The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".
The Church-Turing thesis, along with concurrent discoveries in neurobiology, information theory and cybernetics, led researchers to consider the possibility of building an electronic brain. This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence. Silver didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligenceĪrtificial beings with intelligence appeared as storytelling devices in antiquity, Īnd have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.
To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques-including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics. General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. The various sub-fields of AI research are centered around particular goals and the use of particular tools.
In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. Īrtificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an " AI winter"), followed by new approaches, success and renewed funding. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Īs machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. ĪI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem-solving", however, this definition is rejected by major AI researchers. Leading AI textbooks define the field as the study of " intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Artificial intelligence ( AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.
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