European Journal of Mechanical Engineering Research (EJMER)

EA Journals

Unleashing the Power of Big Data: A Catalyst for Advancements in Artificial Intelligence

Abstract

In an era marked by the rapid accumulation of digital information, the interplay between big data and artificial intelligence (AI) has become a catalyst for transformative advancements. This abstract provides an overview of our comprehensive exploration into the profound impact of big data on the evolution of AI. This paper investigates how the volume, variety, and velocity of big data are empowering AI to reach unprecedented levels of sophistication. Big data-driven innovations, including deep learning, natural language processing, and predictive analytics, are redefining industries and driving efficiency, personalization, and data-driven decision-making. By understanding the intricacies of this relationship, this research aims to serve as a compass for AI practitioners, researchers, and policymakers. The synergistic journey of big data and AI holds the key to a future of unprecedented innovation and societal transformation, provided it is navigated with a commitment to responsible, ethical, and transparent development. The fusion of big data and artificial intelligence (AI) represents a transformative synergy that is reshaping industries and research alike. This abstract provides an overview of a comprehensive study that explores the pivotal role big data plays as a catalyst for advancements in AI. By analyzing how large-scale data is harnessed, processed, and integrated into AI systems, this research showcases the profound impact of this collaboration.

Keywords: Artificial Intelligence, Autonomous Systems, Big Data, Cognitive Computing, Deep Neural Networks, Finance, Healthcare, Recommendation systems

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This work by European American Journals is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License

 

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Email ID: editor.ejmer@ea-journals.org
Impact Factor: 7.01
Print ISSN: 2055-6551
Online ISSN: 2055-656X
DOI: https://doi.org/10.37745/ejmer.2014

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