Artificial Intelligence: Data Representation and Problem Solving

Artificial Intelligence: Data Representation and Problem Solving

Artificial Intelligence (AI) has moved far beyond laboratory research to become an essential force shaping industries, driving innovation, and unlocking new possibilities across healthcare, creativity, business, and daily life. However, many still perceive AI as a complex and inaccessible field reserved only for specialists.

This book provides an intuitive yet comprehensive guide designed specifically for readers seeking practical knowledge and hands-on experience rather than theoretical complexity. It introduces the four core AI capabilities—Representation, Classification, Generation, and Exploration—and demonstrates how these can be applied across various data types including text, images, sound, and numeric data.

Inside, you’ll find structured explanations and guided implementations to help you:

Understand how AI systems encode, process, and interpret diverse types of data.
Explore practical methods for classifying information through head-based and prompt-based techniques.
Generate new, creative content with techniques such as Autoencoders, GANs, and Large Language Models.
Discover how AI agents learn, make decisions, and solve problems using reinforcement learning.
Ideal for students, researchers, aspiring AI professionals, and non-technical practitioners, this book requires no extensive programming background, offering clear, step-by-step practices for text, image, sound, and numeric data. By the end, readers will confidently see AI not just as a technical tool, but as a collaborative partner in solving real-world problems.