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Learn about artificial intelligence (AI), the field of computer science that develops and studies methods and software that enable machines to perceive and act in their environment. Explore the major goals, techniques, applications, and challenges of AI, as well as its origins and development over time.
Machine learning (ML) is a field of artificial intelligence that develops and studies algorithms that can learn from data and generalize to unseen data. ML has many applications in various fields, such as natural language processing, computer vision, and medicine, and is related to data mining, statistics, and neural networks.
AGI stands for artificial general intelligence, a type of AI that matches or surpasses human capabilities across a wide range of cognitive tasks. Learn about the characteristics, tests, and challenges of AGI, as well as the related concepts of strong AI, weak AI, and AI-complete problems.
Deep learning is a subset of machine learning methods based on neural networks with representation learning. It involves stacking artificial neurons into layers and training them to process data, and can be applied to various fields such as computer vision, speech recognition, natural language processing, and more.
A neural network is a model inspired by the structure and function of biological neural networks in animal brains. It consists of connected units or nodes called artificial neurons, which can learn from experience and perform various tasks such as predictive modeling and problem solving.
Learn about the history, methods, and applications of symbolic AI, a paradigm of AI research based on high-level symbolic representations and logic. Explore the challenges, successes, and limitations of symbolic AI and its relation to other approaches.