(untitled) (The International Journal of Engineering & Information Technology (IJEIT), 2024)
Artificial intelligence has undergone a sea change since the introduction of deep learning, bringing innovations that were previously only seen in science fiction to life. Artificial neural networks (ANNs), a notion with decades-long roots, are at the center of this paradigm shift. This study explores the evolutionary path from the first perceptron’s to the complex, multi-layered architectures of today, exploring the historical link between ANNs and deep learning. The paper reveals the key discoveries, scientific advances, and theoretical breakthroughs that have sparked the evolution of neural network research into the deep learning algorithms that are now the foundation of many contemporary artificial intelligence applications. This is done through a thorough review and analysis of the literature. The study also looks at the computational and socioeconomic elements that have helped or hindered this development. The paper provides a sophisticated understanding of the mutually beneficial growth of ANNs and deep learning by clarifying how their interconnected evolution has developed, emphasizing how earlier breakthroughs have paved the way for present-day achievements and the promise of artificial intelligence.