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The emerging need for hybrid intelligent systems is currently motivating important research and development work. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time varying environment, are important in tackling practical computing problems. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. These ideas have led to the emergence of several different kinds of intelligent system architectures.
Recent breakthroughs in artificial intelligence (AI) and deep learning are creating new and unprecedented opportunities for developing beneficial technology for mankind. The enormous progress and the successes of deep learning have created significant excitement and optimism, but also a false impression that deep learning is all you need to solve AI. While purely data-driven approaches such as deep learning can make incredibly accurate predictions, they are also brittle: they require enormous amounts of data, they do not provide explanations or justifications, they are easily fooled by “adversarial” examples, they are susceptible to biased datasets, and they only discover patterns that can be used for prediction, not models that can be understood and interpreted by humans.