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Equation 33 · Part 3 · AI Feeds on the Distance Between an Intention and an Outcome

Symbol g

Wm(B)=∑(g,h)∈Gwgh 1{(g,h)∈Nm(B)}.W_m(B)=\sum_{(g,h)\in\mathcal{G}}w_{gh}\,\mathbf{1}\{(g,h)\in\mathcal{N}_m(B)\}.
gg

What this part means

in bit-transitions and h in active human minutes.

Its job in the formula

g appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

Where the article explains it

The region lives in intention-gap/horizon space: g is in bit-transitions and h in active human minutes.

The passage around this formula

To compare systems without pretending that this irregular region is a Euclidean animal range, bin the preregistered plane into cells and define width as Wm(B)=∑(g,h)∈Gwgh 1{(g,h)∈Nm(B)}W_m(B)=\sum_{(g,h)\in\mathcal{G}}w_{gh}\,\mathbf{1}\{(g,h)\in\mathcal{N}_m(B)\}. The unit is weighted grid cells, stated with the binning rule. Report its depth too: the greatest gap or horizon cell satisfying the reliability rule. A broad, shallow system has a large WmW_m but low maximum depth; a narrow, deep specialist may have small width yet persist at large g or h . Neither is “more intelligent” by definition. The map makes a tradeoff inspectable.

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Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

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See this notation across published equations →

The article lists its research sources here.