The rise and fall and rise again of artificial intelligence

This academic article evaluates the historical trajectory, structural shifts, and macro-evolutionary cycles of Artificial Intelligence (AI) from its foundational inception to its contemporary ubiquity. The development of AI has not been a linear progression; rather, it is characterized by distinct, volatile cycles of intense technological enthusiasm—termed "AI Springs"—punctuated by systemic funding contractions and institutional disillusionment, known as "AI Winters." To map these socio-technical transitions, this study analyzes the history of AI across three core evolutionary epochs: (1) The Symbolic Era and First Disillusionment (1950s–1970s), dominated by hand-coded, logic-based expert systems that eventually collapsed due to combinatorial explosions and hardware boundaries; (2) The Connectionist Resurgence and Second Contraction (1980s–1990s), driven by multi-layer backpropagation and commercial expert system platforms, which ultimately failed to scale due to limited training data and primitive processing speeds; and (3) The Contemporary Deep Learning and Generative Renaissance (2000s–Present), catalyzed by massive datasets, parallel graphic processing unit (GPU) computing architectures, and transformer networks. The analysis highlights how the current "rise" differs fundamentally from previous iterations due to deep commercial integration and scalable neural network foundation models. Ultimately, this research provides a historical-epistemological framework and structural analysis for computer scientists, technology historians, and foresight engineers looking to anticipate future structural inflections in intelligent systems.