[context][unknown] or [context]x

What comes after the known context? Traditionally information is gathered/inputted from left to right, what comes after information, is unknown, and the previous known information determines what comes next. It looks like “[context]x” where x is an unknown bit, and is used in modern AI, compression, and information theory in general.

Back in 2004, while going to school for my Network Administrator diploma, I coined a term “Spatial Decomposition” to describe my goal of achieving the removal and addition of information in a non-linear fashion with a 2 dimensional space of information. I have a working decomposer / recomposer that uses a simple zero context (input) for determining the probability of an event occurring. Alone this predictor has no knowledge of much of anything, it accumulates two core values at a minimum. It can produce multiple probabilities using different techniques, as well as mix probabilities including weighted to determine the unknown. On it’s own it would be similar to one artificial neuron in a neural net where their is still a lot of room for the transformation of the signal with other ‘neurons’. The difference from modern inputs, is that the input of information can come from the left side and the right side narrowing the guess by a large amount as well a neuron on it’s own is very ineffective at making a guess. One Spatial Decomposition ‘neuron’ can produce significant results. This particular ‘neuron’ on it’s own with no context has no real memory requirements, just needing computational power.

What if we could determine the unknown using the both the left and right hand side instead of the traditional linear left context for input?

Wouldn’t it make it much easier to determine say the following –

“How [unknown] you today?

Instead of

“How [unknown]”

Is this possible? Yes it is, using Spatial Decomposition, where the context of information is no longer required to be isolated preceding a symbol.

The principal equation of a layer of Spatial Decomposition / Recomposition is fairly simple:

SpatialBounds+SpatialLayers=SpatialLayers-SpatialLayer;

Where a layer is not required to be known, it’s essentially free.