The simulation of human intelligence processes by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are examples of AI applications.
The idea of inanimate objects endowed with intelligence has existed since antiquity. Myths depict the Greek god Hephaestus forging robot-like servants out of gold. Engineers in ancient Egypt erected statues of gods, which were animated by priests. Thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used their times’ tools and logic to describe human thought processes as symbols, laying the groundwork for AI concepts like general knowledge representation.
As the hype surrounding AI has grown, vendors have been scrambling to promote how their products and services incorporate AI. What they call AI is frequently just one component of AI, such as machine learning. AI necessitates the use of specialised hardware and software to write and train machine learning algorithms. There is no single programming language that is synonymous with AI, but a few are popular, including Python, R, and Java.
In general, AI systems operate by ingesting large amounts of labelled training data, analysing the data for correlations and patterns, and then applying these patterns to predict future states. By reviewing millions of examples, a chatbot fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images.
This aspect of AI programming is concerned with gathering data and developing rules for converting the data into actionable information. The rules, known as algorithms, instruct computing devices on how to complete a specific task in a step-by-step manner.