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When AI researchers tried to give neural networks 4x the communication capacity, their creation immediately began self-destructing. Signal amplification reached 100,000 times normal levels—the computational equivalent of a nuclear reaction.

The rescue didn't come from cutting-edge innovation. It came from a 1967 mathematical algorithm gathering dust in pure mathematics journals.

This episode chronicles the dramatic crisis in DeepSeek AI's hyperconnection architecture and its elegant solution through doubly stochastic matrices and the Sinkhorn-Knopp algorithm. We explore how constraint enables capability, why pure mathematics developed decades ago solves today's challenges, and what the rescue of a 671-billion-parameter AI model teaches us about the nature of progress.

Why This Matters: The relationship between constraint and freedom applies far beyond AI. This story reveals how the most sophisticated systems we build often need the most classical boundaries—and how solutions to our most pressing challenges may already exist in knowledge developed for entirely different purposes.

References:

mHC: Manifold-Constrained Hyper-Connections

Deep Residual Learning for Image Recognition (ResNet, 2016)

Available for Broadcast on PRX https://exchange.prx.org/pieces/605567-the-mathematics-of-rescue-when-ancient-

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Four recurring narratives underlie every episode: boundary dissolution, adaptive complexity, embodied knowledge, and quantum-like uncertainty. These aren’t just philosophical musings but frameworks for understanding our modern world.

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