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Machine learning is aimed at developing a computer that learns like humans. State-of-the-art machine learning technologies, which are based on statistical processing of big data by powerful computers, are highly successful in various real-world problems such as speech recognition, image understanding, and natural language translation. However, humans do not require big data or an enormous computational power to acquire intelligence and thus there is still a significant gap between machine learning and human learning. The goal of my research is to construct neuro-inspired machine learning paradigms that can fill the gap between artificial intelligence and human intelligence and establish a foundation of next-generation intelligent data processing technologies.