Artificial intelligence (AI) is the branch of computer science concerned with building machines and software that perform tasks which, when done by humans, are understood to require intelligence, such as perceiving the environment, understanding language, solving problems, and making decisions. The field spans a wide range of approaches, from rule-based systems that encode human expertise directly to statistical models that learn behavior from data.
The term was coined for a 1956 workshop and has since described a rotating cast of techniques, expanding and contracting in public attention through cycles of hype and disappointment.
History
The field's founding moment is usually dated to the 1956 Dartmouth workshop, organized by John McCarthy, who coined the term "artificial intelligence" for the proposal. Early AI research emphasized Symbolic AI: logic, search, and hand-coded rules. This approach produced early successes such as theorem provers and, later, commercial expert systems in the 1980s, but it struggled with tasks that resisted explicit rules, such as vision and language. Funding collapses followed setbacks and inflated expectations, producing what researchers call an AI winter, notably after a critical 1973 UK government report and again in the late 1980s as expert systems failed to scale.
A parallel, initially marginal line of work pursued statistical and connectionist methods. The Perceptron (1958) modeled a single trainable neuron, but its documented limitations contributed to the first AI winter. Interest revived through the 1980s and, decisively, in 2012, when a deep convolutional network called AlexNet dramatically outperformed prior methods on a major image recognition benchmark, a result widely credited with starting the modern Deep learning era.
Deep learning matured through the 2010s with progress in vision, speech, and language processing. The 2017 Transformer (architecture) architecture enabled the training of much larger models on text, culminating in the Large language model paradigm. The Launch of ChatGPT in November 2022 made conversational AI broadly accessible and triggered a wave of investment and public attention often called the AI boom.
Approaches
AI research is often divided between symbolic approaches, which manipulate explicit representations of knowledge, and connectionist or statistical approaches, which learn implicit representations from data. Machine learning is the dominant modern paradigm, encompassing supervised, unsupervised, and reinforcement learning. Hybrid approaches, combining learned components with symbolic reasoning or search, remain an active research direction, particularly for tasks requiring reliable logical inference.
Applications and impact
AI systems are deployed across search, translation, recommendation, medical diagnosis, scientific research such as protein structure prediction, autonomous vehicles, and increasingly as general-purpose assistants and coding tools. The rise of capable AI agent systems since 2024, which combine language models with tool use and multi-step planning, has extended AI from answering questions to completing tasks independently.
Debates
Long-running debates concern whether current techniques can lead to Artificial general intelligence, a system with human-level competence across essentially all cognitive tasks, and what risks such systems might pose. Alan Turing's 1950 paper proposed an operational test for machine intelligence, now known as the Turing test, though its adequacy as a benchmark is widely disputed. Critics argue that fluent text generation does not imply understanding, while proponents point to emergent capabilities as evidence of genuine progress toward general intelligence.