#4550 AI Architectures and Applications
#4550 AI Architectures and Applications #4550 List and describe the progression of framework for machine learning and AI The evolution of machine learning (ML) and artificial intelligence (AI) frameworks mirrors the shift in how we instruct computers: moving from explicit, rule-based instructions to data-driven statistical models, then to massive neural networks, and finally to agentic, generative orchestration layers. Here is the chronological and technological progression of AI/ML frameworks. 1. The Symbolic & Rule-Based Era (1960s – 1980s) Before computers learned from data, AI was dominated by Symbolic AI and Expert Systems . The frameworks of this era focused on formal logic, explicit "if-then" rules, and processing symbols. LISP (1958) & Prolog (1972): While programming languages rather than specialized frameworks, they served as the foundational environments for early AI. Prolog introduced built-in logical deduction engines. CLIPS / JESS (1980s): Specialize...