Conventional Vs Intelligent Computing 1. IntroductionThis report provides us a clear understanding of the computing types, a way to identify whether a solution is intelligent or not with examples.2. Computing typesSmart solution to a given problem is found by analyzing and making decision based on available data in a predictive manner, due to which it is said to be effective and efficient. 2.1 Conventional computingConventional computing is a method which guarantees a solution to the given problem, the solution is often told by the programmer, as how to solve the problem and the result produced is consistent and reliable. It functions with rules and calculations. It cannot explain reasoning’s; it does what it is programmed to do and no more. It has precise knowledge.2.2 Intelligent computingIntelligent computing is a method which doesn’t guarantee a solution to the given problem, and the result produced may not be consistent or reliable. It makes knowledge based decisions by using reasoning’s and deducing over the knowledge to perform pattern matching. It functions with concepts. It has imprecise knowledge.2.3 Intelligent v/s Conventional computingConventional and intelligent both operate on different kinds of problems, hence we cannot justify conventional computing is better than intelligent computing or vice versa. The differences between both the computing is that intelligent computing solves a range of problems in a given domain whereas conventional can solve only one problem at a time in a given domain, computational isn’t effective with variating, noisy and imprecise data. Intelligent computing is learning and conventional stays same all the time. 3. Examples and findingsA real life example can be thought of a physician, who when treats a patient, he/she use their past experiences, like the cause of the symptom, the treatment and medicines used to treat the same symptoms. This could be the same as what intelligent computing works as it also learns from its experiences, whereas computational computing is used in normal day to day computing like calculator, which uses rules and predefined instructions to solve the problems.4. ConclusionsThe conclusion deduced after research was that the combination of conventional computing and intelligent computing, i.e. hybrid computing will result into a better and more efficient and reliable solution, as it will have the techniques needed to solve complex problems, which will help us achieving goals of higher accuracy.5. Intelligent SolutionA solution is said to be intelligent if it is efficient, accurate and attains the requirement. To attain an intelligent solution the problem must be identified, the set up goal must be achieved and evaluation must be made, that how efficiently the solution overcomes and solves the given problem.6. Learning agent versus Utility agentUtility agent maximize their usefulness, profits and benefits and learning agents learn from their decisions, they can deal with any environments, handle complex data and improve their own performances. Example of intelligent agent is a web browser designed by IBM on intelligent agents, it helps you search the web and customizes the view according to the use knowledge it learned and provides customized views, example of utility agent can be thought of as mars lander which if faces a rock in its way it computes to find the best output path. Learning agent and utility agent both provide solutions which are better than each other depending on the situation, where they are used as stated in the above example.