The attention mechanism has fundamentally reshaped natural language processing (NLP), enabling models to capture complex linguistic relationships with unprecedented accuracy. Introduced prominently in Vaswani et al. (2017), attention allows models to focus on relevant parts of input sequences, enhancing performance in tasks like machine translation and sentiment analysis. This essay
Natural Language Processing (NLP) encompasses a variety of tasks, each with distinct methodologies and applications, including Named Entity Recognition (NER), sentiment analysis, classification, machine translation, summarisation, and information extraction. These tasks underpin numerous real-world applications, from virtual assistants to automated content analysis. Named Entity Recognition involves identifying and classifying named
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,
Natural Language Processing (NLP) is a pivotal subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. By enabling machines to understand, interpret, and generate human language, NLP bridges the gap between human communication and computational systems. At its core, NLP combines principles from computer
Neural networks represent one of the most significant developments in machine learning, offering computational models inspired by the biological neural networks of animal brains. As specialised models within the broader machine learning paradigm, neural networks have evolved from theoretical constructs to practical tools that drive modern artificial intelligence applications. The
Machine learning (ML), a fundamental pillar of artificial intelligence, equips computational systems with the capacity to derive insights from data and refine their performance autonomously. Its profound influence permeates diverse domains, encompassing medical diagnostics, financial modelling, and autonomous systems. This essay offers a critical examination of the three principal paradigms
Machine learning (ML), a subset of AI, focuses on algorithms that enable systems to learn patterns from data and make predictions or decisions. Mitchell (1997, 2) provides a foundational definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance
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
Having explored the definitional complexities and historical evolution of AI, we now examine how these developments have crystallised into systematic taxonomies. The progression from symbolic systems to contemporary neural architectures—traced in the previous sections—has given rise to increasingly sophisticated attempts to classify AI systems according to their capabilities
Having examined the diverse definitional approaches to AI, we now turn to the historical trajectory that has shaped these conceptual frameworks. The theoretical foundations discussed above—particularly Turing's behavioural criterion and the Dartmouth Conference's formalisable intelligence hypothesis—provided the intellectual scaffolding for AI's subsequent
The concept of Artificial Intelligence is characterised by conceptual plurality, reflecting divergent disciplinary perspectives and evolving technological capacities (Bringsjord & Govindarajulu 2024). Whilst numerous definitions have been proposed, there is no universally accepted formulation. Instead, understandings of AI range from attempts to replicate human cognition to approaches centred on rational
