Artificial Intelligence: Learning and Limitations

Artificial Intelligence, IA, is a new technology with enormous potential to change the world forever as we know it. It finds applications in many fields of human activity, including services, industry, education, social networks, transportation, among others. However, there is little discussion about the accuracy and reliability of such technology, which has been used in situations where human life depends on its decision-making process, which is the result of its training, one of the stages of development. It is known that the learning process of an Artificial Intelligence, which can use the Artificial Neural Networks technology, presents an error of the predicted value in relation to the real value, which can compromise its application, being more critical in situations where the user's security is a major issue. In this article, we discuss the main technologies used in AI, their development history, considerations about Artificial Neural Networks and the failures arising from the training and hardware processes used. Three types of errors are discussed: The Adversarial Examples, the Soft Errors and the Errors due the lack of Appropriate Training. A case study associated with the third type of error is discussed and actions based on Design of Experiments are proposed. The objective is to change the way the AI models are trained, to add some rare conditions, and to improve their ability to forecast with greater accuracy in any situation