Artificial Intelligence: Learning and Limitations

Abstract

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

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

“…Deep machine learning models achieved significant levels of accuracy and high performance, although their implementation is more time-consuming and hardwaredemanding [36,54,55]. The feature extraction process was fully automated with deep machine learning methods because it was already built into the algorithm itself.…”

Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications

“…Currently, efficient natural language processing (NLP) methods are mostly based on neural network algorithms [4,5]. There is a wide variety of text analysis tasks that neural networks can solve with high accuracy, such as part of speech tagging [6], machine translation [7], authorship attribution [8], named entity recognition [9][10][11][12][13], entity relation extraction [14][15][16] and so on.…”

Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification

“…Furthermore, besides data classification tasks, this concept was later used as a stepping stone to enhance data insights in the field of Computer vision as it introduced detailed object classification and recognition in image/video datasets. Lastly, recent advances in the field of deep learning ANNS [5], [6], [7] focus on data classification and image recognition tasks via deep learning techniques [8], [9] in several fields. Most notably this includes e-commerce [10], finance [11], humanitarian aid [12], education [13], healthcare [14], and ecological informatics [15].…”