Take me to a nondescript college campus, to some nondescript two story building. My AI watches the doors, a plain two door entrance. If an asychronous stream of five people per hour go in and out, my AI agent will guess this is the lab building.
This came up in Klings blog. My AI makes a basic assumption, nondesript builds are designed by the same AI algorithm, designed to efficiently flow people throughout the building. So, the door is my hole into a Black Body, and my AI is finding the orbitals inside. But more generally, finding orbitals from a limited and known set of building types. It does not expect an airport inside of a building, it is not doing Isaac's grammar.
This is a hologram algorithm, finding a hidden symmetrical axis that acts as a 'Lagrange' point for all, within the boundary conditions of the finite set. So, for a lab, there are only a few finite ways to spread them out in a two story. So, this general quantization problem reduces to a large uncertainty, finite selection set; more like estimating the NBA rankings going forward then solving an atomic orbital. The hologram concept remains, it is like reverse engineering the Black Body by observing a small, radiation from a tiny hole, except we are extremely sparse.
Pattern matching sparse is a term, much to be contrasted with almost Euler dense. When we are so sparse, our Lagrange points are easily discovered distance measures (small set of numbers) between to finite sequences. In this case, computations should boil down to finite, directed graph; low order (3-9).
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