AI Paradigms: From Symbolic Rules to Neural Networks and Intelligent Agents

Cross-posted from Zeromath. Original article: https://zeromathai.com/en/artificial-intelligence-paradigm-en/ AI is not one fixed idea. It has evolved through several paradigms, and each paradigm reflects a different answer to the same core question: what is intelligence, and how should a machine implement it? If you only look at today’s models, AI can feel fragmented. But if you look at the major paradigms side by side, the field becomes much easier to understand: symbolic AI focused on rules, connectionism focused on learning from data, and agent-based AI focused on interaction with an environment. This article connects those paradigms into one structure and shows what each one contributed, where each one failed, and why the next one emerged. Related topics: Symbolic AI: https://zeromathai.com/en/classical-ai-symbolic-ai-1g-en/ Connectionist AI: https://zeromathai.com/en/connectionist-ai-en/ Knowledge Base: https://zeromathai.com/en/knowledge-base-en/ Inference Engine: https://zeromat