Computational intelligence (CI) is a soft computing branch of computer science that draws inspiration from nature, including evolutionary computing, fuzzy logic, and neural networks. CI allows robots to learn from experience, think, remember, perceive, and make decisions in the face of uncertainty. Neural networks can become experts in their fields, but require a lot of computing power and can perform unpredictably. Expert systems, which use predetermined sets of rules, should not be confused with neural networks.
Computational intelligence (CI) is a branch of computer science in which projects evolve from the bottom up, with order emerging from an initial lack of structure. This is similar to many processes observed in the natural world. Computational intelligence includes concepts such as evolutionary computing, where problems are solved using models of the evolutionary process and, when applied to machine learning, allows robots to learn from experience. Fuzzy logic, a system that resembles human decision making, can be used to solve problems where there is vagueness or uncertainty. Neural networks are systems based on human brain function and can be used to detect patterns and trends in complex data.
Unlike hard computing, where solutions are guaranteed and problems are limited under strict conditions, computational intelligence falls into the soft computing category, where positive outcomes do not always occur. Computational intelligence often draws inspiration from nature, for example in the field of evolutionary computing, where systems are created that evolve to solve complex problems. This can be applied to artificial or synthetic intelligence, resulting in robots that learn from experience and develop over time.
Fuzzy logic-based systems can be used in computational intelligence to simulate human ways of thinking. They could be combined with biology-inspired neural networks in the field of cognitive robotics, creating robots with the ability to think in a way that resembles human thought processes. In addition to thinking, these robots could also learn, remember, perceive and make decisions in the face of uncertainty, as humans do. This could allow bots to better understand human requests, allowing them to detect the meaning behind the words used. This could be essential for a machine that does household tasks.
Neural networks are generally considered part of computational intelligence. Like the human brain, they are made up of many individual interconnected parts, similar to nerves. They work together to solve problems, learning as they go, because the connections between things are tunable, like the connections between nerves.
Once neural networks have learned to analyze data, they can actually become experts in their fields and can be used to predict outcomes in different scenarios. A disadvantage of this type of computational intelligence is that it requires a lot of computing power and can perform unpredictably. Neural networks should not be confused with expert systems, which use predetermined sets of rules to make decisions and don’t adjust them to fit the data.
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