Information systems of artificial intelligence

Information systems of artificial intelligence

Information systems of artificial intelligence are one of the latest analytical technologies based on certain models, algorithms, and mathematical theorems that allow, based on a set of initial data, to evaluate the values ​​of unknown parameters and characteristics.


The world economic system is becoming more and more global and international. The dynamics of business are constantly increasing, and competition and the struggle for resource reserves are increasingly leading to a situation where, with a shortage of time, the only correct practical decision should be made. To do this, the manager must, in the presence of many uncertainties, analyze the situation in a short time, determine the versions of decisions, perform a risk assessment and take full responsibility for the developed and implemented decisions. It is very difficult to carry out all these actions using only “manual” methods, and therefore the risks of developing incorrect decisions are very significant. For this reason, formalized methods for making decisions in the presence of uncertainties have been developed, which can be described using fuzzy logic.

5 examples of artificial intelligence in everyday life

Information systems of artificial intelligence

There are significant differences between crisp and fuzzy logic. With a clear logic, the expected consequences should in any case unambiguously follow the stated message, in the case of setting clear rules for the implementation of the conditions. For example, “if A, then B”, or “if A and B, then C.”

In fuzzy logic, the limits of the action of conditions are either not specified, or not specified at all. For example, “if A, then in the time interval from T1 to T2, B may be significantly greater than C, but maybe almost equal to C.” Everything is determined by the initial and current conditions, which can quickly change even within the specified time interval.

Algorithms that analyze these situations usually consider different scenarios for the likely development of events and evaluate the risks of all options. That is, information systems in this version, in addition to typical actions for collecting, storing, and broadcasting information data, must-have components that perform processing and information analysis in a multivariant format. Since the development of a business situation is usually determined by a whole set of parameters, and the model that describes these situations is almost always non-linear, the actual problems can often be reduced to multivariate estimation and optimization problems in a non-linear format.


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