RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE MACHINE LEARNING AND NEURAL NETWORK

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.