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Intelligence artificielle

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Artificial Intelligence[1] (AI) refers to the field of ordinateur[3] science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include reasoning, learning, planning, perception, and language understanding. AI draws from different fields including psychology, linguistics, philosophy, and neuroscience. The field is prominent in developing machine learning[2] models and natural language processing systems. It also plays a significant role in creating virtual assistants and affective computing systems. AI applications extend across various sectors including healthcare, industry, government, and education. Despite its benefits, AI also raises ethical and societal concerns, necessitating regulatory policies. AI continues to evolve with advanced techniques such as deep learning and generative AI, offering new possibilities in various industries.

Définitions des termes
1. Intelligence artificielle ( Artificial Intelligence )
1 Artificial Intelligence, commonly known as AI, is a field of computer science dedicated to creating intelligent machines that perform tasks typically requiring human intellect. These tasks include problem-solving, recognizing speech, understanding natural language, and making decisions. AI is categorised into two types: narrow AI, which is designed to perform a specific task, like voice recognition, and general AI, which can perform any intellectual tasks a human being can do. It's a continuously evolving technology that draws from various fields including computer science, mathematics, psychology, linguistics, and neuroscience. The core concepts of AI include reasoning, knowledge representation, planning, natural language processing, and perception. AI has wide-ranging applications across numerous sectors, from healthcare and gaming to military and creativity, and its ethical considerations and challenges are pivotal to its development and implementation.
2 Artificial Intelligence (AI) refers to the field of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include reasoning, learning, planning, perception, and language understanding. AI draws from different fields including psychology, linguistics, philosophy, and neuroscience. The field is prominent in developing machine learning models and natural language processing systems. It also plays a significant role in creating virtual assistants and affective computing systems. AI applications extend across various sectors including healthcare, industry, government, and education. Despite its benefits, AI also raises ethical and societal concerns, necessitating regulatory policies. AI continues to evolve with advanced techniques such as deep learning and generative AI, offering new possibilities in various industries.
2. machine learning. Machine learning, a term coined by Arthur Samuel in 1959, is a field of study that originated from the pursuit of artificial intelligence. It employs techniques that allow computers to improve their performance over time through experience. This learning process often mimics the human cognitive process. Machine learning applies to various areas such as natural language processing, computer vision, and speech recognition. It also finds use in practical sectors like agriculture, medicine, and business for predictive analytics. Theoretical frameworks such as the Probably Approximately Correct learning and concepts like data mining and mathematical optimization form the foundation of machine learning. Specialized techniques include supervised and unsupervised learning, reinforcement learning, and dimensionality reduction, among others.

Intelligence artificielle (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research à computer science that develops and studies methods and software which enable machines to perceive their environment and uses learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.

AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines (e.g., Recherche Google); recommendation systems (used by YouTube, Amazonet Netflix); interacting via human speech (e.g., Google Assistant, Siriet Alexa); autonomous vehicles (e.g., Waymo); generative et creative tools (e.g., ChatGPT et AI art); and superhuman play and analysis in strategy games (e.g., chess et Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."

Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence. Artificial intelligence was founded as an academic discipline in 1956. The field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter. Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques, and after 2017 with the transformer architecture. This led to the AI boom of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.

The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-makinget le integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, and education. This raises questions about the long-term effects, ethical implicationset risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

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 reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. General intelligence—the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals.

To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search et mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations researchet économie. AI also draws upon psychologie, linguistics, philosophy, neuroscience, and other fields.


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