Soft Computing

Updated: Dec 21, 2021

Soft computing or computational intelligence is a field of artificial intelligence-based computing techniques that allow for and are dedicated to approximated and natural calculations. This is important because it enables the solutions to problems that require partial truths, uncertainty, imprecision, and approximation, things that aren’t achievable with traditional computing models and algorithms. Fuzzy logic, machine learning, neural networks, probabilistic reasoning, and evolutionary computation are all areas of soft computing, designed to solve complex and real-world problems.

To begin, soft computing was first introduced and coined by mathematician Lotfi A. Zadeh in the early 1990s and is a way we have continued the path to having machines replicate the human mind as closely as possible. Soft computing techniques first took inspiration from the human brain’s ability to solve real-world problems through approximation and estimation. Soft computing differs from hard computing, which uses existing mathematical algorithms to solve problems. Hard computing provides precise solutions that are not always appropriate for every situation. Solutions produced for complex problems using soft computing are purposely imprecise or “fuzzy.”

To begin, the first technique to mention is fuzzy logic. Fuzzy logic is a technique that strays from conventional boolean logic and allows multiple possible truth values to be created in the same variable. It creates a way of problem-solving with imprecise spectrums of data that opens the possibility for numerous accurate conclusions. In fuzzy logic, all true statements are partial and are assigned a value of probability between 0 and 1, such as 0.3 or 0.9. This allows for the mimicking of real-life circumstances where one hundred percent truth or falsehood is rare. Another example of soft computing is neural networks. An Artificial Neural Network (ANN) emulates networks of neurons that make up the human brain. The use of the word neural refers to the human brain in that the layers of nodes that neural networks are composed of mimic the way neurons signal to each other and deliver information. This way, a machine or computer can learn like the human mind. Lastly, genetic algorithms (GA) and evolutionary computation are a subset of computing that is based upon nature and natural selection. The algorithms imitate the processes of natural selection, where the strongest individuals of a species are selected for reproduction.

Soft computing is an essential field for the future and progression of AI. As mentioned, these techniques and models are needed when hard computing fails to solve real-world problems. Soft computing can map the human mind, bringing us even closer to the goals of improving AI. These techniques are essential in many applications, including wireless communication, consumer appliances that are bringing Artificial Intelligence to homes, robotics, transportation, healthcare, and data mining. Soft computing is a remarkable field, and the areas of application will only increase with time.

Written by Mariah

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