Too much noise vs lack of information

Humans are good at ignoring noise. Image © | Dreamstime.com

Humans are good at ignoring background noise and retrieving only the information that they need. This ability is supported by generic learning mechanisms and improves with age and life experience.

At the same time, humans do benefit from hints. They use all, even small and ambiguous pieces of information for reducing their uncertainty about the intentions of the speaker, and in many cases the hints are sufficient despite their ambiguity.

Not enough uncertainty

Talking about oxidation at a Phlogiston conference. Image © | Dreamstime.com

It is easy to see how too much uncertainty can hinder communication. What about too little uncertainty?

Redundancy is useful

Saint Jerome in the Wilderness by Bernardino Pinturicchio. St Jerome is the patron saint of translators.

I used to translate. At the time I honestly believed in the fixed code model of communication: that there is meaning in the source text and that the task of the translator is to convey that meaning in the target text as exactly as possible. That view of language also included the belief that it is possible to convey meanings precisely, concisely, economically.

What’s wrong with terminology?

General semantics is a study of what words mean. This attempt to start from a smaller amount of data (words) and obtain a larger amount (meanings) is an impossible task of creating something out of nothing. Terminology approaches the same relation from the opposite direction – how are things called. The process goes from the larger dataset to the smaller one, which is not outright impossible. Could terminology’s description of communication be more plausible then?

Lossy encoding of thoughts

Could you reconstruct a document based on its hash sum?

A hash function is a unidirectional function for mapping a large dataset onto a much smaller one. Used in cryptography (e.g. digital signatures), hash sums allow easy verification that a document is indeed the document we expect it to be. The opposite task, to reconstruct the document from the hash sum, has been made deliberately difficult. Other examples of unidirectional or lossy encoding are file formats like jpeg or mp3 and, perhaps surprisingly, natural language.