Visualizing high-dimensional data step-by-step #machinelearning
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When you understand these systems as high dimensional holographic compressions, it’s easy to see why they can “easily”discover causal trajectories that prove opaque to humans.As I’ve described it previously, seeing a goal-framed subspace of the world through the lens of a holographic compression is analogous to viewing a lower dimensional space from a higher dimension.While human security researchers and hackers navigate the causal maze from *within* its constraints, an LLM sees the constraint s
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