Riesgo en el uso de modelos de Inteligencia Artificial. Análisis basado en modelo causales

Abstract:

In the different contexts in which the human being develops, it has been observed that, with the massive use of digital platforms, the role of data analysis has increased, which has produced an increased interest in using them through browsers. Under artificial intelligence, both in the use of software, the storage of information and the parallelism with the different social networks, they have generated a product of high pbkp_redictive power, with automatic and semantic interpretation according to the data entered. This study was conducted with a quantitative approach, with descriptive criteria and has considered the modeling of learning, which has allowed us to anticipate decision making and has contributed to educational pbkp_rediction. The objective was to establish the risks of using fuzzy AHP TOPSIS models for deep learning in browsers. It was concluded that the importance of using deep learning and useful and effective neural networks can benefit companies, for this reason, their use is projected in average users, but the more demand for this type of technology, collateral risks may also appear.

Año de publicación:

2023

Keywords:

• Redes Neuronales
• Inteligencia Artificial
• Aprendizaje profundo
• NAVEGADORES
• Modelos De Aprendizaje

Fuente:

rraae