The concept of generative artificial intelligence (GenAI) has long been employed in academic and technological discourse; however, it only began to garner widespread recognition in the mid-to-late 2010s, a period marked by pivotal breakthroughs in AI development that fundamentally transformed the landscape of its potential applications. Early in the decade,
The Transformer architecture, unveiled by Vaswani et al. (2017), has catalysed a seismic shift in natural language processing (NLP), redefining the boundaries of language modelling and accelerating advancements in artificial intelligence (AI). By introducing a novel approach that prioritises parallel computation and attention-driven processing, the Transformer has surpassed traditional models,
The transformative potential of Natural Language Processing (NLP), as a cornerstone of artificial intelligence, lies in its ability to enable machines to understand and generate human language, facilitating advanced human-computer interaction and knowledge extraction. However, the complexity of human language presents significant obstacles, particularly in managing linguistic ambiguity, contextual nuances,
The evolution of learning machines, a cornerstone of Artificial Intelligence (AI), represents one of the most transformative developments in modern science and technology. From rudimentary rule-based systems to sophisticated pattern recognition models capable of processing vast datasets, the trajectory of AI reflects both technological innovation and shifting conceptual paradigms. Defining
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
