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AI Business: Artificial Intelligence In Business Examples

AI Business: Artificial Intelligence In Business Examples, it is modern cognitive science, which began in a formal way in the fifties of the previous century, but before this time period. we find that a group of other sciences was concerned in one way or another with artificial intelligence, and in an indirect manner. By reviewing genetics, you will find what is related to intelligence in the field of studying the genes of scientists in an attempt to support their genetic intelligence.


Everything You Need To Know About Artificial Intelligence In Business. AI goals



Artificial intelligence in business


  • In physics, we find that all students undoubtedly have the feeling that all the good ideas were taken from Galileo, Einstein, Newton, and the rest of the scientists. it is necessary to study for many years until one of them can make a new discovery, on the other hand, artificial intelligence is still open to his study. New Einstein all of his time.
  • Or it is a branch of computer science in the sense of making intelligent behavior possible for humans. there is a need for a data system used to represent information and knowledge and algorithms that require it to draw a method through which this information is used. And programming languages are used to represent both information and algorithms.
  • Artificial intelligence is one of the main rules on which the technology industry is based at the present time, and the meaning of the concept of artificial intelligence has been abbreviated. (AI) can be clarified as the ability of a machine and a digital computer to perform specific functions that mimic and resemble those performed by intelligent objects. such as their ability to think or Their ability to learn through past experiences or other processes that need to be carried out with mental processes. artificial intelligence seeks to reach systems that are characterized by intelligence and behave like humans do in terms of learning and understanding.


Branches of Artificial Intelligence:


  • Learning from experience.
  • Search (search).
  • The logic of artificial intelligence (logical Al).
  • Reasoning (common sense knowledge and reasoning).
  • Ontology.
  • Epistemology.
  • representation.
  • Planning (planning).
  • Pattern recognition.
  • genetic programming.
  • Inference and conclusion.
  • Guidance (heuristics).


Core capabilities of AI business:


AI Business: Artificial Intelligence In Business Examples, No one knows the boundary between unintelligent and intelligent action. In fact, it would be unwise to suggest that there be a dividing line characterized by subtlety, but the main capabilities of AI are:


  • Generate new ideas with modern methods.
  • Recognize and understand unclear and conflicting sentences and phrases.
  • Responding flexibly and effectively.
  • Deduction of distinct signs between modes despite the similarities that link them.
  • Synthesizing modern concepts by taking old concepts and merging them with each other in new ways.
  • Exploiting favorable situations by chance.
  • Recognize the relative importance of the various elements of a given situation.
  • Finding similarities between situations despite the differences may isolate them.


AI goals


Goals of artificial intelligence:


  • Learn and benefit from past experiences.
  • The ability to handle complex situations.
  • Solve problems when there is a lack of important information.
  • Distinguish important information from others.
  • Act quickly and correctly.
  • Understanding and comprehension of visual images.
  • Treat symbols and letters.
  • The ability to be creative and imaginative.
  • Compliance with laws, regulations, and rules.

Artificial intelligence features:


Symbolic representation: 


The representation of the information through symbols, and this representation is close to the form of the individual's representation of the information he possesses in his daily life. This is one of the first features of artificial intelligence programs, as it deals with symbols that are not numerical and this is the opposite of what is familiar and acceptable in most computers of the current era. that deal with numerical quantities and numbers, and of course. there is nothing to prevent artificial intelligence programs from performing the arithmetic operations they are used to. As the values that are extracted are used at a high level for decision-making. this feature enables programs to deal with knowledge in a natural way. which contributes to performing Qualitative Processing instead of the usual digital processing known in the field of computers.


Experimental research:


AI programs tend to problems for which there are no solutions that we can find based on certain logical steps. As it follows the method of experimental research, these programs break into problems that do not have a known general solution method. and this means that the programs do not use sequential steps to find the correct solution. but they choose a specific method for the solution that is good while retaining the possibility of changing the method if it turns out that the first option does not lead To a quick solution. i.e. focusing on solutions that achieve the goal (Sufficient Solutions) and not confirming the existence of optimal or accurate solutions, as is the case in the current traditional programs.


Embracing and representing knowledge:


As one of the important characteristics of artificial intelligence programs is the use of symbolic representation in expressing information. and following experimental research methods in order to obtain solutions, artificial intelligence programs must possess in their construction a large base of knowledge that contains the link between cases And the results. unlike statistical programs, artificial intelligence programs guarantee a method of information representation. as they use a special structure to describe the knowledge, and this structure guarantees facts and relationships between these facts (Relationship) and the rules linking these relationships (Rules).


Unconfirmed or incomplete data:


Programs designed in the field of artificial intelligence must be able to provide solutions if this data is uncertain or complete, and this does not mean that we give solutions no matter.

How incorrect or correct the solutions are, but rather in order to perform It is good that it is able to provide acceptable solutions, otherwise, it becomes insufficient. 

another characteristic that artificial intelligence programs can do and their ability to find some solutions even if the information is not completely present at the time when the solution is needed. and the consequences of the lack of information integration cause conclusions less realistic or less worthy.


The ability to learn:


The ability to learn is one of the characteristics of intelligent behavior, and whether learning in humans is through observation or taking advantage of previous mistakes. artificial intelligence programs must be built on machine learning strategies, and the ability to improve performance by taking into account previous errors. this ability is related to the ability To generalize the information, draw similar and selective cases, and neglect some unnecessary information.


Inferencing:


It is the ability to elicit expected solutions to a specific problem from the reality of known data and previous experiences. especially for problems with which it is not possible to use traditional, familiar means of solution. This ability is achieved on a computer that stores all expected solutions, in addition to the use of laws, strategies or inference, and logical laws.