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AI is defined as computational capability to perform human-like intelligent tasks such as learning, reasoning, perception, and decision-making.
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, including learning, reasoning, problem-solving, perception, and decision-making. As a research field spanning engineering, mathematics, and computer science, AI develops and studies methods and software that help machines perceive their environment and use learning and intelligence to take actions that maximize the chances of achieving defined goals. The scope of AI research includes both broad application areas and specific research goals. High-profile applications include web search, chatbots and virtual assistants, autonomous vehicles, strategy-game play and analysis (e.g., chess and Go), and content generation such as images, audio, and video. Traditional AI research goals focus on capabilities like learning, reasoning, knowledge representation, planning, natural language processing, and perception, with support for robotics. To pursue these goals, researchers use techniques such as state-space search and mathematical optimization, formal logic, artificial neural networks, and statistical/probabilistic and operations-research approaches, drawing on disciplines including psychology, linguistics, philosophy, and neuroscience.
AI is defined as computational capability to perform human-like intelligent tasks such as learning, reasoning, perception, and decision-making.
The scope of AI research covers core goals (learning, reasoning, knowledge representation, planning, NLP, perception, robotics) and uses multiple techniques (search/optimization, logic, neural networks, and statistical methods).
AI is the capability of computational systems to perform tasks associated with human intelligence, such as learning, reasoning, perception, and decision-making.
AGI refers to AI that can complete nearly any cognitive task at least as well as a human.
Knowledge representation is the way AI encodes concepts and relationships so programs can answer questions and make deductions about real-world facts.
NLP enables programs to read, write, and communicate in human languages, including tasks like translation and question answering.
Machine learning studies programs that improve their performance on a task automatically, often using supervised, unsupervised, or reinforcement learning.
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