Artificial Intelligence (AI): Definition, Different Methods and Applications

Guest Author: Kumar Swamy Banothu | Category: Internet

Artificial intelligence: Definition, Different methods and Applications

General Definition: Artificial Intelligence (AI)

AI is a branch of computer science, which is concerned about the study of mechanisms of intelligent human behavior. It is done by simulation using artificial artifacts, usually with computer programs on a calculator (computer simulation).

Realistic definition:

The general meaning of AI suffers from the fact that the terms "intelligence" and "intelligent human behavior" themselves are not yet well defined and understood. On the other hand, AI is also a tool that can be used to empirically test theories of intelligence. The execution of programs on computers represents empirical experiments. In contrast to other branches of computer science, artificial intelligence is a practical discipline. This type of definition of AI raises further questions; above all, it leads to the paradox of science, whose primary goal is to define itself.

The Different Methods and Applications of Artificial Intelligence

There are various methods and applications in artificial intelligence, with the following areas: methodology, special requirements, deduction systems, automatic programming, and understanding of natural language, computer vision and robotics, a method of learning, support in the field of education (Intelligent Computer-Aided Instruction) and Heuristic Search.

In the field of Artificial Intelligence (AI), the most critical areas are the representation of knowledge as well as the effective use of the presented instruction.

The unique requirements of the linguistic means of expression to create the Artificial Intelligence (AI) programs, especially the necessary symbol processing, require select (KI) programming languages.

Thus, they provide access to certain forms of knowledge representation and enable knowledge evaluation, such as built-in methods of reasoning.

Automatic Proofs

Furthermore, artificial intelligence includes the employment of "automatic proofs" for mathematical theorems, which in turn linked to the field of application of deduction systems. The goal of the developed deduction systems is to enable the querying of the database administrator systems, whereby the database systems, in turn, based on the relative model so that they are responsible for the declining database queries.

The deduction systems also have a connection to automatic programming. Thus, based on formal specifications, a verification of the programs can be carried out automatically through the deduction system. Electronic programming also includes automatic generation of executable applications from official specifications, as well as clear evidence of hardware components such as integrated circuits and hardware.

Also, the methods of applying natural language as well as language processing, belong to artificial intelligence. Here, among other things, the results of linguistics are used, for example, from syntactic theory. Thus, speech recognition is one of the central tasks in this area, besides language analysis.

The more advanced artificial intelligence technologies such as computer vision and robotics are mainly concerned with the data interpretation of the real physical environment.

Computer vision

Computer Vision is a program that understands the areas of image understanding, such as gray scale analysis, scene analysis, which involves recognizing geometric objects from line drawings and perceiving shapes. In principle, the focus of computer vision is describing the meaning of a scene in terms of content, for example, by constructing a semantic network.

In order to create a recognition on the objects, the computer vision in robotics used. In this classic field of application, the planning and controlling robot actions play a super ordinate role.

The focus of the methods of learning and cognitive models are the peculiarities of human intelligence. The cognitive models accomplish here the creation of computer programs that create a simulation of human problem-solving behavior. The area of learning is about methods that should enable computer programs not only to function based on already existing knowledge but also to provide previously known problems and their solutions for knowledge enhancement.

Whereas in learning to focus on the transfer of human learning ability to the computer, within the ICAI (Intelligent Computer-Aided Instruction) application area, the aim is to provide people with assistance in the learning process, with particular emphasis on educational insights.

Heuristic Search

The last important branch of Artificial Intelligence is Heuristic Search. It is a methodology field that comes from the initial experiments of Artificial Intelligence Technology (AI). The heuristic search revolves around the problem in the development of game programs, such as the search for "good" moves. And just because of the combination of possibilities, it can quickly lead to an explosion of the number of possible steps. Heuristics are used to narrow the search space so that the corresponding game situations can research faster and better.

If the subject of AI is everywhere, many still talk about it in the future. This is for example the case with Rank Brain, developed and exploited by Google in the algorithm of its search engine. An algorithm based on AI, Rank Brain goes well beyond the chosen words, seeks to understand the intention of the user and deduces the most relevant pages, beyond their simple semantic dimension. Henceforth, it is the quality of the content that outweighs the keywords.

The growing emergence of AI underscores how much today marketing is built on perfect customer knowledge. Without data, nothing is possible. It is the sine qua non for being able to build consistent marketing strategies.

With digital marketing everything is now the opportunity to collect and exploit the "data". By observing and memorizing the behaviors of each user, by listening to him or by exchanging with him the websites or on the social media. By sharing and aggregating the data to that of others to be able to better understand or deduce who the customers are and what their needs are.

Summary:

In this context, the AI brings a supplement of intelligence, soul and personalization. Its power of calculation, analysis and learning brings us closer to the needs of each client. He is able to really personalize the pages and offers, taking into account who is the customer, his need identified or anticipated, the "device" used. Better, with the AI, the marketer can imagine a personalized storytelling, able to create emotion and thus a proximity between the brand and its customer.

Guest Author: Kumar Swamy Banothu