Symbolic AI uses explicit rules and symbols to represent knowledge, like a detailed instruction manual. Connectionist AI (like neural networks) learns patterns from data, mimicking brain connections. Early AI focused on symbolic methods; modern AI is dominated by connectionist approaches, especially Deep Learning. Each path has distinct architectures, workflows, and strengths for different types of problems. Understanding these "two paths to machine intelligence" is crucial for appreciating AI's evolution and capabilities.