Exploring AI Learning Paradigms
Kirti Seth, Nidhi Gupta, Ashish Seth · 2026
This chapter attempts to show the big ways computers learn. First, supervised learning may mean that the system receives clear examples with tags so that it can guess new things. In unsupervised learning, data without tags are analyzed, and hidden groups are identified. A mix called semi-supervised learning uses some tagged and some plain data, which could be useful when labels are cheap. Reinforcement learning is a type of trial-and-error in which an agent receives rewards; therefore, it learns what works best. In addition, self-supervised learning has grown rapidly. It creates its own labels from raw input, allowing large models, such as Transformers, to be trained without human intervention. Finally, generative learning pushes artificial intelligence (AI) beyond merely recognizing patterns; it can actually write sentences, draw pictures, or compose music. These different styles hint at a move toward more independent and creative machines, although some critics say we still lack true understanding in modern AI research contexts.