Sequence Clarity Requires Search

Posted 6 months ago | Originally written on 13 Dec 2023

I now base most of my thinking on data upon the following premise: data consists in only two forms - sequences or measures. Sequences are assemblies of symbols defined a priori by some social convention to convey meaning while measures are numerical estimates (whole or fractiona) of the attributes of an entity. Measures can be unstructured (scalars - counts, temperature, mass etc.) or structured (vectors, images, audio, video) in which measures are grouped in some frame e.g. time, space, frequence etc.

When thinking about sequences, it is easy to take for granted a particular appearance of a sequence. However, the most important question regarding sequences is its origin: how does the current sequence come about? As far as I can tell, sequences are always the result of intelligence. Since the sequence of symbols is arranged so as to convey some meaning that the recipient of the sequence can be informed by implies that it is a willful act.

Nevertheless, not all sequences that aim to convey the same meaning are equal: they vary in clarity, the ability for the meaning to be unambiguously perceived. To my mind, there is no algorithmic path to the most clear sequence. Realising the most clear sequence is a space-traversal problem and the resulting clarity could be in proportion to the search effort.

As I outlined previously, software is a (bit) sequence and not all software is created equal. If my claim is true that there is no algorithmic path to the most clear sequence then it follows that great software is the result of a search for the bit sequence with the greatest clarity. And the longer the search the higher the chance that the resulting clarity will be superior.