Networks Are Such Powerful Learners
The theory behind modern AI: the universal approximation theorem and its limits, why depth exploits compositional structure far more efficiently than width, how networks untangle data into linearly separable representations, and why representational capacity alone was never enough without data.
Jul 17, 202611 min read
Machine LearningDeep LearningTheoryAI