Symbols can live more than fifty times longer than humans. They require almost no energy to persist, just the occasional refresh every fifty years or so to not fade. How do we talk about this space where symbols are popping up, fading or being replicated, on surfaces and screens? I suggest a new word: Infosphere.

I believe learning through motion-through conducting physical experiments in the real world-is vastly superior to learning through reading.

But the number of experiments one can do is vast, with some being far more informative than others, and time and resources are limited. Reading can point one to the motions-the experiments-that are most informative.

Thus, the most valuable symbols are the ones that guide one to conduct the most useful experiments.

Lately I've been figuring out how to put all of science into one file. I think the file would largely be experiment after experiment after experiment. Each one, the near minimum number of symbols to communicate the easiest experiment that can be done to cause learning the next most useful set of patterns about the world.

Pretext is all the words, all the definitions, all the patterns not in the text but that the text depends on. The texts that the author possesses and the reader requires.

We can embody all of science into a single fully connected text file.

Lately I've been thinking about topological sorting.

Topological sorting is sorting concepts in dependency order.

For example, if you wanted to sort "fire" and "internal combustion engine", fire would come first. To explain ICEs, you need fire, but to explain fire you don't need ICEs.

A scale is an ordering of numbers. Objects map to a scale to allow comparibility in that dimension.

The word scale is an overloaded term. Usually when I use the word "scale" I am using a different version of it, such as "scale it up" or "economies of scale". In this post I'm using it in the sense of a measurement or yardstick or number-line or type.

Do you want to learn a new way to think? And a new way to write? Do you want to learn how to look at everything from a new perspective?

If so, read on.

I will try to explain to you how to "think in Parsers". I'll also then go into "writing with Parsers", a computer language we've made. It might not click immediately, but when it finally does I think you'll find it wonderfully useful.

No matter what topic you want to learn, from music to math, chemistry to carpentry, mechanics to quantum mechanics, thinking in parsers gives you another path to understanding any domain.

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Can we quantify intelligence? Yes.

Program P is a bit vector that can make a bit vector (Predictions) that attempts to predict a bit vector of actual measurements (Nature).

Coverage(P) is the sum of the XNOR of the Predictions vector with the Nature vector.

Intelligence(P) is equal to Coverage(P) divided by Size(P).

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by Breck Yunits

All tabular knowledge can be stored in a single long plain text file.

The only syntax characters needed are spaces and newlines.

This has many advantages over existing binary storage formats.

Using the method below, a very long scroll could be made containing all tabular scientific knowledge in a computable form.

May 11, 2024 — That charts work at all is amazing.

Forty years.

One-billion heart beats.

Four-quadrillion cells.

Eight-hundred-eighty-octillion ATP molecules.

Compressed to two marks on a surface.

by Breck Yunits

April 23, 2024 — I wrapped my fingers around the white ceramic mug in the cold air. I felt the warmth on my hands. The caramel colored surface released snakes of steam. I brought the cup to my lips and took a slow sip of the coffee bean flavored water inside.

Happiness is a hot cup of coffee in a ceramic mug on a cold day.

March 30, 2024 — Given a box with side S, over a certain timespan t, with minimum voxel resolution V, how many unique concepts C are needed to describe all the patterns (repeated phenomena) P that occur in the box?

December 28, 2023 — I thought we could build AI experts by hand. I bet everything I had to make that happen. I placed my bet in the summer of 2022. Right before the launch of the Transformer AIs that changed everything. Was I wrong? Almost certainly. Did I lose everything? Yes. Did I do the right thing? I'm not sure. I'm writing this to try and figure that out.

January 20, 2020 — In this post I briefly describe eleven threads in languages and programming. Then I try to connect them together to make some predictions about the future of knowledge encoding.

This might be hard to follow unless you have experience working with types, whether that be types in programming languages, or types in databases, or types in Excel. Actually, this may be hard to follow regardless of your experience. I'm not sure I follow it. Maybe just stay for the links. Skimming is encouraged.

by Breck Yunits