Theoretical foundation of artificial intelligence in the development of mobile applications at the Institute of Admission and Leveling of the Technical University of Manabí
DOI:
https://doi.org/10.55204/trc.v3i2.e223Keywords:
artificial intelligence, mobile devices, big data, technologiesAbstract
Introduction: This article aims to theoretically support artificial intelligence in the development of mobile applications, which one of the greatest advantages of artificial intelligence is that it allows and expands the capabilities of other technologies, such as big data. In fact, it allows you to "make sense" of these vast amounts of structured and unstructured data.
Development: Advances in AI are already driving the use of big data with its ability to process vast amounts of data and deliver business and commercial benefits. Thanks to this, AI has established itself as an essential technology for the coming decades in areas such as transportation, education, health, and culture.
Practical applications or future lines of research: The methodology applied to the following research is bibliographical since it provides different concepts and theories of various expert authors on the subject. In conclusion, artificial intelligence is a branch of computing that seeks to simulate human intelligence in a machine.
Conclusions: Artificial intelligence systems work with algorithms, using techniques such as deep learning and machine learning to demonstrate intelligent behaviors.
Keywords: artificial intelligence, mobile devices, big data, technologies.
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References
Accenture (2017). Model Behavior. Nothing Artificial. Emerging Trends in the Validation of Machine Learning and Artificial Intelligence Models. Recuperado de: https://www.accenture.com/us-en/insight-emerging-trends-machine-learning
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Harvard Business Press.
Berné Valero, J. L., Garrido Villén, N., & Capilla Romá, R. (2023). GNSS. Geodesia espacial y Geomática. Colección Manual de referencia.
Brynjolfsson, E. & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company
Cepal. (2018). Datos, algoritmos y políticas. La redefinición del mundo digital. Recuperado de: https://www.cepal.org/es/publicaciones/43477-datos-algoritmos-politicas-la-redefinicionmundo-digital
Chimarro Amaguaña, J. D. (2020). Sistema integrado para la operación de un brazo robótico teleoperado en tiempo real mediante la plataforma Firebase con el uso de dispositivos móviles (Master's thesis, Quito, Ecuador: Universidad Tecnologica Israel).
Colle, R. (2017). Algoritmos, grandes datos e inteligencia en la red. Una visión crítica.
Delía, L. N. (2017). Desarrollo de aplicaciones móviles multiplataforma (Doctoral dissertation, Universidad Nacional de La Plata).
Fernández Vaca, D. A. (2022). El derecho al habeas data ya la seguridad digital frente a los delitos informáticos (Bachelor's thesis, Universidad de Guayaquil, Facultad de Jurisprudencia Ciencias Sociales y Políticas).
Iglesias de Castro, M. (2019). Intervención y efectos en Ian Hacking. Ene, 12, 27.
Lee, A. (2016). “The meaning of AlphaGo, the AI program that beat a Go champ”, Maclean’s, Toronto, Rogers Media, 18 de marzo. Recuperado de http://www.macleans.ca/society/science/the-meaning-of-alphago-the-ai-program-thatbeat-a-go-champ/.
Martén Saborío, S. (2023). El problema epistemológico de los Big Data en la producción de conocimiento científico.
Mckinsey Global Institute. (2017). What´s now and next in Analytics, AI and Automation. Recuperado de: https://www.mckinsey.com/featured-insights/digital-disruption/whatsnow-and-next-in-analytics-ai-and-automation
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
OECD (2016). Enabling the next production revolution: the future of manufacturing and servicesinterim report: Meeting of the OECD Council at Ministerial Level Paris. Paris.
Ramos Arocha, D. (2018). Aplicación móvil para la visualización de los eventos registrados en la plataforma de gestión de eventos de la UCLV (Doctoral dissertation, Universidad Central “Marta Abreu” de Las Villas).
Rao, A. S., & Verweij, G. (2017). PWC. Sizing the prize. ¿What’s the real value of AI for your business and how can you capitalise? Recuperado de: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligencestudy.html
Rahimian, V. & Ramsin, R. (2008, 6 de junio). Designing and agile methodology for mobile software development: a hybrid ethod engineering approach. Second International Conference on Research Challenges in Information Science.
Ryan, C. (2020). Civilizados hasta la muerte. Capitán Swing Libros.
Richard, N., Franceschi, Z. A., & Córdoba, L. (2021). La misión de la máquina. Técnica, extractivismo y conversión en las tierras bajas sudamericanas. Bononia University Press.
Teigens, V., Skalfist, P., & Mikelsten, D. (2020). Inteligencia artificial: la cuarta revolución industrial. Cambridge Stanford Books.
Toloza Mora, C. M. (2021). TG2_Prototipo aplicación interactiva soportada en la plataforma Android para reforzar el aprendizaje de los compuestos químicos.
Valverde Bourdié, S. (2019). Aplicaciones de la inteligencia artificial en la empresa.
Villaseca, D., & González, S. (2023). De Silicon Valley a tu negocio: Innovación, data e inteligencia artificial. Alpha Editorial.
Von Krogh, G. (2018). Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing. Academy of Management Discoveries, 4(4), 404–409.d DOI:10.5465/amd.2018.0084
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