Difference between conventional computing and intelligent computing
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The conventional computing functions logically with a set of rules and calculations while the neural computing can function via images, pictures, and concepts. Conventional computing is often unable to manage the variability of data obtained in the real world. On the other hand, neural computing, like our own brains, is well suited to situations that have no clear algorithmic solutions and are able to manage noisy imprecise data. This allows them to excel in those areas that conventional computing often finds difficult.
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Artificial intelligence is the intelligence exhibited by machines or software. It is also an academic field of study. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
Computational Intelligence:
Computational intelligence is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches, first principles modeling or explicit statistical modeling, are ineffective or infeasible. Many such real-life problems are not considered to be well-posed problems mathematically, but nature provides many counterexamples of biological systems exhibiting the required function, practically.
Differences
Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match. Techniques to achieve this divide roughly into two schools of thought: Conventional AI and Computational intelligence (CI).
Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI." These techniques, which include fuzzy logic, have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism. These two methodologies have also been labeled as neats vs. scruffies, with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.
Computational Intelligence:
Computational intelligence is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches, first principles modeling or explicit statistical modeling, are ineffective or infeasible. Many such real-life problems are not considered to be well-posed problems mathematically, but nature provides many counterexamples of biological systems exhibiting the required function, practically.
Differences
Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match. Techniques to achieve this divide roughly into two schools of thought: Conventional AI and Computational intelligence (CI).
Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI." These techniques, which include fuzzy logic, have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism. These two methodologies have also been labeled as neats vs. scruffies, with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.
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