The Concept Of Artificial Intelligence And Its History

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The concept of artificial intelligence and its history

Milestones of the last decade

In 1996, the IBM computer called: Deep Blue manages to overcome the world chess champion Kaspárov. This computer was configured to learn the best 70000 plays in chess history.

In 2011, IBM created its first super computer to participate in the American game called: Jeopardy, which is characterized by questions and answers in questions. The potential developed by Watson is currently used by scientists due to the abilities of interpreting the natural language and self-learning of the environment.

In 2014, a conversational bot manages to win the Turing test for the first time in history, which is considered a great advance in scientific history for artificial intelligence. The bot called: Eugene Goostman, simulated being a 13 -year -old Ukraine child and was able to deceive 33% of the judges who was a human [18], was developed by the Russian Vladimir Veselov, the Ukrainian Eugene Demonko andThe Russian Sergey Ulasen.

In 2016, Google created a super computer called: Deep Mind [16] capable of playing Go (a Chinese board game), the first mention of this game was 548.C, and is similar to chess, however, it is a 19 × 19 board.

On October 18, 2016, Microsoft indicates that it has managed to overcome the barrier of the recognition of the human voice, they mentioned that their recognition system had an error rate (Wer) of 5.9 [19]. This means that a computer can recognize a conversation as people do.

Microsoft developed its own engine in charge of the Deep Learning: called Satori [13], has indexed one billion objects in 4 years and runs daily to capture and learn up to 28000 DVD of content, with this information, Microsoft Bing shows predictive data on: politics, Soccer and Olympics, on the other hand, IBM has its own engine called: Watson.

Within artificial intelligence, the field that studies patterns recognition is the so -called Machine Learning and is applied to solve two types of problems:

Types of AI

  1.  Act as a man. This is the definition of McCarthy, where the model to follow for the evaluation of the programs corresponds to the behavior of the people. The so -called Turing test (1950) also uses this reasoning. Elisa’s system, spoken Bot (program) is an example of this.
  2.  Reason as people. The important thing is how reasoning is done and not the result of this reasoning. The proposal here is to develop systems that reason in the same way as people do. Cognitive science uses this point of view.
  3.  Think rational. In this case, it is also defined in reasoning, but here it is based on the fact that there is a single rational way to think. Logic allows to formalize reasoning and is used for this purpose.
  4.  Act rationally. Again the objective is the result, but now objectively evaluated. For example, program in the game as chess will win. To achieve this goal, they want to calculate the result.

Computer artificial intelligence:

Computational intelligence implies iterative development or learning (P.eg. iterative modifications of parameters in connectionist systems). the knowledge is achieved based on empiric facts. Some methods of this branch include:

Neural networks:

They are a learning paradigm and automatic processing inspired by the way the nervous system of animals works. This is a neuron interconnection system in a network that collaborates to produce an output stimulus.

Diffuse systems:

Techniques to achieve reasoning under uncertainty. It has been widely used in modern industry and mass consumption products, such as washing machines.

Evolutionary computing:

Applies concepts inspired by biology, such as population, mutation and survival of the most suitable to generate successively better solutions for a problem. These methods in turn are divided into evolutionary algorithms and collective intelligence.  

Machine Learning or Automatic Learning is a branch of artificial intelligence that began to collect importance from the 80s. It is a form of AI that no longer depends on rules and a programmer, but the computer can establish its own rules and learn by itself.

ML systems work on large volumes of data, identify behavior patterns and, based on them, are able to predict future behaviors.

Deep Learning is a kind of Machine Learning algorithms based on neuronal networks, in fact, the functioning of these algorithms tries to imitate the brain.

conclusion

We can conclude that AI is a powerful tool that helps us achieve endless objectives, but the same, like any other tool, can be used in a negative way, therefore reaching a wrong result, followingthe same biases and errors that have been made when programming and planting the basis of an AI.

Thus, being just a tool, it cannot be concluded that it is bad or good, as can be seen in films such as those mentioned in the introduction. But, being a tool, it does not have its own intention, but that it is given due to the use that the person who is using it, thus being able to overcome the world chess champion, or help handle a car of a car ofAutonomous form.

Artificial intelligence needed to be treated as what it is, that is, a technological advance like any other, without specific intention.

However, it must be used wisely, being a powerful tool, its ill -intentioned use can have negative consequences.

Finally, it should be noted that the arrival of artificial intelligence, regardless of the vision one has, is something that has come to change everyone’s life. 

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