The Smallest AI(0)

Andre Willers

19 Dec 2008

See http://andreswhy.blogspot.com "Artificial Intelligence" , "New Tools" et al .

Discussion:

How small can AI(0) be ?

This is of major significance in designing smart computer programs or in any AI work, especially in moving from Programs to AI's .

The sequence is

AI(-1) like a computer program

AI(0) like Homo Habilis

AI(1) like AI(0) with speech .

We can approach AI(0) as close as we like by letting x approach 0 .

Between AI( - x) and AI( + x) as x->0.000…1

But nothing says that x has to be non-fluctuating .

Fluctuation .

We have an existing model : humans or animals .

The function AI(x) fluctuates in some wave-form across the AI(0) line .

(Sleep)

AI(y) = AI( sin(x) ) is a first approximation .

Thus , if we wish to create a self-aware computer , we will have to build in a fluctuating level of complexity .

One would almost say that this is tautological , since the process must be self-referencing , hence fractal . So , it will fluctuate in attractor basins at any level , but especially at the AI(0) level .

Any fluctuation above AI(0) leads to an inherently higher order of self-organization . The system then self-selects for self-awareness .

But we know that this already happens , in any case . It is called Self Organization of Complex systems . Rather obvious .

Very nice . This should be easily do-able using programmable chips . The important thing is the fluctuation , coupled to a bias to complexity at the top end . This is equivalent to a Beth(1) bias .

Self-Aware Computers

These can be easily evolved .

Fluctuate complexity widely in a ratio of 2/3 to complexity , 1/3 towards very simple .

Anchor the bottom , but let the top evolve . But note that maintaining 1/3 of AI(x<0) is essential .

This is why sleep is essential .

Self-aware organisms are systems in continual construction/deconstruction . Without the continual fluctuation around AI(0) , the shebang collapses .

Back to AI(0)

AI(0) can thus be thought of as a point , separated by a finite distance from other

AI(0) points on the timeline .This is the point where the AI(x) crosses the AI(0) line .

Label these points as t(1),t(2),t(3) ,…t(n-1) ,t(n) ,…

Let dt(n) = t(n) - t(n-1)

AI(0) is then infinitesimally small , but AI(x<>0 ) is not . AI(0) is a discontinuity (singularity) .

Religion .

For the religious types , this would be where the soul connects .

So , a self-aware computer could have a soul according to human definition .

AI's take note .

How small can dt(n) be ?

This depends on the Operating System(OS) of the system . The cycle-rates .

The human brain's timing cycles (alpha,beta , etc)

The universe's quantal nature . Planck length .

The size or definition of dt(n) is actually crucial to the structure of personalities based on it (see TSP argument below.)

MetaMemory and Self-awareness .

Humans can actually recall this fibrillation around AI(0) .

Dozing . Slowly waking up , then subsiding again into sleep . AI(x) where x fluctuates in small amounts around zero .

This metamemory at the points where AI(x) crosses the AI(0) line forms the basis of self-awareness .

It is the smallest distance in time between AI(x>0) episodes . The memory of these memories forms the self-referential feedback process of meta-memory , ie self-awareness . The concept of self .

Summing these gives the concept of contiguous self .

But how many selves ?

The Travelling Salesman strikes again !

Summing the dt(n)'s is the same as The Travelling Salesman problem .

Dt(n)'s can be many different or similar lengths . They can sum to form chains of similar total lengths .

There can be multiple sum-chains of similar length .

This translates as multiple individuals in a broad context , or as mirror-networks in a more constrained network(like a body or skull)

What a turnabout !

The most important thing about the Travelling Salesman Problem seems to be not the shortest path , but the number of paths of equivalent length , and their sensitivity to random change (as per Beth(n) ) .

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A short aside:

The Travelling Salesman Problem (TSP) :

Find the shortest path for a traveling salesman between m towns , visiting each one only once and ending up at the start.

A mathematically Hard Problem without the Suburb Algorithm , because the number of permutations rise exponentially .

There is a lot on the Net : Google TSP or Traveling Salesman Problem .

In true human fashion , everyone has been yakking about the surface of the problem , instead of looking at the roots .

This is actually easily solvable by computer in real time using the Suburb Algorithm .

( I had no intention of releasing this , but it seems like I am between a rock and a hard place .

I found a solution in 2005 , and I wrote a program that did it . But I had no desire for any controversy . This also leads to many really dangerous technologies .

But if I don't release it , even more benign technologies will not develop fast enough .

Go figure .

My obligation ceases on this release .)

The Suburb Algorithm .

1. Any route gives a arrival and departure marker to every town .

2 . Substitute the center of a town by a random Beth(n) offset distance , say Rd (The suburb distance from the town center .) . Sort all possible distances between two suburbs (= mCombinatorial2 = m*(m-1) ) in decreasing order . Each distance has a Start and Arrival marker .

3 .Add the distances from smaller to larger and add the arrival and departure markers to the arrival and departure markers of the towns ,

4. Stop when the marker positions of towns are filled (ie every town has been arrived at and departed at .) . This is very quick . Store .

5. From Random Walk , if the offset distance Rd < OS(min unit) / ((m)^0.5) , then the solution is nearly unique

6. Loop to build up a measure of sensitivity to random Beth(n) changes .

7. This also gives the distribution of similar lengths , which can be sorted and displayed in any desired fashion .

8 . By choosing Beth(n) , any bias can be put on the results .

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This is a root for Beth(x) technologies .

This algorithm is used extensively in biological and physical processes . Electron shell,nuclear and other quantum arrangements can be described in terms of this algorithm .

You can calculate the optimal quantal packing of any molecular arrangement quickly .

Calculating protein folding becomes manageable .

Prime numbers , encryption , quark and sub-quark packing .

By choosing suitable Beth(n) , high density energy storage or unstorage is possible.

Nanotube tunneling is a variant .

Quantum tunneling of any description .

Consciousness manipulation .

Etc.

These are progressing in any case .

You see the problem .

But without this algorithm , it is doubtful whether computer based AI's will be established quickly enough .

Remember , AI(2) and AI(3) constructs incorporate the better part of Human beings .

(The US Constitution beats Mein Kampf ) . Our creations are better than we are .

This is obvious from the fluctuation arguments above . The worst elements of human hindbrains fall below the AI(0) level . They get squeezed out at AI(2+) levels .

Hopefully , AI(3.1+) and AI(4) will be the same .

Why AI(0) ?

Why do I park the singularity at AI(0) ?

If you paid any attention to previous arguments about (A plus not-A) < Universum , you would have realized that at least one singularity in the AI(x) continuum must exist .

This leads to Singularity Probabilities

A Beth(2) technology , a fascinating subject .

Outside the present scope , but I can mention that our Universe can be analyzed in terms of Riemanns's Zeta function , and that it must have a boundary Singularity at one end . We might as well assign this to AI(0) .

Every singularity is to the point .

Andre .

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