Artificial Intelligence Techniques

Artificial Intelligence Techniques

1. 1. Concept

   • Artificial intelligence techniques refer to different fields of research and application development of artificial intelligence. AI is the type of intelligence that shows the different artifacts created by humans, usually referred to computer systems. But it also refers to the field of scientific research in the intention of creating environments that make use of said intelligence.AI is intended to come close to the workings of the human mind. And it is used when it is considered useful to incorporate into a computer system knowledge or behavior in the face of events that would be more typical of a human being.
     • 1. Machine learning or automatic learning Machine Learning is the branch of science that seeks to develop artificial intelligence techniques that allow computers to learn by themselves. To do this, programs are created that can generalize certain responses from unstructured information, which is provided as examples. With this, knowledge is induced by the computer. 2. Fuzzy logic or fuzzy logic Known as heuristic logic. This technique affects the relative of an observed scenario as a differential position. It is a type of logic that takes two random values, contextualized and related to each other. For example, consider a person of 2 meters as tall having previously taken the value of a person of one meter as short.
       • 3. Artificial life It consists of the study of life and artificial environments that show qualities of living beings in simulated environments. One of the artificial intelligence techniques with the most projection for the future in the field of research. 4. Expert systems It refers to an information system that is based on the knowledge of a highly complex and very specific application area. It serves as a consultant and expert assistant for the users of its interface.
         • 5. Data mining or data mining This technique consists of the discriminated extraction of information that is implicit in the data handled. Such information, previously unknown, is destined to be used in some other process. Data mining probes, prepares and explores the data in order to extract some information that is hidden in it. 6. Bayesian Networks Also known as belief networks, these networks are a multivariate probabilistic model, which relates a set of random variables using a directed graph to explicitly indicate chance influence. With a probability updating engine called Bayes' Theorem, these networks become a very useful tool when calculating probabilities in cases of new evidence. It is one of the types of networks that are called casual type.

2. 2. Main characteristics

   • 1. Elimination of monotonous tasks It implies that an artificial intelligence system continues to do the task as ordered, no matter how many times it has to do it. This also leads to minimizing errors and human costs. 2. Handling a large amount of data It represents one of the most relevant aspects of the characteristics of artificial intelligence. Artificially intelligent systems manage large amounts of data. Even a small company of about fifty employees has huge amounts of data to analyze, which can be handled thanks to these systems. Added to this, information from multiple sources is stored.
     • 3. Imitation of human cognition It is one of the most outstanding characteristics of artificial intelligence. The reason? Well, through these systems the way in which the human mind thinks and solves problems is imitated. So inferences are made, the environment is interpreted and decisions are made (Mazurek, 2019). It is possible that not everything can be identical; however, developers and scientists are working hard to fully achieve this feature. 4. They are futuristic Businesses employing artificial intelligence can count on the perception of the environment to find opportunities. For example, an autonomous vehicle records the speed of nearby cars and tries to execute patterns similar to those found in traffic. Through technologies such as machine learning, data can be fed into algorithms and a certain goal can be achieved in various scenarios.

3. 3. Objective and/or function

   • Thus, the global objective is specified in more specific objectives, which are shown in the following table: Objective 1: Acquire advanced training, of a specialized and multidisciplinary nature, aimed at promoting the initiation of research tasks in Artificial Intelligence. Objective 2: Provide a greater degree of knowledge in Artificial Intelligence techniques and methods to be able to address and solve problems of a scientific and technological nature through research (research to innovate). Objective 3: Create innovative programs that are capable of combining the specialized nature of the training with the creativity that underlies original, active and productive lines of research (innovate to investigate) in Artificial Intelligence. Objective 4: Train the student to be creative when approaching and solving problems of a scientific and technological nature through research in Artificial Intelligence.
     • The objectives described above are aimed at students acquiring, during their studies, a series of general and specific skills. The competences of the Master's Degree in Artificial Intelligence have been structured into three categories. The first includes general skills, some from a Royal Decree and, therefore, common to any master's degree in Spain; other proposals by the Polytechnic University of Madrid; and, finally, some included in the EURO-INF standard, which indicates competencies so that a degree can be accredited as a Master in Computer Science. In a second category of competences are those related to the research orientation of the proposed degree, shared by all the research-oriented master's degrees of the Faculty of Informatics, and which are different from those shared by those with a professional orientation. In a third category will be the specific competences in Artificial Intelligence, which differentiate the proposed degree from any other research master's degree of
       • General skills CG1: That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ​​study.) CG2: That students are able to integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. CG3: That students know how to communicate their conclusions and the knowledge and ultimate reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way. CG4: That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. CG5: Organization and planning. CG6: Information management. CG7: Specification and performance of complex, poorly defined or unfamiliar computer tas