Читать книгу Industrial Internet of Things (IIoT). Intelligent Analytics for Predictive Maintenance онлайн

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AI is related to application areas that involve expert systems or systems based on knowledge, natural language comprehension/translation, intelligent systems/learning, speech comprehension/generation, automatic programming, or even image and scene analysis in real time, among many others. Therefore, it can be evaluated that the technological AI field aims to emulate human beings’ capabilities including problem-solving, understanding natural language, computer vision, and robotics, considering systems for knowledge acquisition, and even knowledge representation methodologies [15].

To obtain the full value of AI, Data Science is necessary (ssss1), consisting of a multidisciplinary field that employs scientific methods to collect and extract value from data, combining skills such as statistics, probabilities, frequency of occurrence of events, observational studies, and computer science, with business knowledge to analyze data gathered from distinct sources [29, 30].

The central principle of AI technologies is to replicate, and then exceed, the processes and conduct humans perceive, notice, see, and react to the world, fueled by several forms of Machine Learning techniques that recognize patterns in data to allow prognosis and predictions. Propitiate a better comprehensive understanding of the wealth of available data, information, and predictions to automate overly complex or ordinary tasks, improving productivity and performance, automating tasks or processes that previously demand human energy, and also making sense of the data on a superhuman scale [31].