Merry Christmas

Rural Relaxation: A Winter Sunset in Surrey

Sunset seen through a break in the trees
Bookham Common, Surrey

When I am feeling trapped I find that there is no better way to relax than to take a rural walk.

Brain Storms – 8 – Was Douglas Adams right about the Dolphins?

Douglas Adams, in The Hitch Hikers Guide to the Galaxy, suggested that dolphins were more intelligent than humans.

Perhaps his conclusion was based on the finding of a vast conference in with all the animal species that ever lived sent one delegate to decide on which of them had the best brain. This caucus was called because they were all fed up with Humans unilaterally claiming that they were more intelligent than all the other animals – by simply defining intelligence as “those mental activities which humans can do which other animals can't do.”

Continues below the fold

Brain Storms - 7 - Getting rid of those pesky numbers

There has been a break in the brain storming – in part because I have been distracted by other things and in part because my ideas had become stuck in a box – and I hadn't realised the implications.

The box I was stuck in was the one relating to the handling of numbers in conventional computer systems . I had already decided that because hunter-gatherer man would not be doing much, if any, counting – much less arithmetic - I should strip the arithmetic facilities out of the CODIL model if I was to relate it to how the brain works. However when I came to think about mapping onto a neural net there were some problems

Perhaps the easiest way is to relate the problem to CODIL's history. The original ideas developed in a very large commercial data processing department where numbers (quantities, prices, dates, customer identity numbers, etc.) were of paramount importance. It was also concerned with the real world text names of objects such as people, goods for sale, and places. In developing the CODIL paradigm I did many unconventional things but I still kept the equivalent of the conventional computing concept of a data field – which had a name so that the computer could address it and a value which was directly linked to something in the real world. The result was the CODIL item, such as the following examples:

QUANTITY > 27

PRODUCT = Diesel

This structure has been retained in al subsequent developments – when the aim was to design a flexible and user friendly tool and the idea of having a “data base” package that could not handle numbers would have been ridiculous.

... But in modelling the brain I am not building a “user friendly” tool, I am trying to model how the pre-civilization brains worked. I note that there is no difference between Australian Aborigine brains and Western European brains – despite the fact that in evolutionary terms they are at least 50,000 years apart – while modern civilization is no more than 10,000 years old. This means that in developing a brain model anything which could be an artefact resulting from the development of civilization can safely be excluded.

So back to the little example. “PRODUCT” is the name given to a set which includes “Diesel” ... but wait - isn't “Diesel” the name given to a set which includes different grades of diesel oil ... If we get rid of numbers (and also some of the features for manipulating strings of characters) we can redefine the item value as a set name where the set contains a single member – the name of the set being a representation of its value. This could be represented (with trivial changes to the existing software) by representing the item “PRODUCT = Diesel” as:

PRODUCT  DIESEL

However at the paradigm level it is possible to say that an item is just a pair of symbolic set names where one set is a member of the other. What we have done is to simplify the CODIL model by discarding a component (the item value) and made the approach more general.

The implications of this will be discussed in later “Brain Storms” posts.

Earlier Brain Storms

1. Introduction
2. The Black Hole in Brain Research
3. Evolutionary Factors starting on the African Plains
4. Requirements of a target Model
5. Some Factors in choosing a Model
6. CODIL and Natural Language