A field encompassing methods and capabilities
Artificial intelligence (AI) is a field of computer science and a set of methods that enables computer systems to perform tasks associated with intelligence. These include learning from data or experience, interpreting information, recognizing patterns, and selecting actions to pursue defined goals. The term does not refer to a single technology: AI systems range from simple algorithms to complex artificial neural networks.
Rather than fully reproducing human thought, these systems perform specific tasks using computational instructions and models. How they are built, how they learn, and how they are applied depend on the problem they are intended to address. A voice-recognition tool and a system that recommends an action can both be classified as AI, even though their functions and methods differ.
How AI learns and what it can do
In many systems, learning involves identifying patterns in examples and using what has been learned to interpret new inputs or produce results. Deep learning and neural networks are approaches used in this field; natural language processing enables systems to work with human language. Not all AI learns in the same way, however: some solutions run algorithms defined for a task, while others use models that can adjust their responses based on data.
Applications of AI include speech recognition, language translation, computer vision, large-scale data analysis, and the generation of predictions or recommendations. The field also covers tasks such as playing chess, proving theorems, solving mathematical problems, composing music, creating images, and supporting disease diagnosis. This variety reflects the potential to automate intellectual activities, not a universal ability to understand every situation.
A field in evolution
AI research aims to develop systems that perceive aspects of their environment and use information to take actions directed toward goals. What these systems can do depends on their method, data, and intended task; performing a complex activity does not, by itself, mean that a machine reasons like a person in every sense.
AI has been a research field since the 1950s and became an academic discipline in 1956. Its history includes periods of optimism as well as phases of stagnation and reduced funding, known as “AI winters.” Interest grew from 2012 with advances in deep learning and accelerated after 2017. These shifts help explain the field’s recent expansion, as it continues to evolve with new methods and applications.