Soft computing allows computers to work with imprecision and make educated guesses, similar to the human brain. It uses various disciplines to analyze problems and provide accurate answers. Soft computing has potential benefits in fields such as robotics, medicine, engineering, and physics.
Traditional electronic calculations tend to be black and white. When working in binary code, with sequences of zeros and ones, there is no possibility of anything other than simple yes or no answers. While this may be an adequate mode of computing for many tasks, soft computing takes a different approach. In short, soft computing allows the computer to take on a certain level of imprecision in its work. Some might equate it to artificial intelligence, as it is similar to the way the human brain works.
From a human perspective, soft computing introduces trade-offs in computer processing that are not present in hard computing. There are times when the answer to a question may be yes or no, but there is still not enough information to definitively calculate what the answer is. Traditional computers facing this situation will simply stop and wait until there is enough information to make an accurate conclusion. Soft computing is, in essence, the ability of a computer to provide an answer of maybe, or even to make an educated guess about what the answer might be until more information becomes available.
To use a mathematical example, it’s simple to say that the sum of two plus two equals four. It is also correct to say that the sum of two plus two is somewhere between three and five. Naturally, the goal is to provide the most accurate answer possible. While a computer might be tempted to ignore the second option, soft computing, when done correctly, will see this response as a potential option. While the computer will always opt for the most accurate answer available, it will consider making an estimate if not all numbers are known with certainty.
To provide its answers, or its evaluation of answers, the computer will use many different disciplines. Among the five best known are fuzzy systems, evolutionary computing, probabilistic reasoning, machine learning, and neural networks. By using many different computational methods to analyze a problem, the computer can eventually provide an accurate answer to a question that was initially answered inaccurately.
In effect, the computer gave a response that was not preprogrammed into it. From a computing perspective, and perhaps from a biological perspective, this could be considered artificial learning or intelligence. Some might argue that the path to the answer was pre-programmed, whether or not the answer was, so it didn’t constitute true intelligence. The question of whether this constitutes actual intelligence is a philosophical question, one that probably depends very much on one’s perspective.
The field of computing is generally enthusiastic about the possibility of soft computing and its potential benefits. It could revolutionize robotics, perhaps making prostheses that are more realistic, easier to use and move more naturally. Soft computing could also be used in many other fields, such as medicine, engineering and physics.
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