The conceptual foundations of Artificial Intelligence (AI) emerged in the 1940s, most notably through Alan Turing’s work on computation. In his influential paper Computing Machinery and Intelligence, Turing (1950) introduced the “Imitation Game” (now the Turing Test) as a pragmatic benchmark for determining whether a machine could mimic human intelligence. Rather than offering a strict definition, Turing reframed intelligence as a functional, computational process. The formal inception of AI as a discipline occurred in 1956 at the Dartmouth Conference, organised by John McCarthy, Marvin Minsky, and others. They proposed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” (McCarthy et al. 2006). This optimistic vision launched a wave of early research into symbolic AI, which sought to replicate human reasoning through logical rules and formal structures.
During the 1960s and 1970s, symbolic AI dominated the field. Systems such as the General Problem Solver (Newell and Simon 1961) demonstrated rule-based reasoning, while ELIZA (Weizenbaum 1966) simulated therapeutic dialogue through pattern matching. These programs illustrated early successes but were limited to narrow tasks and domains. The 1980s brought expert systems like MYCIN, which could diagnose infections using encoded medical knowledge (Buchanan & Shortliffe 1984). However, reliance on hand-crafted rules proved brittle. The field struggled with ambiguity, uncertainty, and scalability, leading to reduced funding and the first so-called “AI winter.” A significant paradigm shift occurred in the 1990s as AI researchers increasingly adopted statistical and data-driven approaches. Machine learning (ML), defined as the ability of systems to improve through experience, gained traction with the development of algorithms like decision trees and backpropagation for training neural networks (Rumelhart et al. 1986). IBM’s Deep Blue famously defeated chess champion Garry Kasparov in 1997, signalling AI’s potential in constrained domains (Campbell et al. 2002). However, generalisation remained a challenge.
The 2010s marked the advent of deep learning, enabled by advances in computational power, big data, and graphics processing units. A key breakthrough came with AlexNet, a deep convolutional neural network that outperformed all competitors in image recognition tasks (Krizhevsky et al. 2012). This catalysed a wave of progress across vision, speech, and natural language processing. Transformer-based models like BERT (Devlin et al. 2019) and GPT-3 (Brown et al. 2020) demonstrated human-like language capabilities. Reinforcement learning also matured, exemplified by AlphaGo’s 2016 victory over world Go champion Lee Sedol (Silver et al. 2016). These breakthroughs not only redefined AI’s capabilities, but also reignited debates about its ethical, epistemological, and societal implications—especially concerning transparency, resource concentration, and long-term risks (Crawford 2021; Marcus 2018).
In conclusion, the evolution of AI embodies more than just a sequence of technological breakthroughs; it represents a shifting understanding of intelligence itself. From Turing’s theoretical propositions to today’s deep learning systems and generative models, the field has progressively redefined what it means for machines to act, learn, and decide. Yet, each advancement brings new ethical, epistemological, and political questions that cannot be resolved through technical solutions alone. As AI systems increasingly mediate human experience, shape institutions, and influence global power dynamics, it is imperative that their development be guided not only by innovation but also by critical reflection, inclusivity, and democratic accountability.
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