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Artificial intelligence (AI)

is the intelligence of machines or software, as opposed to the intelligence of human beings or animals.

AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTubeAmazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go).[1]

Artificial intelligence was founded as an academic discipline in 1956.[2] The field went through multiple cycles of optimism[3][4] followed by disappointment and loss of funding.[5][6] After 2012, deep learning surpassed all previous AI techniques,[7] leading to a vast increase in funding and interest.

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoningknowledge representationplanninglearningnatural language processingperception, and support for robotics.[a] General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals.[8] 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 statisticsprobability, and economics. AI also draws upon psychologylinguisticsphilosophyneuroscience and many other fields.[9]

Goals

The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[a]

Reasoning, problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[10] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[11]

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger.[12] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[13] Accurate and efficient reasoning is an unsolved problem.

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