Examining the Mutualistic Interplay Between Neural Networks and Machine Learning in the Realm of Artificial Intelligence

EasyChair Preprint 12480

Abstract

In the field of Artificial Intelligence (AI), the symbiotic relationship between neural networks and machine learning has emerged as a focal point of study. This paper investigates the intricate interplay between these two fundamental components, elucidating their mutualistic dynamics and their collective impact on advancing AI technologies. Neural networks, inspired by biological neural systems, serve as the cornerstone of many machines learning algorithms, facilitating the processing and interpretation of complex data. Conversely, machine learning techniques empower neural networks by enabling them to learn and adapt from vast datasets, refining their performance over time. Through a comprehensive analysis of various applications and case studies, this study explores how the synergy between neural networks and machine learning algorithms enhances the capabilities of AI systems in diverse domains, ranging from computer vision to natural language processing. By delving into the symbiotic relationship between these foundational elements, this research aims to deepen our understanding of AI development and inspire novel approaches for harnessing their combined potential to address real-world challenges.

Keyphrases: Applications, Artificial Intelligence, Interplay, Natural Language Processing, Symbiotic Relationship, advancements, computer vision, machine learning, neural networks

@booklet{EasyChair:12480,
  author    = {Asad Ali},
  title     = {Examining the Mutualistic Interplay Between Neural Networks and Machine Learning in the Realm of Artificial Intelligence},
  howpublished = {EasyChair Preprint 12480},
  year      = {EasyChair, 2024}}