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Chapter 3 Intelligence


Chapter 3 Intelligence

Deep Thought does not know the ultimate question to Life, the Universe and Everything, but offers to design an even more powerful computer, Earth, to calculate it. After ten million years of calculation, the Earth is destroyed by Vogons five minutes before the computation is complete.
From "The Hitch Hikers Guide to the Galaxy" by Douglas Adams

This chapter is split in two halves one is the "Statistics of words" and why that gives us humans a huge advantage.  The second half is "How to develop intelligence" from using language and neural nets.

For “Artificial Intelligence” as Deep Thought would tell you, if it was a real computer, that what you really need is a lot of completely radical individualist thinkers all with free will listening to whoever they choose to.

This is because with simple connections the intelligence goes up in the order of squares of the number of the thinkers not linearly.  So having a planet full of trillions of beetles should be a million times better at thinking than a planet full of billions of humans.  Assuming the beetles communicate globally. However, Words make all the difference.

Statistics on Words


It maybe that we do not understand Beetle but with humans, it is a lot more obvious that we communicate globally.  Human individuals are parts of families, families are parts of tribes, tribes became cultures and city states and countries including renowned trading families that struck trading agreements, which then formed alliances and grew into trading blocks and religions and global corporations transcend all these borders.

All these groupings have rulers and governments and committees, create minds, within minds, within minds as each culture defines it's own way of making decisions as does every sub-culture.

Those minds have been slowly getting bigger and bigger as the groups have moved towards a global economy.  It seems that all humans do is communicate.  

However, there is a big difference between humans and beetles, we appear to have many more words than beetles, words expand the power of communication way more than astronomically.

When we use Words these connections can have so many more subtleties. The formula for the number of meanings is the number of letters in the alphabet raised to the power of the number of characters in the Word.  the growth is astounding.


Characters in phrase
Letters
1
2
3
4
5
6
7
8
9
10
1
1
1
1
1
1
1
1
1
1
1
2
2
4
8
16
32
64
128
256
512
1024
3
3
9
27
81
243
729
2187
6561
19683
59049
4
4
16
64
256
1024
4096
16384
65536
262144
1048576
5
5
25
125
625
3125
15625
78125
390625
1953125
9765625
6
6
36
216
1296
7776
46656
279936
1679616
10077696
60466176
7
7
49
343
2401
16807
117649
823543
5764801
40353607
282475249
8
8
64
512
4096
32768
262144
2097152
16777216
134217728
1073741824
9
9
81
729
6561
59049
531441
4782969
43046721
387420489
3486784401
10
10
100
1000
10000
100000
1000000
10000000
100000000
1000000000
10000000000

With an alphabet of just 10 characters and a 10 letter statement you can create 10 billion words approximately 3,000 times as many as there are. in the English language. 

The extra length to the words in an actual language is largely due to the grammatical restrictions we put in human languages of mixing consonants and vowels, using prefixes and suffixes in standard ways.  which means we do not use the full flexibility of letters.


With just an alphabet of 17 characters and 17 letters you can create over 827 Quintilian words. A Quintilian is1 followed by 18 zeros.  Way more than we could ever need in a life time. 

An aside on random passwords

This is why using truely random long passwords including upper case, lower case, numbers (some accept punctuation as well), that are different on all your internet and computer accounts is so important.

Just the lower case letters is an alphabet of 26 letters mix that with upper case gets you to an "alphabet" of 52 letters and adding ion the digits gets you to 62 characters. This increase in the number of characters increases the options a lot but not as much as by the length of your password.  Each character you add to the length makes it 62 times harder to crack; as opposed to just lower case which is only 26 times as complicated.


Back to astronomical numbers...


However, any sentence can be as long and as complicate as you want.  The actual number of logical statements is way beyond astronomical.

With our 26 letter alphabet with punctuation one could in just a 16 letters uniquely name every single star in the visible Universe.  Although, at the moment we cannot see them and I suspect we would choose a larger number of letters to avoid names with excessively repeated consonants or vowels or names that sound the same.

With our language it is possible to describe far more than the Universe, within one's own life time.  This means our Community of billions with all its hundreds of thousands of words far outstrips any animal which does not use words or only uses very few words.

It is the use of words that make humans so much more intelligent than beetles.The word is far more important discovery than fire.  If someone discovered fire without the word to pass it on to their children it would have soon gone out.

How to develop intelligence

 

American and English difference

 

Neurone and neuron mean the same thing. On the BBC we use neurone. So, please excuse the deliberate spelling mistakes.

Getting back to intelligence

 

Getting back to many small thinkers and how to develop artificial or even communal intelligence.

We find in America and indeed most “Westernised” democracies free thinking humans tend to listen almost exclusively to their leaders, whether that is political leaders, thought leaders in science and/or religion or fashion leaders on the music scene.  They give up their freedom voluntarily to fit in with society and gain cooperation from society and acceptance from their peers.

Let us look at a technically simpler example.

Neural nets are the most economical and effective form of Artificial intelligence otherwise known as AI. They are based on our understanding of the structure of the brain, hence the name.  The network is made of many nodes each with many inputs and one output.

The brain is made up of many neurones.  Each neurone has many inputs called dendrites and one output called an axon, the same pattern as the Neural Net.

Neural Networking is based on many layers of these very simple nodes, each node listens to which ever node it can on the previous layer and has one output to the next layer.  Almost always the most important input node is what is known as the Feed back loop.  The connection which tells the network how well the whole network did last time it made a decision the axon of the whole network.

Initially each node chooses in some predetermined or random way which input it is going to deem important. Each input is another nodes output. All it can do is adjust how important it thinks the various inputs are, how much it “loves or hates” that input, and adjust its output accordingly. So it multiplies each input by a value which adjusts its importance and so comes up with a different output.

In its simplest form love is a measure of how much we accept someone and hate is how much we reject someone or something. It is the weighting the lover gives as to how important the loved one is. The adored at one end the ignored in the middle and the detested at the far end of the negative end of the love scale.

That is a very simple but profoundly important definition of love. It is used within the simplest definition of intelligence.  It keeps track of how much it appreciates the relevance of an input and has a memory of it. The axon the output of the network feeds back to the first layer. That feedback gives the network the ability to learn. As it gives a measure of success of each combination of weightings and the network learns the best way to get the desired result.

The only input from the “Programmer” is a definition of the desired result.

Like a child it learns from experience, when it makes mistakes it corrects them and usually within about 36 generations of evaluation the neural network is quite capable of knowing how many cars are waiting at a set of traffic lights or on a road faster and more accurately than a human can. There are some things they are good at and some they are bad at.

For example one military experiment went badly wrong and instead of being able to count tanks in battle field the neural net learnt to recognise whether it was a cloudy day!  On cloudy days there were no angular shadows or shapes on clear days there were lots often many more than there were tanks.

Our brains can read distorted images much better than neural nets.  This is why as a security input on most websites you have to retype the letters shown on a list of distorted letters.  This is precisely to stop computers getting in, computers pretending to be humans.

Most people would not call the weighting of an input love in one node appreciation nor intelligence but put only a few hundred nodes together in a layered network and they can recognise exactly how many people are on a busy platform on an underground platform to within 1 person something very few humans can do!

The big advantage of Neural nets is neural networks can form more complex neural networks. For example at each road junction in London is a closed circuit television camera and associated neural network can tell how much traffic there is at each junction. A larger neural network takes the input from all these junctions and can then keep the traffic flowing through London and can take in to account the concerts and the football matches and the neural net manages London's traffic lights and does so much better than any human could. The central neural network coordinates all the lights in the junctions centrally and then sends out the timings back to each set of lights individually.

Networks can specialise, which is why this network not only has the equivalent of an eye the camera but an optic nerve and an optic centre to recognise the traffic patterns which match what we have eyes and visual centres and verbal centres in our brain.

Some lift systems in Japanese sky scrapers know the habits of their inhabitants better than the inhabitants and open lift doors as that person approaches the lift. Now that is what I call service. I think you can see where this simplistic form intelligence is heading towards a love we could recognise as love.

However, it must be recognised that this “service” is programmed in as the desired goal.
These neural nets are making real world choices. They are a part of our culture and performing useful services for mankind.

The reason we listen to our thought leaders is exactly the same.  We listen to our leaders for a similar reason to find out how well we did and how we can improve.