Artificial Intelligence (UI, English Artificial intelligence, AI) is a computer science specializing in the creation of machines showing signs of intelligent behavior. The definition of "intelligent behavior" is still the subject of discussion, most commonly used as a standard of intelligence by human reason. John McCarthy came to 1955 for the first time.
Artificial intelligence research is highly specialized and specialized, and it is divided into several fields that can often not be linked. The whole research is also divided into several technical problems; some of the subfields deal with the solution of specific problems, some of them, for example, to use specific tools or to achieve specific applications. The question of whether it is possible to construct artificial intelligence is also closely related to the problem of consciousness, the question of calculations carried out by the human brain itself or the question of the evolution of cognitive abilities. Similar philosophies of artificial intelligence are similar dilemmas.
The main issues in artificial intelligence research include thinking, knowledge, planning, learning, natural language processing (communication), perception and the ability to move or manipulate objects. Achieving general intelligence is still one of the main goals of research in this field.
From the psychosocial point of view, artificial intelligence is one form of non-human intelligence.
Artificial Intelligence has set itself the goal of modeling a human activity that is considered intelligent when a model is a computer program, the exact world. Intelligent human activity is the product of human psyche processes, ie processes real world. They are modeled, on the one hand, by the external manifestations of human intelligent activity, the kind of structure of the human brain, still limited to neural networks. Let us recall that the exact world is defined in such a way that all its entities are precisely defined (ie zero internal vagueness), so that everyone familiar with them knows exactly what they mean, absolutely without any doubt. Their exactness therefore lies in the precise and infallible connection of the human psyche with their meaning. Artificial intelligence models the phenomena of the real world, but also the phenomena of the exact world. What tools do you have and what are the possibilities and limits in an exact world?
Modeling the exact world with the exact world
We assign to the exact world: mathematics, exact science, exact games, exact machines (Turing machine). The tasks that solve artificial intelligence in this area are (in the examples and in the corresponding order): derivation of theorems, cognitive robotics, chess game, intelligent algorithms. Since, in this case, the boundaries of the exact world do not cross, there are no transgressions against the correctness of the transfer to another world. Using the results of exact science (data and knowledge) in artificial intelligence, it has already correctly solved the exact science - moving it into an exact world.
How to get into the real world from the real world
The only bridge between the real world and the exact world is the instrument we call the magnitude. It is part of the exact world, because it is precisely defined (as described above) and at the same time it is an elementary, measurable manifestation of the real world or a probe into it. The only possible language of the exact world is the artificial formal language (mathematics, formal logic, programming languages), whose membership in the exact world is given by the exact interpretation (ie zero internal vagueness) of all its linguistic constructions and operations over them. Quantities make it possible to get to know the real world so that the acquired knowledge is part of the exact world and the artificial formal language allows these knowledge to be represented as relations (they model the recognized realities of the real world) among the quantities. It is artificial (Newtonian) knowledge of the real world where a part of the real world is represented a chosen set of variables and mathematically described relationships between them. The set of selected variables is formed discreet a filter of knowledge of the real world. Quantities and artificial formal language are instruments of exact science, but they are also the only instruments that have artificial intelligence available to form a model of a component of an exact world, that is, viewable and executable on a computer. It follows from the fact that the knowledge used in the artificial intelligence system must be obtained by the method of exact science, ie as the relations between the quantities represented by the formal language (mathematics, formal logic, programming languages) eg in expert systems.
It is necessary to take deeper insights into the paths of artificial intelligence led by modeling the activity of neural networks. Great hopes are being put into it, but it is necessary to determine to what extent this model can bring the real human brain into action. Human knowledge uses knowledge as a filter vagueness and the subsequent processing of information is inseparably accompanied by this intra-mental (internal) vagueness, to a large extent subjective and emotional. These are the principles far removed from the principles that an exact world can offer. From the previous one, we know that the exact world must use as a filter of knowledge (instead of vagueness) a discrete filter made up of a set of variables chosen for the role of the representatives of that part of reality. Previous views have suggested that the model of the neural network will accurately model the real human brain sufficiently to overcome the problem of the immense complexity of the structures of the human brain, that is, on the basis of a deeper understanding of these structures, that complex description will be available. This view is based on experience with, for example, modeling of physical processes, where increasing the number of variables and relationships between them usually increases the accuracy of the model's compliance with reality. For internal mental processes modeled by models of human brain structures, this is not the case. Here is the target state of a model generating processes that process information infused with intra-psychic vagueness, subjectivity and emotionality, ie processes with properties found outside the exact world. Let's recall that no matter how complex the brain structures are, is still found in the exact world and the processes it describes (as well), and can not leave him. In other words, any increase in the number of variables and relationships used to describe the structures of the human brain does not approach this description of a real brain processing information inherently built on intra-mental vagueness, subjectivity, and emotionality. It is an impossible overflow from an exact world to a real one with inherent vagueness. It can be said that intra-psychic processes inherently linked to intra-mental (internal) vagueness can not be modeled by an exact world with forbidden internal vagueness. Intra-psychic processes differ inherently from their inherent connection to intra-psychic vagueness from all the processes known in the real world such as physics, chemistry, etc. In other words, these processes are not understandable by the exact science method, they are not modelable for the exact world. The exact world is too weak in this case, because the above-mentioned requirement of its exactness is very strict and it departs it from the principles of the real world when intra-psychic processes are inherent vagueness. Here are the limits of the possibilities of exact science, including mathematics and exact computer-machines. As a search for a new path, attempts are made to use live brain structures such as rats, instead of computer models of neural networks, connected via a suitable interface in artificial intelligence systems, components of cognitive robots.
The exact world, which is the strongest and indispensable tool of scientific knowledge, has not only the internal cracks described by Gödel's sentences, the use of (not quite correct) inductive processing of data obtained in the real world to create hypotheses about it, but also the outer limits given by the prohibition of internal vagueness. The discrete filter of knowledge (and so the exact world) greatly restricts the area of knowledge. The vagueness filter allows you to know vaguely many, the discreet filter allows you to know just a few, more precisely, only a tiny part of the real world.