HOPE: The Hilbert Operator for Progressive Encoding

A deep-dive into Google's DeepMind/UC Berkeley breakthrough HOPE, a data-free method that compresses networks by separating a frozen universal core from a plastic slack. We explain why traditional pruning misses value hidden in scale symmetries, how HOPE uses batch-norm statistics and maximum entropy to map a neuron’s true contribution in Hilbert space, and what this could mean for sustainable, continually learning AI—and for how we think about human intelligence. Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information. Sponsored by Embersilk LLC