Exploring the evolution of Artificial Intelligence methods
M. Nagabhushana Rao, Somepalli Himabindu, K. Aruna Kumari, Ms Nagalakshmi, A. Leela Sravanthi · 2025
The application of artificial intelligence (AI) has developed fast and has gone through a lot of transformations in the recent past. In this chapter to begin with, we have a bird-eye view of its development and try to ponder over it. We start by diving into what AI is all about and the many ways it’s put to use. Then, we delve into the past to trace the journey of AI from its early days with expert systems and rule-based setups to the cutting-edge strides we see today, like the breakthroughs in deep learning and reinforcement learning. We explore many AI techniques in depth, such as machine learning, how computers understand languages, how they see the world, expert systems, and fuzzy logic. We don’t stop there; we walk you through different kinds of machine learning, including the ones where the computer is taught, left to figure things out by itself, and situations where it learns through trial and error. Along the way, we give you an overview of neural networks, deep learning, and how these artificial brain-like structures help make decisions. We’re not just about tech but also about ethics and society. We weigh the impacts of AI on our lives and ponder the necessity of AI systems that are trustworthy, understandable, and responsible. The paper states that AI needs to be upfront, interpretable, and answerable. This comprehensive survey of AI methods is a goldmine for researchers, practitioners, and students dipping their toes into AI’s waters. It’s a roadmap through AI’s past, a guide to solving big, knotty problems, and a megaphone shouting about the importance of AI that’s honest, graspable, and responsible.