Artificial Intelligence (AI) -

In Computer Science it is called Artificial Intelligence (AI) to the reasoning ability of a non-living agent. John McCarthy, coined the term in 1956, defined it:

"It is the science and engineering of making intelligent machines, especially intelligent computer programs."

The term "artificial intelligence" was formally coined in 1956 during the Darthmounth conference, but by then it had been working on it for five years in which many different definitions had been proposed which in no case had been fully accepted by the research community.

AI is one of the newer disciplines along with modern genetics.

Search of the required state in the set of states produced by the possible actions.

  • Genetic Algorithms (analogous to the process of evolution of DNA strings).
  • Artificial Neural Networks (analogous to the physical functioning of the brain of animals and humans).
  • Reasoning through a formal logic analogous to abstract human thought.

There are also different types of perceptions and actions, can be obtained and produced, respectively by physical sensors and mechanical sensors in machines, electrical or optical pulses in computers, as well as by inputs and outputs of a software and its software environment. Several examples are in the area of ​​systems control, automatic planning, the ability to respond to diagnostics and consumer queries, writing recognition, speech recognition and pattern recognition. AI systems are currently part of the routine in fields such as economics, medicine, engineering and the military, and have been used in a variety of software applications, strategy games such as computer chess and other video games.

Categories of Artificial Intelligence 

Stuart Russell and Peter Norvig differentiate these types of artificial intelligence:

  • Systems that think like humans: These systems try to emulate human thought; for example artificial neural networks. The automation of activities that we link with processes of human thought, activities such as decision making, problem solving, learning.
  • Systems that act as humans: These systems try to act as humans; that is, they imitate human behavior; for example robotics. The study of how to get computers to perform tasks that, for the moment, humans do better.
  • Systems that think rationally: that is, with logic (ideally), try to imitate or emulate the logical rational thinking of the human being; for example expert systems. The study of calculations that make it possible to perceive, reason and act.
  • Systems that act rationally (ideally): They try to emulate rational human behavior; for example intelligent agents. It is related to intelligent behaviors in artifacts.

Schools of Thought of AI

AI is divided into two schools of thought:

  • Conventional artificial intelligence
  • Computational intelligence

Conventional Artificial Intelligence

It is also known as Symbolic-Deductive AI. It is based on the formal and statistical analysis of human behavior before different problems:

  • Case-based reasoning: Helps you make decisions while solving certain specific problems and, apart from being very important, requires a good functioning.
  • Expert systems: Influence a solution through prior knowledge of the context in which it is applied and deals with certain rules or relationships.
  • Bayesian networks: Proposes solutions through probabilistic inference.
  • Artificial intelligence based on behaviors: they have autonomy and can be self-regulated and controlled to improve.
  • Smart process management: facilitates the making of complex decisions, proposing a solution to a certain problem just as a specialist in the activity would do.

Computational Artificial Intelligence

Computational Intelligence (also known as subsymbolic-inductive IA) involves interactive development or learning (eg, interactive modifications of the parameters in connectionist systems). The knowledge is achieved based on empiric facts. The concept of AI is still too diffuse. Contextualizing, and taking into account a scientific point of view, we could include this science as the person in charge of imitating a person, and not his body, but imitate the brain, in all its functions, existing in the human or invented on the development of an intelligent machine.

Sometimes, applying the definition of Artificial Intelligence, we think of intelligent machines without feelings, which hinder finding the best solution to a given problem. Many think of artificial devices capable of concluding thousands of premises from other given premises, without any kind of emotion has the option to hamper such work. In this line, you have to know that intelligent systems already exist. Capable of making wise decisions.

Although, for the moment, most researchers in the field of Artificial Intelligence focus only on the rational aspect, many of them seriously consider the possibility of incorporating emotional components as indicators of state, in order to increase the effectiveness of the intelligent Systems.

This means that intelligent systems must be equipped with feedback mechanisms that allow them to be aware of internal states, as is the case with humans with proprioception, interoception, nociception, and so on. This is critical both for making decisions and for preserving your own integrity and security. The feedback in systems is particularly developed in cybernetics, for example in the direction change and autonomous speed of a missile, using as a parameter the position at each instant in relation to the target that must reach. This must be differentiated from the knowledge that a system or computational program can have of its internal states, for example the number of cycles completed in a loop or loop in sentences type do ... for, or the amount of memory available for a given operation .

Intelligent systems, not taking into account emotional elements, allow them not to forget the goal they must achieve. In humans, forgetting the goal or abandoning goals for emotional disturbance is a problem that in some cases becomes incapacitating. Intelligent systems, by combining durable memory, goal setting or motivation, together with decision making and prioritization based on current states and target states, achieve extremely efficient behavior, especially in the face of complex and dangerous problems.


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