Conventional and to perform more complex operations.Intelligent computingUnlike conventional

Conventional computers follow a set of instructions and accordingly process data. Also referred to as Von Neumann computers. Major components include memory,processing and bandwidth.It is a general purpose machine that we can make to do anything we like as long as it is given a set of instructions called programs. They store numbers in memory and perform operations on numbers stored in memory. Example adding and subtracting. They can perform complex tasks by executing series of instructions called algorithms.Storage and processing are achieved using switches called transistors.Similarly like switches they can save number 1 as on and if off they save 0 ,these series of numbers can be used to save digits ,alphabets, numbers, symbols and codes based on binary.These computers calculate using circuits that are called Logic gates created by connecting together many such logic gates. Logic gates compare patterns or bits stored in temporary memory that are called registers and then convert them into new patterns or bits. Limitation to conventional computing is that it depends on these conventional transistors , ie. the more memory we require more is the number of transistors that we need to process 0’s and 1’s and to perform more complex operations.Intelligent computingUnlike conventional computing that uses set of instructions or algorithms to perform operations intelligent computing can also compute images and concepts and process them as information.In many cases various inputs cannot be converted to bits for computer to process and Intelligent computing tackles the problems by analyzing them and making decisions.It focuses on attempting human like intelligence through symbols or speech or the ability to make decisions based on certain situations. By analysis of these symbols,images and using various methodologies for making a decision.Its main principles include :Fuzzy Logic : Intelligent computing’s main principle that is used for approximate reasoning which is necessary in a concept to learn from previous mistakes.Neural Networks : Approximately mimicking brain like neurons function to process and learn from data.Evolutionary Computation : Concept of evolutionary algorithms also known as problem solvers. Main applications includes optimization.Learning Theory : Process of using emotional, cognitive ,experiences and environmental effects to acquire and enhance knowledge and skills.Helps make predictions using previous experiences.Probabilistic Methods : To evaluate outcomes of an intelligent system. To see possible solutions to problems using previous knowledge.When is a solution Intelligent?When it is the outcome of various factors such as its ability to interact with real world problems, Reason and Plan, Learning and Adaptation to the given problems and situation. And if the solution solves the problem and adapts to the changing environment of the problem and gives a better solution to the new problem.