Intelligent design

Biological life is so complex that it must be created by intelligent design. On the other hand, it is so complex that no sane intelligent creature would dare to engineer it.

Simplistic View On Evolution

This is the environment.




These are the organisms.



This is the phenotype.




These are mutations. The phenotype does not much change.




Genotype explores the environment, learns about the limits suitable for living. If the environment (including diseases) does not change, the genotype stays in steady state.




The environment changes. Inbreeding occurs (sexual), mutations become obvious, phenotype swells.




Genes are in desperate search for new environment. New exists are found due to the memory obtained when the living conditions were good and stable.




Two incompatible genotypes are now created.

Saccade hypothesis

Thinking about how the visual cortex works I came to the conclusion that the 'movies' must be produced by the cortex itself. This is to make sure that the changes of the picture are not the changes in the world. One example for this can be saccades. I started looking for more information about this subject and surprisingly found that the main reason for saccades is difference in retina resolution. I do not agree with this.

According to the current belief the main reason for saccades of the human eye is that the central part of the retina, the fovea, plays a critical role in resolving objects. By moving the eye so that small parts of a scene can be sensed with greater resolution, body resources can be used more efficiently.

In my opinion this is not the case. The main reason for saccades is building abstract invariant of the picture. In visual cortex the information propagates up the higher level by building more abstract notation of the picture. The voluntary changing of the picture is the way for the cortex to deduce the common information. The crucial role here is that the brain (cortex) knows that the real cause (objects being seen) does not change. So the cortex must be working differently when the picture coming from the retina changes due to external factors or changes due to internally motivated saccades.

The memory prediction mechanism allows comparing the changed picture with the predicted according to a particular distance and direction of the saccade. This is done in partly learned memory. But the same mechanism works as finding the nvariants in the changing pictures produced by slightly different view.

The analogy of this process is the theory and the experiment. The theory is proven by different experiments giving the different results but fitting in the theory prediction. The assumption is that the essence does not change with the experiments.

"Saccades are a widespread phenomenon across animals with image-forming visual systems. They have been observed in animals across three phyla, including animals that do not have a fovea (most vertebrates do not) and animals that cannot move their eyes independently of their head (such as insects)." [Land, MF. "Motion and vision: why animals move their eyes". J Comp Physiol A. 1999 185:341–352.] Although in this paper the author argues that the reason for saccades "is the need to avoid the blur that results from the long response time of the photoreceptors".

Assuming that building image invariant is the main reason for saccades, one would come to a conclusion that the other voluntary picture changes should happen in the eye. Indeed, some animals allow their eye rotate [above paper]. Why human eyes do not rotate I do not know. Maybe our vision system is complex enough (3D) to allow less emphasis on obtaining picture.

Sensory cortex and saccade motor cortex must be closely coupled. Because the sensory cortex governs the motor cortex and motor cortex has to feed back its lower level information to sensory cortex. There should be many effects which can easily be tested by experiments to support this hypothesis.

How to measure chaos

This is to propose the way to measure chaos in a computational system.

I studied the reversible algorithms of arithmetic multiplication (factorization problem). I have found a very interesting fact that when the information describing the ensemble grows fast, this exact process is difficult to reverse, i.e. find feasible computation reverse algorithm. Let me explain by example of computation.

Suppose there is an initial set of N bits {x}. If all bits are independent and can initially be set to either 0 or 1, then this is the ensemble of 2^N states. Now suppose we have a function G describing all forbidden states of the ensemble. [The same up to inversion arguments would go with all allowable states.] Initially G=0, since all values of x are allowed.

Next suppose that we have a computational process which is done in atomic steps. Each step can be one of two kinds: either creating a new bit or forgetting an old. For example, add a new bit y₁=x₁&x₂ (let us signify & as AND operation, | as OR, and ^ as XOR). The G function becomes y₁^(x₁&x₂) if previous G=0, or in general case G_next=G_previous|y₁^(x₁&x₂). Forgetting a bit x₂ would make G_next=G_x2=0&G_x2=1, where G_x2=0 is G with x₂ replaced with 0.

It seems easy to keep G function defined while doing the calculation process. This is important because while we know G function the calculation is immediately reversible – it is easy at each backward step find out the previous forgotten bit. But the problem appears when G function becomes impractically huge in representation. This happens, for example, with arithmetic multiplication. The only way to attack this problem is to find symmetries in the function so to make it computationally practical, smaller.

Suppose that with a given intellectual strength we find the smallest representation of G function. This function can be written as a sequence of the atomic steps A=B&C, where A,B,C are bits or their negations (say A=B|C is ~A=~B&~C). The negation is not an operation, rather a perception of the bit. With this representation the function becomes measurable in number of atomic computational steps.

That makes it very natural to define the chaos as the increase of the size of the function G. Note that the definition includes the intellectual strength of the observer, which seems natural for the definition of chaos. Also it qualitatively resembles Lyapunov exponents as chaos measure.

Precise theory for Artificial Intelligence

Every research area has its own language. But there is a distinction between precise and non-precise theories describing the areas of interest. In physics, for example, the language is mathematical formulas. And this is a precise formal language; because formulae help, in general, to solve physical problems even if people applying the formulae have different interpretation of them. We can argue on the definition of Time and Enegry. But when it comes to the formula with E and t, everyone will get the same result regardless of personal (internal) understanding of these concepts.

Completely different situation is with Artificial Intelligence research area. Here are concepts of information, memory, awareness giving completely different philosophical theories depending on how the concepts are being understood. There is so big variety of opinions that it is difficult to call this area of research scientific. Nevertheless some groups of people sharing the same beliefs are able to communicate and even to make some progress in ideas. But with no practical results. Obviously now the understanding of the problem in question is different than ten years ago. Hopefully a simple model can be built in some near future, just to give the common basis for terminology used in this field.

Beating the nature

We are not cleverer than the nature. Just sometimes the nature does not understand us.

When we get together

When we get together our intellect does not add up, but our stupidity does.