The evolution
of description
Copyright 2016 Graham Berrisford.
One of about
300 articles at http://avancier.website. Last updated 12/01/2021 10:51
This is a supplement to the chapter introducing description theory here https://lnkd.in/dQNhNbd
It explores the evolution of description.
Contents
The big bang and space-time
continuum
Sensing and reacting to continuous change
Sensing and reacting to discrete things
Remembering perceived entities and events
Creating and using mental models
Recognising family resemblances
(fuzzy types)
Abstracting logical mental models from physical ones
Using words to define and
communicate fuzzy types
Beware that natural language is an imperfect
communication mechanism
The strict types used in mathematics and computing
Fuzzy logic and artificial intelligence
The aim here is not to present a new “a priori”
view of the world.
The aim is to integrate existing theories into a
coherent story.
The story of description is well-nigh magical, and
not limited to humans.
A honey bee can dance to describe where a discrete pollen source can be found.
It can communicate
using a code that not only other bees but also humans can decode.
And recent experiments show that though a honey bee has a brain the size of a grain of sand, it can count up to four.
Heinz von Foerster
(1911 to 2002) was a thinker interested in the circularity of ideas.
He is reputed
to have said “We live in the domain of descriptions that we invented.”
But we don’t live in a universe we invented.
Scientists believe it started with a
big bang about 14,000 million years ago.
In the beginning,
there was a lot of energy, and then a lot of disordered matter.
Gradually, the laws
of physics and evolution created things so orderly they can seem to be
designed.
Planets fell into
orderly repeating orbits; tides ebbed and flowed on a daily basis.
So now some things
in the universe behaved in an orderly fashion.
The earth was formed about 4,500
million years ago.
And life on earth began at least 3,500 million
years ago, possibly more.
There were things on
earth before life, but no description of them.
There were no memories and messages.
There was no conceptualisation
or model of things in space and time.
There were no representations or
symbolisations of what exists and what happens.
Description is a side effect of
biological evolution.
Darwinist evolution in biology is not goal-directed with a fixed forward-looking goal; rather, species adapt themselves to an ever-changing environment.”
http://plato.stanford.edu/entries/scientific-progress/#ReaIns
Eventually, by consuming energy, some matter became organised into an organic life form.
An organism is an individual life form - or single-celled entity, a plant, an animal.
It is a system - meaning an organization of parts that cooperate in repeated processes to a common end.
Plants and then animals evolved to sense and react to variations in continuous variables
Both internal
variables (hydration, salinity, PH) and external variables (heat, light,
sound).
They adapted their behavior to changes in these variables - autonomously.
|
Autonomic
adaptation to change |
|
Senses of variables <inherit and use> <abstract from> Animals <sense and react to> Matter
and energy |
http://plato.stanford.edu/entries/scientific-progress/#ReaIns
The universe is an ever-unfolding
process.
Physicists describe
it as four-dimensional space-time continuum.
The word
“continuum” implies space and time can be subdivided without any limit to size
or duration.
We divide it where the form of things changes – as on solid-fluid phase boundaries.
And where the state of things changes - such from night to day.
This implies animals hold an internal model of the sense of a thing.
The model is a bio/electro/chemical pattern of
some kind, inherited from a parent.
The animal can match new perceptions to it, and act
according to inherited rules.
|
Autonomic reactions |
|
Sensations <recognize> <represent> Animals < detect and react to> Phenomena |
Animals evolved to recall new things they perceived.
They can record a first perception in a first sense memory.
The encode a second perception in a second sense memory.
Then match the two memories, and act according to whether they recognise a recurrence of the first thing.
.
|
Sensations and memories <recognize> <represent> Animals <detect and
react to> Phenomena |
This research suggests
most animals remember particular entities better than particular events.
However, animals can recognise a pattern or sequence in which
events happen.
This other research suggests even rats can replay
memories in order to recognise things in sequence.
You can remember the sequence of steps in a dance, notes in a melody, or
words in a story.
And of course, the sequence of words in a sentence or message is
important to its meaning.
My notes on how the brain works suggests we know very little about how it works.
But
how brains create and use descriptions of reality doesn’t matter here; it only
matters that they evidently do.
Today,
EA is about business activity systems that remember information,
The
record and use information about entities and events they need to monitor and
direct.
They
use that memory to decide how to responds to events when they happen.
Conant’s “Good Regulator Theorem”
is a fundamental principle of cybernetics.
It says "every good regulator of a system must be a model of that system".
|
The good regulator |
|
Models <create and use> <represent> Regulators <detect and direct > Phenomena |
Similarly, we humans monitor and direct
entities and events in our environment.
We must have a model of those entities and
events, however sketchy that model may be.
The existence of mental models is an
abstraction for which we have only indirect evidence.
But that evidence is overwhelming (honey bees
tell other bees where the pollen is, and they find it).
So, we can use the term without needing to explain how a mental model is made or remembered.
It is often said that perception is reality.
However, animals make predictions based on perceptions.
When predictions are tested and found to come true, then we have some assurance that, also, reality is perception.
That is how biological evolution helped animal with accurate sense models to survive, thrive and reproduce.
(This may be considered contrary to Wittgenstein’s argument that a private language is impossible).
To exclaim
Danger! Is to convey a meaning, a concept, a type that classifies the current situation.
“In describing a situation, one is not merely registering a [perception], one is classifying it in some way, and this means going beyond what is immediately given.”
Chapter 5 of “Language truth and logic” A J Ayer.
To compare every new thing we see against every remembered thing would be a very inefficient way to look at the world.
If we can remember a common pattern of sense memories, then we can compare a new thing against that pattern.
This must be a more efficient way of recognising what a thing is and how to deal with it.
Evidently, animals have evolved to perceive the world as composed of things that share family resemblances.
And learning depends on the ability to detect resemblances things, and act accordingly.
Experiments show animals remember family resemblances – perhaps as a group of sense memories.
This forms an abstraction - a generalisation – a pattern - a “type” – that matches a set of similar things.
|
Typification |
|
Types <create and use> <encode qualities of> Describers <observe> Similar
things |
For example
Consider the shadow of a man on a cave wall; a pattern of light and shade.
It has no meaning until viewed by an actor who is able to recognise it as a description of something.
It might be
recognised as the body shape of the man
type.
Dogs use their sense memories to help them survive by
recognising things and anticipating the behaviour of things.
A dog senses what it observes to be a discrete
thing in reality, say a bone.
The dog has no words for the qualities and
quantities that its senses detect.
Nevertheless, the dog does manage to encode a
memory of the bone thing in its brain.
The memory might be a grouping of sensory measures
(smell, taste, texture…) and experiences (chewable, pleasurable).
Later, when the dog finds a new thing that resembles the old thing, it can act
according to its recorded experience.
The resemblance must - near enough - group same
qualities in similar quantities.
Repeated observations of resemblances can
strengthen the dog’s memory of how best to deal with things of that type.
The goal of this learning process is to optimize
performance and minimize the number of mistakes made in dealing with new
things.
If an animal can remember a general pattern
or type, then a startling new idea emerges.
It becomes possible to count things of that
type, and communicate the total number to others.
|
Abstraction of quantities
from sets |
|
Quantities <create and use> <represent totals of> Counters <observe and envisage> Things of a type |
The argument here is that logic and
mathematics cannot exist until intelligences have crystallized family
resemblances into types.
You can’t say a statement is true or false,
until you know the types referred to.
·
You can’t say “This man is my brother” is true
or false, until you know the properties of the brother type.
·
You can’t count your siblings until you know the
properties of the sibling type.
·
You can’t count any things until you know the
type to which they
Primitive animals surely don’t classify things into rigid
“types”.
But they can recognize “family resemblances”
between similar things (e.g.
food items, friends and cliff edges).
And learn to respond to similar things in
appropriate ways.
Scientists have studied how far honey bees,
dolphins and babies can understand quantities.
We know many animals can recognize when a smallish family
of similar things gains or loses a member
Experiments show
dolphins can recognize which of two boards has, say, five dots rather than six.
And babies (before
they have words) can recognize when a small group of things gains or loses a
member.
Astonishingly, experiments suggest honey bees
can count up to four and communicate that amount to other bees.
Honeybees
are clever little creatures. They can form abstract concepts, such as symmetry
versus asymmetry.
And they
use symbolic language — the celebrated waggle dance — to direct their hivemates to flower patches.
New
reports suggest that they can also communicate across species, and can count —
up to a point.”
http://www.livescience.com/2909-bees-count.html
A number is a
description made by a describer who can do three things.
1. observe
or envisage several discrete things
2. either
aggregate them (e.g. items in basket) or match
them to a given type
3. count
the things in the aggregate or matching the type
Types enable
counting; numbers imply types.
And types can be organised in type hierarchies.
Sense memories help
animals to direct behavior to their advantage.
The direction can be autonomic: you sense heat and automatically starting sweating.
Or conscious: you see a train coming, predict it will run you over, and step off the track.
Animals who can form more accurate mental models survive and thrive better than those who form inaccurate models.
|
Idealisation triangle |
|
Mental models <create and use> <abstract concepts from> Animals <observe and envisage> Realities |
Here, mental model does not mean a philosophy of life, world view or “Weltanschauung”.
It means a model of particular concepts/meanings that actors perceive or envisage
in realities.
We form and store such mental models in our
minds, in written words, and in other ways.
We share mental models using variety of
communication mechanisms.
·
Gesturing
animatedly to an oncoming train is enough to share the mental model of that as
a threat to survival.
· Spoken words translate mental models into transient sound waves – heard by a currently present audience.
· Written words transcribe mental models into persistent graphical symbols – readable by future and remote audiences.
Logical and physical mental models.
Physical
mental models are bio-electro-chemical
Logical
mental models are concepts/meanings that actors perceive or envisage.
Physical
mental models are private, not directly shareable.
Logical
mental models are shareable between animals.
How do we know both kinds of models
exist?
We define a logical model, and
predict actions to follow from sharing it.
We test that the logical model has
been shared between animals
Successful tests imply the logical
model has been stored in a physical form.
E.g. we express the logical model represented in a
honey bee’s dance as the “distance” and “direction” of a pollen source.
Then
test that other bees find the pollen in the location thus described.
This
shows those bees have stored mental models long enough to complete actions as
predicted.
The
storage of each physical mental model must be bio-electro-chemical, but how it
works is irrelevant.
Physical models may be fragile, unstable, and inconsistent.
Nevertheless, evolution first made them good enough to be storable and actable on.
Then
equipped animals with tools to share the logical information/meaning in them.
Animals evolved
to communicate descriptions of things to their fellows.
They translate
their mental models into messages that others - even in other species – can interpret.
· One bottlenose dolphin can recognise another by its signature whistle.
· Honey bees dance to describe pollen
locations.
· Dogs bark to tell us a stranger has arrived.
· Domesticated dogs can communicate several meanings in barks, growls, howls and whimpers.
The remainder of the story is essentially a human one.
Verbal descriptions of perceptions and memories
are the foundation of advanced reasoning and communication.
They help us to communicate about complex things,
both observed things and envisaged things.
We expect listeners/readers to recognise speakers/writers complex meanings, and act accordingly.
We humans developed the ability to communicate
descriptions of realities using spoken words.
Then found we could describe words using words, and convey these definitions to a message receiver.
We developed ways to preserve and share
descriptions using written words.
We invented dictionaries, and conventions for
defining words (by genus and difference).
Long ago, astronomers observed things in the sky that shared the property of being a light source, a “star”.
They noticed some “stars” shared a second common property, that of being “wandering”.
They invented the type name “planet” as a short-hand label for these two properties of a planet.
The spoken word is transient, and has a limited audience.
To illustrate verbal type names and definitions, this work has to use the written form.
|
These |
are described using a |
which is to imply the
ideas/properties in this |
|
Things |
Type name |
Type elaboration |
|
Venus, Mars etc. |
Planet |
Wandering thing, Starry thing |
The table above shows how human actors idealise real things, and encode those ideas in verbal descriptions.
We use type names as a short-hand to describe things that are observed or envisaged.
By
defining words using words, our brains surely grow a vast network of
type names, associated with each other and with other sense memories.
A type might be recognised:
· in a private language known only to one individual
· in a domain-specific language shared by a specialist group
· as a universal concept (universally useful and logically consistent with previously agreed universal types).
In the long view, types turn into states.
· Over a short time, you see objects you can classify under structural types (e.g. child and adult).
· Over a long time, apparently static object types now appear as the transient states of processes.
This is why type hierarchies don’t work well as persistent database structures.
The English language used here is rich in words that have multiple meanings and words that share meanings.
Consider these words: type, concept, property, quality, feature and attribute. They are all used definitive descriptions.
It seems some of these words work better with certain verbs others. E.g.
· A rose bush is a plant that instantiates the types “thorny”, “flowering” and “bushy”
· A rose bush is a plant that embodies the concepts “thorny”, “flowering” and “bushy”
· A rose bush is a plant that exhibits the properties “thorny”, “flowering” and “bushy”
· A rose bush is a plant that has or possesses the qualities, features or attributes “thorny”, “flowering” and “bushy”.
Further confusion arises in discussion because
concepts, properties and qualities exist in two forms: as types and
instances (values or facts).
The term "property" is used ambiguously to mean property type (“height in metres”) and/or property instance (1.74 metres).
The term "concept" is probably used more often for the property type than the property instance.
Beware also that the terms “class” and “type” are widely confused, for example, in programming languages.
We use “type” as above – with reference to the description of one individual thing (e.g. one planet).
We try to reserve “class” to mean a set or group (e.g. all planets), which could have a property of its own (e.g. average volume).
The by-products of evolution now include mathematics and computing.
Mathematicians define mathematical objects in terms of strict types.
They form mathematical proofs using those types.
Computers read process types, perform process instances
They create data structures that instantiate data types.
They are based on boolean logic,
in which everything is "true or false" (1 or 0).
Whether everything is ultimately describable in binary
terms is a philosophical question worth pursuing.
But in practice, much computer input and output data is
somewhere between true or false.
The evolutionary story of description runs on to artificial intelligence.
What follows is edited from http://whatis.techtarget.com/definition/fuzzy-logic.
The words we use are not strict types
(except within a very limited ontology).
Natural language (like most of the universe) is not
reducible to true or false sentences.
A "state of matters" or "fact" that we recall or communicate has "degrees of truth".
0 and 1 may encode wholly false and wholly true; but there are degrees of truth in between.
E.g. the result of a comparing two things is not "tall" or "short" but ".38 of tallness."
Think of it this way
·
the
strict types used in maths evolved from the fuzzy types in nature.
·
the
binary or Boolean logic used in computers is a special case of the fuzzy logic
in nature.
Fuzzy logic seems closer to the way
our brains work.
We aggregate data and form a number of
partial truths which we aggregate further into higher truths.
When certain thresholds are exceeded,
further results such as motor reaction, are triggered.
A similar process is used in neural networks, expert
systems and other artificial intelligence applications.
Fuzzy logic is essential to the development of human-like
capabilities for AI (aka artificial general intelligence).
AI is the representation of generalized human cognitive
abilities in software so that, faced with an unfamiliar task, the system can
find a solution.
There are now robots that can perform the magic of abstracting a general type of thing from observations of particular things.
“Theoretical results in machine learning mainly
deal with a type of inductive learning called supervised learning.
In supervised learning, an algorithm is given
samples that are labeled in some useful way.
For example, the samples might be descriptions of
mushrooms, and the labels could be whether or not the mushrooms are edible.
The algorithm takes these previously labeled
samples and uses them to induce a
classifier.
This classifier is a function that assigns labels
to samples including the samples that have never been previously seen by the
algorithm.
The goal of the supervised learning algorithm
is to optimize some measure of performance such as minimizing the number of
mistakes made on new samples.” Wikipedia
Biological and psychological evolution have developed and refined how we typify and describe reality.
The evolutionary story runs something like this.
· The big bang and the space-time continuum
· Sensing and reacting to continuous change
· Sensing and reacting to discrete things
· Remembering perceived entities and events
· Creating and using mental models
· Recognising family resemblances (fuzzy types)
· Counting things
· Abstracting logical mental models from physical ones
· Verbalising mental models
· Using words to label, define and communicate fuzzy types
· The strict types used in mathematics and computing
· Fuzzy logic and artificial intelligence
This story leads also us to a particular view of philosophy.
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