Artificial Intelligence is an approach to make a computer, a robot, or a product to think how smart human think. AI is a study of how human brain think, learn, decide and work, when it tries to solve problems. And finally this study outputs intelligent software systems.The aim of AI is to improve computer functions which are related to human knowledge, for example, reasoning, learning, and problem-solving.
The intelligence is intangible. It is composed of
- Reasoning: The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. Or we can say, "Reasoning is a way to infer facts from existing data." It is a general process of thinking rationally, to find valid conclusions. Reasoning is essential for AI so that the machine can also think rationally as a human brain, and can perform like a human. To read more about types of reasoning visit link
- Learning: Learning is the process of converting experience into expertise or knowledge. Learning can be broadly classified into three categories, as mentioned below, based on the nature of the learning data and interaction between the learner and the environment.
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Supervised learning algorithm
- Unsupervised learning algorithm
- Semi-supervised learning algorithm
- Reinforcement learning algorithm
- Problem Solving: In computer science, problem-solving refers to artificial intelligence techniques, including various techniques such as forming efficient algorithms, heuristics, and performing root cause analysis to find desirable solutions. In artificial intelligence, problems can be solved by using searching algorithms, evolutionary computations, knowledge representations, etc. For example Various searching techniques can be used to solve a problem. To read more about types of problem solving visit link.
- Perception: Perception is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception can be seen as a special type of categorization (or classification, pattern recognition) where the inputs are sensory data, and the outputs are categorical judgments and conceptual relations. The difficulty of the task comes from the need of multiple levels of abstraction, where the relations among data items are many-to- many, uncertain, and changing over time. Accurately speaking, we never "see things as they are", and perception process of an intelligent system is often (and should be) influenced by internal and external factors beside the signals themselves. Furthermore, perception is not a pure passive process driven by the input. In AI, the study on perception is mostly focused on the reproduction of human perception, especially on the perception of aural and visual signals. However, this is not necessarily the case since the perception mechanism of a computer system does not have to be identical to that of a human being.
- Linguistic Intelligence: Ability to speak, recognize and use mechanisms of phonetics (speech sounds), syntax (grammar), and semantic (meaning). Linguistic intelligence is the most widely shared human capacity and is obvious in poets, novelists, journalists and effective public speakers. Young adults with such intelligence have fun writing, reading, telling stories or making crossword puzzles.
The objectives of AI research are reasoning, knowledge representation, planning, learning, natural language processing, realization, and ability to move and manipulate objects. This includes approaches like statistical methods, computational intelligence, and traditional coding AI. During the AI research related to search and mathematical optimization, artificial neural networks and methods based on statistics, probability, and economics, we use many tools. Computer science attracts AI in the field of science, mathematics, psychology, linguistics, philosophy and so on.
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