RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE MACHINE LEARNING AND NEURAL NETWORK

Usman Ahmad, Abdul Shakoor, Sara Abbasi, Muhammad Rashid, Humera Omer Farooq and Muhammad Shahid Khan
Additional contact information
Usman Ahmad: Lecturer, Department of Creative Computing Bath Spa University Academic Centre RAK, UAE
Abdul Shakoor: Assistant professor, Department of Civil Engineering Abasyn University Islamabad
Sara Abbasi: Department of Computing and Technology, Iqra University Islamabad, Pakistan
Muhammad Rashid: International Islamic University Islamabad. Department of Software Engineering. Faculty of computing
Humera Omer Farooq: Assistant Professor College of Art & Design , University of the Punjab
Muhammad Shahid Khan: Lecturer in IT, Mardan

Bulletin of Business and Economics (BBE), 2023, vol. 12, issue 2, 142-148

Abstract: The foundation of modern technological development is the complex interaction between Artificial Intelligence (AI) Machine Learning (ML) and Neural Networks (NN). In order to better understand the dynamic dependencies that characterize these areas, this study takes a multifaceted approach that combines quantitative analysis and a thorough literature assessment. The study explores three hypotheses by building a synthetic dataset that includes variables including trends in AI investment measures for measuring the performance of ML algorithms, and complexity of NN design. The findings support the idea that investments in AI research encourage innovation in ML algorithms, demonstrating a symbiotic relationship between AI and ML breakthroughs. The evolutionary synergy between these components is further highlighted by a quantitative association between enhanced ML algorithm performance and increased Neural Network complexity. The analysis further supports the relationship between increased neural network performance and funding in AI research revealing the profound impact of AI on NN capabilities. These empirical findings are consistent with well-established theoretical frameworks and provide a deeper appreciation of the interdependence that underpins the advancement of technology. The study aids stakeholders in navigating the dynamic environment of cutting-edge technology by fostering a holistic understanding of how AI ML and NN jointly affect innovation.

Keywords: Artificial Intelligence; Machine Learning; Neural Networks (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:12:y:2023:i:2:p:142-148

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