Human intelligence is a complex set of information. It requires the most advanced features explained in the previous chapters. It requires a cooperation of many thought centers all with a well developed awareness and a kind of flexibility as roughly described in chapter 22 on language.
The goal of artificial intelligence is to mimic the properties of human intelligence by means of a computer program. There are many approaches to tackle this challenge. Some efforts start by simulating directly logical inferences in a particular area of knowledge, another approach is to mimic the basic abilities of learning and make the system learn all the rest.
I propose not to simulate human intelligence but to use it as it is and transplant it into a computer (the information being of intelligence). The result is not artificial intelligence but human intelligence working in an artificial breeding ground. Although this seems at first pure science fiction, I believe it is possible, even with the computers of today.
To transplant human intelligence, we must be able to grasp it. The theory of behavior of information is of great help here. First we have learned that all knowledge, thus also intelligence is information, so what we want to grasp is a set of information.
A second thing we have learned is that all used knowledge is also expressed in the result of its application. So wherever human intelligence is applied, it is expressed in the result. This makes it possible to gather the information of human intelligence without unraveling human brains. Many applications of human intelligence are expressed in language. It should be possible to extract human intelligence from this text. Because there is plenty of written language available in computer readable form, text seems to be a cheap source of this precious information.
Language is the result of the instantiation of a complex set of abstract information. Extracting these abstract structures with preservation of their properties is the most difficult step in the process. Fortunately, we have plenty of text available allowing to make the proper abstractions.
Our brain is a network of a very large number of connected cells. These cells, called neurons, have been carefully studied. The logical functioning of each of these cells seems to be relatively simple.
Since the early days of artificial intelligence (1959), many attempts to mimic the brain have been based on the believe that the structure of the brain results from the operation of a large number of simple neuron cells. Not only the logical operations of the neuron cell have been considered to be simple, the rules to "grow" new connections where also considered to be simple.
Several simulations, with varying complexity have been made (and are still made today). Some stimulating results have been obtained. However, the level of comprehension remained very low. The study of these networks has been valuable for pattern recognition.
From the point of view of the theory of the behavior of information, the physical brain cells are only the most indurated externalizations of intelligence. The brain is an induration of something which functions at smaller, minute level. Before any connection between neuron cells grows, the result of this considered connection has been tested out many times at a smaller level. A new connection between neurons can only grow because the cells are "aware" of what is going on. Especially in the development stage, sophisticated messages are exchanged between the neurons discussing pro and con of establishing a certain connection. However, it is true that a developed brain functions mainly by means of the indurated connections.
The heaviest structures in our brain are externalization of something functioning faster and more stable at smaller level, let us say at molecular level. The establishment of neural structures is assisted by the functioning at this molecular level. The functioning at molecular level is the externalization of a similar functioning at atomic level. The construction of structures at molecular level is assisted by the functioning at atomic level. We can go on several layers more up to subatomic particles (as far we can see).
Simulating subatomic particles in a computer to simulate molecules to simulate cells to simulate the brain to simulate intelligence is of course impossible. Besides these levels of operation, there might be an influence from newer, more intelligent structures of less indurated informatter.
This seems a nice proof that the whole problem is of such complexity that any attempt to realize real transplantation of intelligence or simulation of intelligence is impossible. However I strongly believe that it is possible.
When an organization indurates, the abstract very universal elements become specialized in repeating always the same function. After being indurated, it looses all flexibility and it looses even the ability to do something else. This is also the case for the structures used by intelligence. Similar to the fact that a stable brain runs mainly on the almost mechanical reflexes of neurons, the developing brain runs on the already indurated reflexes of atoms and only partially the flexibility of molecules. The development of a new body does not involve the development of all new atoms and molecules. Indurated atoms are used and even some old molecules are used (otherwise we would have a propagation at more abstract level).
Because we would already be happy with a copy of intelligence (information) disconnected from its very abstract origin, we have only to take a few levels of externalization into account. This limits largely the scale of the project.
The most external structures such as a context described in chapter 22 must be highly flexible and being constructed dynamically (rapidly changing). Deeper levels are more rigid. The abstract structures in these levels are limited to almost reflex like actions.
I suppose it is sufficient to consider only three levels of externalization. The most internal (abstract, atomic particle) is fully indurated and a number of such elements are represented by memories of their reflexes (extracted from text). The next layer is an externalization of these elements forming an organization and being aware of their place in the organization (or even several simultaneous considered places in several organizations). The top layer is then a context structure similar to the description given in chapter 22.
Although classical computers do only one thing at a time and in a natural information structures there is plenty of simultaneous activity, they are at least good in some things:
It is very easy to represent a mapping and a varying distance between structures in a computer. There is no problem with physical space and no pipe like structures have to be build as in our brain. In other words, computer models are not restricted to three physical dimensions.
It is also easy to have many simultaneous instances of an abstract element in a computer memory. All the stable information (knowledge) which is present in every molecule and cell in our brain has only to be represented once in the memory of a computer and can be shared by all the instances.
Another advantage is the possibility to take a copy of the computer memory at almost any moment to create a copy in a separate computer going on to learn from the point reached. In a further stage, experience can be exchanged between the computers to share the findings.
Another possibility is (automatically) analyze the acquired information at a certain stage and to remove some flexibility to gain in speed. This process is a kind of compilation of the acquired information translating it into instructions which can be executed directly by the computer. This will allow to run fairly intelligent programs on computers of the size of the personal computers of today.
The theory of behavior of information is abstracted from a number of models of our intelligence I build in the last ten years for the purpose of artificial intelligence. I needed this abstraction to combine the structures of several models. It is my aim to start soon a new instantiation of this abstracted knowledge into a computer model. I hope to publish the results of these experiments in a separate work.
This is Chapter 24; Artificial intelligence of Behavior
Author: Luc Claeys. All comments welcome, mail to lcl at this site: nanohome.be
Last updated on Nov 12, 1997