Mapping Existence

Towards a Theory of Learning and Knowing

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What is Learning?

Cleaning Up Some Boundaries

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A note on computational efficiency and reducibility and probability is required. Computation in this essay refers to a whatever process to map a system to another system — to take a set of data and relate it to another set. Efficiency is considered on many levels very much how it is thought of as thermal efficiency — adding computation to a system should produce new net change/work/output. An inefficient computation is a process that the signal coming out of the computation is less than the signal that went in. While the universe and an average computer is full of inefficient computation coherent, “useful” systems have more efficient computation than inefficient or only the coherent parts are used.

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In statistics and now machine learning all models are bound by a trade off between bias and variance. Computational Efficiency can be considered a version of this basic phenomenon. A model (a relationship between one set of data and another) is efficient only when its bias and variance are sufficiently low (there is a reasonably high probability the model is an actual representative relationship) and the execution/use/running/operation/observation of consequences of the model can be done in fewer steps that observing all aspects of the relating systems.

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The Mapping is The Medium is the Message

But, wait.

The Algebraic Geometry Of Existence

Map of All Maps


Possibilities and Actualities

The Convergence of Learning as Existence

Consider Human Learning

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Consequences — Where the Efficiency Buck Stops

The Ultimate Human Consequence

The Ultimate Consequential Algebra

The Universe in a Nutshell

Return To Base

The Big Bang

A Bridge Over These Local Boundary Conditions

“Natural Laws”

Art As The Efficient Mapping




Interestingness — the metric that matters

What Is Knowledge, again?

But Does This Distinction of “Knowledge” Matter

The Question of Mattering is a Question of Value is a Question of Computational Efficiency

Back To The Origin (not that it’s possible)

And what sticks around will be what’s interesting.


Non-Understandability of AI

Reification and Agency and Measurement

Bias-Variance Trade Off

Complex System Typing and Features

Chaitin on Algorithmic Information, just a math of networks.

Platonic solids are just networks

Real World Fractal Networks

Correlation for Network Connectivity Measures

Various Measurements in Transport Networks (Networks in general)

What Is Data?

Brownian Motion, the network of particles

Semantic Networks

Prime Number Distribution


Probably Approximately Correct

Probability Waves

Bayes Theorem


Locality of physics

Complexity in economics


Gravity is not a network phenomenon?

Gravity is a network phenomenon?

Useful reframing/rethinking Gravity

Social networks and fields

Cause and effect

Human Decision Making with Concrete and Abstract Rewards

The Internet

The Power of IS

Written by

I be doing stuff. and other stuff. More stuff. I believe in infinite regression of doing stuff.

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